Potential Applications of Video Technology for

Traffic Management and Safety in Alabama

By

Dr. Andrew Graettinger

Dr. John McFadden

Meghavardhan Reddy Govindu

Ramgiridhar Reddy Kilim

Department of Civil and Environmental Engineering

The University of Alabama

Tuscaloosa, Alabama

Prepared by

UTCA

[University Transportation Center for Alabama]{.underline}

The University of Alabama, The University of Alabama at Birmingham, and

The University of Alabama in Huntsville

UTCA Report Number 00463

November 25, 2002

Technical Report Documentation Page

+----------------------+---+------------+-----+---+-----------+--------+
| **1. Report No**     | * |            |     | * |           |        |
|                      | * |            |     | * |           |        |
| FHWA/CA/OR-          | 2 |            |     | 3 |           |        |
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+----------------------+---+------------+-----+---+-----------+--------+
| **4. Title and       |   |            | *   |   |           |        |
| Subtitle**           |   |            | *5. |   |           |        |
|                      |   |            | Rep |   |           |        |
| Potential            |   |            | ort |   |           |        |
| Applications of      |   |            | Dat |   |           |        |
| Video Technology for |   |            | e** |   |           |        |
| Traffic Management   |   |            |     |   |           |        |
| and Safety in        |   |            | No  |   |           |        |
| Alabama              |   |            | vem |   |           |        |
|                      |   |            | ber |   |           |        |
|                      |   |            | 25, |   |           |        |
|                      |   |            | 2   |   |           |        |
|                      |   |            | 002 |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
|                      |   |            | 6   |   |           |        |
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|                      |   |            | Cod |   |           |        |
|                      |   |            | e** |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
| **7. Authors**       |   |            | *   |   |           |        |
|                      |   |            | *8. |   |           |        |
| Dr. Andrew J.        |   |            | P   |   |           |        |
| Graettinger, Dr.     |   |            | erf |   |           |        |
| John McFadden,       |   |            | orm |   |           |        |
|                      |   |            | ing |   |           |        |
| Meghavardhan Reddy   |   |            | Org |   |           |        |
| Govindu, Ramgiridhar |   |            | ani |   |           |        |
| Reddy Kilim          |   |            | zat |   |           |        |
|                      |   |            | ion |   |           |        |
|                      |   |            | Rep |   |           |        |
|                      |   |            | ort |   |           |        |
|                      |   |            | No  |   |           |        |
|                      |   |            | .** |   |           |        |
|                      |   |            |     |   |           |        |
|                      |   |            | U   |   |           |        |
|                      |   |            | TCA |   |           |        |
|                      |   |            | Rep |   |           |        |
|                      |   |            | ort |   |           |        |
|                      |   |            | 00  |   |           |        |
|                      |   |            | 463 |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
| **9. Performing      |   |            | **  |   |           |        |
| Organization Name    |   |            | 10. |   |           |        |
| and Address**        |   |            | W   |   |           |        |
|                      |   |            | ork |   |           |        |
| University           |   |            | U   |   |           |        |
| Transportation       |   |            | nit |   |           |        |
| Center for Alabama   |   |            | No  |   |           |        |
|                      |   |            | .** |   |           |        |
| P O Box 870205       |   |            |     |   |           |        |
|                      |   |            |     |   |           |        |
| University of        |   |            |     |   |           |        |
| Alabama              |   |            |     |   |           |        |
|                      |   |            |     |   |           |        |
| Tuscaloosa, AL       |   |            |     |   |           |        |
| 35487-0205           |   |            |     |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
|                      |   |            | **  |   |           |        |
|                      |   |            | 11. |   |           |        |
|                      |   |            | Co  |   |           |        |
|                      |   |            | ntr |   |           |        |
|                      |   |            | act |   |           |        |
|                      |   |            | or  |   |           |        |
|                      |   |            | Gr  |   |           |        |
|                      |   |            | ant |   |           |        |
|                      |   |            | No  |   |           |        |
|                      |   |            | .** |   |           |        |
|                      |   |            |     |   |           |        |
|                      |   |            | A   |   |           |        |
|                      |   |            | lab |   |           |        |
|                      |   |            | ama |   |           |        |
|                      |   |            | D   |   |           |        |
|                      |   |            | epa |   |           |        |
|                      |   |            | rtm |   |           |        |
|                      |   |            | ent |   |           |        |
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|                      |   |            | ans |   |           |        |
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|                      |   |            | 93  |   |           |        |
|                      |   |            | 0-4 |   |           |        |
|                      |   |            | 76U |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
| **12. Sponsoring     |   |            | **  |   |           |        |
| Agency Name and      |   |            | 13. |   |           |        |
| Address**            |   |            | T   |   |           |        |
|                      |   |            | ype |   |           |        |
| Alabama Department   |   |            | of  |   |           |        |
| of Transportation    |   |            | Rep |   |           |        |
|                      |   |            | ort |   |           |        |
| 1409 Coliseum        |   |            | and |   |           |        |
| Boulevard            |   |            | Per |   |           |        |
|                      |   |            | iod |   |           |        |
| Montgomery, AL 36130 |   |            | Cov |   |           |        |
|                      |   |            | ere |   |           |        |
|                      |   |            | d** |   |           |        |
|                      |   |            |     |   |           |        |
|                      |   |            | Fi  |   |           |        |
|                      |   |            | nal |   |           |        |
|                      |   |            | R   |   |           |        |
|                      |   |            | epo |   |           |        |
|                      |   |            | rt: |   |           |        |
|                      |   |            | 3-5 |   |           |        |
|                      |   |            | -01 |   |           |        |
|                      |   |            | to  |   |           |        |
|                      |   |            | 6   |   |           |        |
|                      |   |            | -30 |   |           |        |
|                      |   |            | -02 |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
|                      |   |            | **  |   |           |        |
|                      |   |            | 14. |   |           |        |
|                      |   |            | S   |   |           |        |
|                      |   |            | pon |   |           |        |
|                      |   |            | sor |   |           |        |
|                      |   |            | ing |   |           |        |
|                      |   |            | Age |   |           |        |
|                      |   |            | ncy |   |           |        |
|                      |   |            | Cod |   |           |        |
|                      |   |            | e** |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
| **15. Supplementary  |   |            |     |   |           |        |
| Notes**              |   |            |     |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
| **16. Abstract**     |   |            |     |   |           |        |
|                      |   |            |     |   |           |        |
| Video technology     |   |            |     |   |           |        |
| applications for     |   |            |     |   |           |        |
| traffic management   |   |            |     |   |           |        |
| and safety are being |   |            |     |   |           |        |
| implemented by state |   |            |     |   |           |        |
| and local government |   |            |     |   |           |        |
| agencies in Alabama. |   |            |     |   |           |        |
| This technology      |   |            |     |   |           |        |
| offers both tangible |   |            |     |   |           |        |
| and intangible       |   |            |     |   |           |        |
| benefits. Although   |   |            |     |   |           |        |
| video technology     |   |            |     |   |           |        |
| provides many        |   |            |     |   |           |        |
| benefits, it         |   |            |     |   |           |        |
| requires substantial |   |            |     |   |           |        |
| up-front costs for   |   |            |     |   |           |        |
| purchasing and       |   |            |     |   |           |        |
| installing the       |   |            |     |   |           |        |
| equipment and        |   |            |     |   |           |        |
| training staff. Due  |   |            |     |   |           |        |
| to the complexity of |   |            |     |   |           |        |
| video systems and    |   |            |     |   |           |        |
| the rapid pace of    |   |            |     |   |           |        |
| change related to    |   |            |     |   |           |        |
| this technology,     |   |            |     |   |           |        |
| agencies may         |   |            |     |   |           |        |
| overlook some        |   |            |     |   |           |        |
| applications while   |   |            |     |   |           |        |
| using resources to   |   |            |     |   |           |        |
| implement less       |   |            |     |   |           |        |
| valuable             |   |            |     |   |           |        |
| applications.        |   |            |     |   |           |        |
|                      |   |            |     |   |           |        |
| This study compiled  |   |            |     |   |           |        |
| a detailed review of |   |            |     |   |           |        |
| current capabilities |   |            |     |   |           |        |
| of video technology. |   |            |     |   |           |        |
| This research        |   |            |     |   |           |        |
| applied and tested   |   |            |     |   |           |        |
| new, low cost video  |   |            |     |   |           |        |
| applications for     |   |            |     |   |           |        |
| traffic management   |   |            |     |   |           |        |
| and safety in        |   |            |     |   |           |        |
| Alabama on pilot     |   |            |     |   |           |        |
| scale projects to    |   |            |     |   |           |        |
| help agencies employ |   |            |     |   |           |        |
| video technology to  |   |            |     |   |           |        |
| its fullest          |   |            |     |   |           |        |
| potential. Several   |   |            |     |   |           |        |
| video systems, like  |   |            |     |   |           |        |
| digital video        |   |            |     |   |           |        |
| cameras,             |   |            |     |   |           |        |
| conventional video   |   |            |     |   |           |        |
| recorders,           |   |            |     |   |           |        |
| surveillance         |   |            |     |   |           |        |
| cameras, and         |   |            |     |   |           |        |
| Autoscope were       |   |            |     |   |           |        |
| implemented and      |   |            |     |   |           |        |
| tested at various    |   |            |     |   |           |        |
| locations within the |   |            |     |   |           |        |
| state. This research |   |            |     |   |           |        |
| project demonstrated |   |            |     |   |           |        |
| applications of low  |   |            |     |   |           |        |
| cost video           |   |            |     |   |           |        |
| technology that can  |   |            |     |   |           |        |
| be implemented to    |   |            |     |   |           |        |
| improve safety and   |   |            |     |   |           |        |
| reduce crashes and   |   |            |     |   |           |        |
| violations.          |   |            |     |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
| **17. Key Words**    |   |            |     |   | **18.     |        |
|                      |   |            |     |   | Dis       |        |
| Intelligent          |   |            |     |   | tribution |        |
| Transportation       |   |            |     |   | St        |        |
| Systems, video       |   |            |     |   | atement** |        |
| technology, traffic  |   |            |     |   |           |        |
| management systems,  |   |            |     |   |           |        |
| crash, violations,   |   |            |     |   |           |        |
| and Autoscope        |   |            |     |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+
| **19. Security       |   | 20         |     |   | 21. **No  | 22\.   |
| Classification (of   |   | . Security |     |   |     of    | Price  |
| this report)**       |   |     Class  |     |   |           |        |
|                      |   |     (of    |     |   |   Pages** |        |
| Unclassified         |   |     this   |     |   |           |        |
|                      |   |     page)  |     |   | > 85      |        |
|                      |   |            |     |   |           |        |
|                      |   | > Un       |     |   |           |        |
|                      |   | classified |     |   |           |        |
+----------------------+---+------------+-----+---+-----------+--------+

# Contents 

[Contents iii](#contents)[Tables vi](#tables)[Figures
vi](#figures)i[Executive Summary vi](#executive-summary)ii

1.  

[Introduction 1](#introduction)

2.  

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1.  
2.  
3.  
4.  

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2.  
3.  

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5.  

[CARE Analysis For Identification Of Video Surveillance Locations
3](#care-analysis-for-identification-of-video-surveillance-locations)[Introduction
3](#introduction-1)[CARE^®^ Overview 3](#care-overview)[CARE^®^ Analysis
For Video Location Identification
4](#care-analysis-for-video-location-identification)[General Crash
Trends In Alabama 5](#general-crash-trends-in-alabama)[Red-Light Running
Crashes And Trends 7](#red-light-running-crashes-and-trends)[Speed
Related Crashes And Trends
8](#speed-related-crashes-and-trends)[Railroad (RR) Related Crashes And
Trends 9](#railroad-rr-related-crashes-and-trends)[Video Technology
Locations Based On CARE^®^ Data
11](#video-technology-locations-based-on-care-data)

3.  

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9.  

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11. 
12. 

[Compositional Traffic Counting Using Video Technology
12](#compositional-traffic-counting-using-video-technology)[Introduction
12](#introduction-2)[Background 12](#background)[Video Cameras And Image
Processor 14](#video-cameras-and-image-processor)[Experimental Design
15](#experimental-design)[Equipment And Cost
16](#equipment-and-cost)[Site Selection 16](#site-selection)[Period Of
I-65 Data Collection 17](#period-of-i-65-data-collection)[Procedure For
Data Collection By Autoscope
17](#procedure-for-data-collection-by-autoscope)[Autoscope Length
Classifications 18](#autoscope-length-classifications)[Vehicle
Classification Disaggregation Model (VCD Model)
20](#vehicle-classification-disaggregation-model-vcd-model)[VCD Model
Development 21](#vcd-model-development)[Training And Validating The
Model Using Only FHWA Data
23](#training-and-validating-the-model-using-only-fhwa-data-for-this-purpose-instead-of-using-autoscope-data-the-values-of-x1-and-x2-were-obtained-by-summing-the-fhwa-classes-one-to-three-and-fhwa-classes-four-to-14.-this-was-performed-to-comply-with-the-underlying-assumption-of-the-cumulative-relation.-the-eight-step-procedure-explained-below-was-followed-to-train-and-validate-the-vcd-model.)[Training
And Verifying The Model Using Autoscope Data
25](#training-and-verifying-the-model-using-autoscope-data-after-training-the-vcd-model-developed-from-fhwa-data-the-aggregated-x1-and-x2-values-were-replaced-with-actual-autoscope-x-values.-the-purpose-of-verification-was-to-evaluate-the-statistical-properties-of-the-generated-data-and-to-compare-those-properties-with-similar-properties-of-the-input-data.-to-verify-and-quantify-the-accuracy-of-the-vcd-model-three-datasets-were-randomly-selected-for-testing-purposes-which-were-excluded-from-the-28-datasets.-the-model-was-first-trained-with-25-datasets.-table-3-8-shows-the-compiled-25-sets-of-both-fhwa-and-autoscope-data.-the-calculated-mean-number-of-cars-and-the-standard-deviation-for-each-class-designated-by-each-column-is-shown-in-the-last-two-rows.-the-eight-step-procedure-discussed-above-was-repeated-and-the-vcd-model-generated-a-new-series-of-25-datasets.)[Results
28](#results)[Conclusions And Recommendations
30](#conclusions-and-recommendations)

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17. 
18. 

[15^th^ Street Corridor Modeling
32](#th-street-corridor-modeling)[Introduction
32](#introduction-3)[Background 32](#background-1)[Methodology
33](#methodology)[Introduction 33](#introduction-4)[Video Data
Collection 34](#video-data-collection)[ARENA Input Parameters
35](#arena-input-parameters-various-traffic-parameters-were-measured-or-calculated-based-on-the-input-requirements-for-the-simulation-model-and-the-operational-analysis-calculations.-the-following-parameters-were-identified-as-input-for-the-arena-simulation.)[ITE
Method Input Parameters
35](#ite-method-input-parameters-input-data-for-the-ite-method-which-involves-the-calculation-of-delay-based-on-the-number-of-stopped-vehicles-and-the-volume-in-a-given-time-period-are-as-follows-traffic-engineering-handbook-1994)[ARENA
Model Development 35](#arena-model-development)[Operational Analysis
36](#operational-analysis)[Results And Discussion
38](#results-and-discussion)[ARENA Simulation Model Results
38](#arena-simulation-model-results)[Conclusions 42](#conclusions)

5.  

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25. 
26. 

[Railroad-Grade Crossing Automated Video Enforcement Analysis
43](#railroad-grade-crossing-automated-video-enforcement-analysis)[Introduction
43](#introduction-5)[Study Approach 43](#study-approach)[Background
44](#background-2)[Literature Review 45](#literature-review)[Definition
Of Railroad-Grade Crossing Violation
45](#definition-of-railroad-grade-crossing-violation)[Railroad-Grade
Crossing Accident History And Safety Measures
45](#railroad-grade-crossing-accident-history-and-safety-measures)[Video
Data Collection 47](#video-data-collection-1)[Site Selection And
Description 47](#site-selection-and-description)[Data Collection
49](#data-collection)[Analysis And Results
49](#analysis-and-results)[Data Reduction 49](#data-reduction)[Violation
Data And Observations 50](#violation-data-and-observations)[Violations
And TPUTs Vs. Train Timings
51](#violations-and-tputs-vs.-train-timings)[Discussion
53](#discussion)[Conclusions 53](#conclusions-1)[Recommendations For
Future Study 54](#recommendations-for-future-study)

6.  

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27. 
28. 
29. 

[Variable Message Signs For Violation Notification
55](#variable-message-signs-for-violation-notification)[Introduction
55](#introduction-6)[Methodology 55](#methodology-1)[Conclusions
60](#conclusions-2)

7.  

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31. 
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39. 

[Mobile Video Units For Intersection Analysis
61](#mobile-video-units-for-intersection-analysis)[Introduction
61](#introduction-7)[Background 61](#background-3)[Conventional Video
Camera Recorder 61](#conventional-video-camera-recorder)[Digital Video
Camera Recorder (Camcorder)
62](#digital-video-camera-recorder-camcorder)[Comparison Of Conventional
And Digital Video Camera Recorders
63](#comparison-of-conventional-and-digital-video-camera-recorders)[I^3^
Cam Van 63](#i3-cam-van)[Conclusions 64](#conclusions-3)[Video For
Emergency Response Management
65](#video-for-emergency-response-management)[Introduction
65](#introduction-8)[Background 65](#background-4)[Video For Emergency
Response Management -- National Case Studies
66](#video-for-emergency-response-management-national-case-studies)[Multi-Jurisdictional
Live-Aerial Video Surveillance System (1991)
66](#multi-jurisdictional-live-aerial-video-surveillance-system-1991)[Houston
TranStar (1993) 66](#houston-transtar-1993)[NAVIGATOR, Atlanta, Georgia
(1993) 66](#navigator-atlanta-georgia-1993)[TransGuide, San Antonio,
Texas (1993) 67](#transguide-san-antonio-texas-1993)[AZtech, Phoenix,
Arizona (1996) 68](#aztech-phoenix-arizona-1996)[Smart Trek, Seattle,
Washington (1996) 68](#smart-trek-seattle-washington-1996)[Commuterlink,
Salt Lake City, Utah (2000)
68](#commuterlink-salt-lake-city-utah-2000)[Summary Of Existing Systems
69](#summary-of-existing-systems)[Video For Emergency Response In
Tuscaloosa 69](#video-for-emergency-response-in-tuscaloosa)[Autoscope
For Emergency Response
70](#autoscope-for-emergency-response)[Surveillance Cameras For
Emergency Response
71](#surveillance-cameras-for-emergency-response)[Conclusions
71](#conclusions-4)

9.  

[Conclusions And Recommendations 73](#conclusions-and-recommendations-1)

10. 

```{=html}
<!-- -->
```
40. 

[References 75](#references)[Web Resources 76](#web-resources)

41. [Appendix 77](#appendix)

# Tables

No. Page

[2-1 Summary of 1994-2000 Alabama accident data
5](#__RefHeading___Toc36976511)[2-2 Annual Alabama red-light running
crash data 6](#__RefHeading___Toc36976512)[2-3 Annual Alabama
speed-related crash data 6](#__RefHeading___Toc36976513)[2-4 Annual
Alabama railroad grade crossing crash data
6](#__RefHeading___Toc36976514)[2-5 Summary of 1994-2000 Tuscaloosa
accident data 7](#__RefHeading___Toc36976515)[2-6 Selected county
red-light running crash data 7](#__RefHeading___Toc36976516)[2-7
Selected city 1994-2000 RLR related crashes
7](#__RefHeading___Toc36976517)[2-8 Tuscaloosa red-light running crash
data 8](#__RefHeading___Toc36976518)[2-9 Selected county speed-related
crash data 8](#__RefHeading___Toc36976519)[2-10 Selected city 1994-2000
speed-related crashes 9](#__RefHeading___Toc36976520)[2-11 Tuscaloosa
speed-related crash data 9](#__RefHeading___Toc36976521)[2-12 Selected
county railroad grade crossing crash data
10](#__RefHeading___Toc36976522)[2-13 Selected city 1994-2000 railroad
related crashes 10](#__RefHeading___Toc36976523)[2-14 Tuscaloosa
railroad grade crossings accident data 10](#__RefHeading___Toc36976524)

[3-1 Vehicle classification data by roadway functional class as reported
to HPMS 13](#__RefHeading___Toc37055678)[3-2 Vehicle classification
based on length given by Traffic Monitoring Guide
15](#__RefHeading___Toc37055679)[3-3 Grouped FHWA and Autoscope classes
used in VCD model development 18](#__RefHeading___Toc37055680)[3-4
Comparison between Autoscope and FHWA data
19](#__RefHeading___Toc37055681)[3-5 Compilation of FHWA data and
Autoscope data (28 datasets) 22](#__RefHeading___Toc37055682)[3-6 Output
of VCD model (28 datasets) 24](#__RefHeading___Toc37055683)[3-7 Summary
of differences between FHWA data and Autoscope data (28 datasets)
25](#__RefHeading___Toc37055684)[3-8 Compilation of FHWA data and
Autoscope data (25 datasets) 26](#__RefHeading___Toc37055685)[3-9 Output
of VCD model (25 datasets) 27](#__RefHeading___Toc37055686)[3-10 Summary
of differences between FHWA data and Autoscope data (25 datasets)
28](#__RefHeading___Toc37055687)[3-11 Testing dataset 1, 95% and 99%
confidence interval for the VCD model output
29](#__RefHeading___Toc37055688)[3-12 Testing dataset 2, 95% and 99%
confidence interval for the VCD model output
29](#__RefHeading___Toc37055689)[3-13 Testing dataset 3, 95% and 99%
confidence interval for the VCD model output
30](#__RefHeading___Toc37055690)

[4-1 6th Avenue East and 15th Street AM peak East approach volume data
39](#__RefHeading___Toc37053850)[4-2 6th Avenue East and 15th Street AM
peak volume and delay comparison 39](#__RefHeading___Toc37053851)[4-3
LOS Table: A comparative study 41](#__RefHeading___Toc37053852)

[5-1 Highway-railroad grade crossing fatalities and rankings: 2000-2001
44](#__RefHeading___Toc37053957)[5-2 National highway-railroad grade
crossing collisions and casualties: 1981-2001
46](#__RefHeading___Toc37053958)[5-3 Alabama highway-railroad grade
crossing collisions and fatalities: 1998-2001
46](#__RefHeading___Toc37053959)[5-4 Summary of violations and TPUTs for
sites 1-3 50](#__RefHeading___Toc37053960)[5-5 Summary of number of
trains and gate activations 51](#__RefHeading___Toc37053961)

# Figures

No. Page

[3-1 Autoscope Solo Pro camera 14](#__RefHeading___Toc37055938)[3-2
Autoscope field setup 16](#__RefHeading___Toc37055939)[3-3 Location of
Autoscope at milepost 210 on I-65 17](#__RefHeading___Toc37055940)[3-4
Flowchart representing an overview of VCD model
21](#__RefHeading___Toc37055941)

[4-1 Schematic representation of the study approach
33](#__RefHeading___Toc37055952)[4-2 Map of the 15th Street corridor
34](#__RefHeading___Toc37055953)[4-3 Basic intersection process
36](#__RefHeading___Toc37055954)[4-4 ARENA simulation model for a
signalized intersection 37](#__RefHeading___Toc37055955)[4-5 6th Avenue
East AM peak model validation: East approach -- Left lane
40](#__RefHeading___Toc37055956)

[5-1 Tuscaloosa sites and camera locations
48](#__RefHeading___Toc37055974)[5-2 Birmingham site and camera location
48](#__RefHeading___Toc37055975)[5-3 Sneak through violations before
train 52](#__RefHeading___Toc37055976)[5-4 Sneak through violations
after train 52](#__RefHeading___Toc37055977)[5-5 3-point and U-turns
52](#__RefHeading___Toc37055978)[5-6 Comparison of day and night
violations and TPUTs 53](#__RefHeading___Toc37055979)

[6-1 Autoscope system setup 56](#__RefHeading___Toc37055984)[6-2 Camera
location at Lurleen Wallace North (LWN) and Stillman Blvd
57](#__RefHeading___Toc37055985)[6-3 Speed detectors and calibration of
video image obtained from camera one
57](#__RefHeading___Toc37055986)[6-4 Autoscope setup to detect vehicle
speeds 58](#__RefHeading___Toc37055987)[6-5 Autoscope setup to detect
red-light violations at LWN and Stillman Blvd
58](#__RefHeading___Toc37055988)[6-6 Autoscope setup to detect red-light
violations with pre-timed traffic control plan
59](#__RefHeading___Toc37055989)[6-7 Autoscope output used to trigger
VMS 59](#__RefHeading___Toc37055990)

[7-1 Conventional video recording camera
62](#__RefHeading___Toc37055993)[7-2 Digital video camera recorder
63](#__RefHeading___Toc37055994)[7-3 I^3^ Cam Van
64](#__RefHeading___Toc37055995)

[8-1 Operation cycle of TransGuide 67](#__RefHeading___Toc37056002)[8-2
SmartTrek-Traffic TV 69](#__RefHeading___Toc37056003)[8-3 Video image
monitor 71](#__RefHeading___Toc37056004)

# 

# Executive Summary

Video technology applications for traffic management and safety are
being implemented by state and local government agencies in Alabama.
This technology offers both tangible and intangible benefits. Video
technology requires a substantial up-front investment costs for the
purchase and installation of equipment and training of staff. Due to the
complexity of video systems and the rapid pace of change related to this
technology, agencies may overlook some applications while using
resources to implement less valuable applications. This research applied
and tested new, low cost video applications for traffic management and
safety in Alabama on pilot scale projects to help agencies employ video
technology to its fullest potential. Several video systems, like digital
video cameras, conventional video recorders, surveillance cameras, and
Autoscope were implemented and tested at various locations within the
state. This research project demonstrated applications of low cost video
technology that can be implemented to improve safety and reduce crashes
and violations.

Section 1

# Introduction

Video technology applications for traffic management and safety are
being implemented by state and local government agencies in Alabama.
This technology offers both tangible and intangible benefits related to
safety and management of traffic. A comprehensive review of current and
potential uses of video technology for traffic management and safety in
Alabama was performed by the University Transportation Center for
Alabama (UTCA). Preliminary results from this research indicate several
specific applications of video technology that may be implemented in the
State of Alabama to improve traffic management and safety (McFadden and
Graettinger, 2000).

Although video technology provides many benefits, it requires
substantial up-front costs for purchasing and installing the equipment,
and for training staff. Due to the complexity of video systems and the
rapid pace of change related to this technology, agencies may overlook
some applications while using resources to implement less valuable
applications.

The goal of this research was to apply and test new video applications
for traffic management and safety in Alabama on pilot scale projects to
help agencies employ video technology to its fullest potential. This
research provides a review of the current capabilities of video
applications, their implementation, and where in Alabama these systems
can most improve traffic management and safety.

This research first identified the specific areas of concern (red-light
running, speeding, and railroad grade crossing safety) and then found
specific locations in Alabama where video technology can be potentially
employed to improve traffic management and safety. Several video
systems, like digital video cameras, conventional video recorders,
surveillance cameras, and Autoscope were implemented and tested.

Several organizations such as the City of Birmingham (Traffic
Engineering Division), the Tuscaloosa Department of Transportation
(TDOT), the Alabama Department of Transportation (ALDOT), the Alabama
Department of Public Safety (DPS), and the Alabama Emergency Management
Agency (EMA) were involved in executing the tasks associated with this
project.

This report is organized to reflect the research tasks accomplished
during this project:

Section 2.0: CARE^®^ Analysis for Identification of Video Surveillance
Locations

Section 3.0: Compositional Traffic Counting Using Video Technology

Section 4.0: 15^th^ Street Corridor Modeling

Section 5.0: Railroad-grade Crossing Automated Video Enforcement
Analysis

Section 6.0: Variable Message Signs for Violation Notification

Section 7.0: Mobile Video Units for Intersection Analysis

Section 8.0: Video for Emergency Response Management

Section 9.0: Conclusions and Recommendations

Section 2

# CARE Analysis for Identification of Video Surveillance Locations

## Introduction

Video surveillance is beneficial if the surveillance occurs at the same
location as traffic incidents. To develop appropriate countermeasure for
locations with high incidents of traffic crashes, a study is required to
identify these locations. The necessary data for this purpose can be
obtained from the Critical Analysis Reporting Environment (CARE^®^)
database, which provides a comprehensive record of traffic crash
information in the State of Alabama. This data is distributed to traffic
safety professionals who perform investigations and select
countermeasures.

To gauge the potential impact of video technology applications, three
specific types of crashes were examined related to red-light running
(RLR), speeding, and railroad-grade crossings for the years 1994-2000 in
Alabama. This section of the report includes an overview of the CARE^®^
software, the method of data acquisition, and analyses of information
for the purpose of the intended study.

## CARE^®^ Overview

CARE^®^ is one of the most sophisticated systems designed for crash
problem identification. It is a software system that provides accident
information to decision-makers involved with traffic safety. These
individuals then analyze the potential crash causes and develop
solutions to combat specific types of traffic crashes. CARE^®^ was
developed at The University of Alabama to provide a simple tool to
access the vast database associated with accident information, and it
exists on two platforms: desktop and Internet (http://care.cs.ua.edu).

Some of the CARE^®^ capabilities are listed below:

-   CARE^®^ gives information on high-accident location (by frequency)
    with a graphical display for major roadways.

-   CARE^®^ is designed for problem identification and countermeasure
    development for crashes associated with alcohol, speed, RLR,
    railroad crossings, etc.

-   CARE^®^ produces statistics that allow visualization of
    statistically significant results.

-   CARE^®^ acts as a "front end" to many statistical processors, and is
    fully compatible with existing data-oriented software packages such
    as MS-Access and MS-Excel (Keith and Brown, 2002).

-   CARE^®^ automatically prioritizes counties, cities, and even
    intersections or road segments by the number of crashes for a
    particular query.

    CARE^®^ can be divided into three components: 1) a dataset, 2) a
    filter, and 3) an analysis process. The system is query-based, which
    means to run CARE^®^ and analyze information, a dataset and a filter
    are needed. The complete dataset consists of accident and occupant
    data from the years 1994 to the present for the State of Alabama. To
    make the data more manageable a subset can be generated and employed
    in subsequent queries through a filter. A filter is a specification
    used to restrict processing of a particular query. Various types of
    CARE^®^ analyses can be performed with a selected filter. Two
    analyses reported in this work were: 1) Frequency Analysis, a simple
    count and percentage provider and 2) Cross-tabulation, a
    simultaneous summary of two different variables that includes counts
    and percentages.

Apart from these two analyses, CARE also provides advanced features like
an IMPACT analysis, HOTSPOT locations, Intersection Magic, ACT, and
DataGen. IMPACT provides outputs in a worst-first order for each
variable and identifies scenarios where appropriate counter measures may
reduce maximum incidents. HOTSPOT can be employed to list details of
intersections with the highest numbers of crashes. A description of
these preprogrammed analyses can be found in Brown and Turner, 2002.

## CARE^®^ Analysis for Video Location Identification

CARE^®^ was used to develop a criteria for determining high accident
locations in Alabama focusing on RLR, speeding, and railroad related
crashes. These crash types were selected because they were the subject
of ongoing research focused on employing video technology as a
countermeasure (McFadden and Graettinger, 2000). The analysis was
performed for the entire State of Alabama, for counties with the highest
crash rates, and for the City of Tuscaloosa. Variables such as month,
day of the week, time, primary contributing circumstances, number of
injuries and fatalities, age of the driver, gender, traffic control
unit, direction of travel, speed limit, traffic lanes, accident
severity, and weather conditions assisted in refining the data retrieval
for the most probable causes for the accidents.

As an example, the following steps describe the procedure to retrieve
accident data for the City of Tuscaloosa for the three categories of
crashes:

Step 1: The 1994-2000 Alabama accident dataset was selected.

Step 2: Using the "create filter" option in CARE^®^, new filters T-City
(City of Tuscaloosa), RLR (red-light running - fail to heed to
signal/sign with a traffic signal), Speeding (speeding), and RR
(railroad grade crossings-related) were generated.

Step 3: Using the filter combination option in CARE^®^, the generated
filters were then combined as T-City-RLR, T-City-speeding, and
T-City-RR.

Step 4: To select the most critical scenario, specific analyses like
frequency, cross tabulation, IMPACT, and HOTSPOT were performed for
crash variables.

The retrieved information was summarized in the form of tables and bar
charts. Based on the results obtained from the CARE^®^ system, general
crash trends for Alabama and the City of Tuscaloosa were analyzed and
reported.

It should be noted that CARE^®^ retrieved the accident data by number of
crashes only, and did not consider total vehicular volume or number of
violations at or near the crash location. Another limitation of using
CARE^®^ is the potential legal repercussions associated with the release
of the specific locations of high crash incidents. Therefore, this
report uses cities as the smallest geographic area although specific
high incident intersections can be identified in a similar manner.
IMPACT and HOTSPOT were beneficial in identifying intersections for
video surveillance, although the results are not reported herein. In
many cases, due to the infrequency of some types of crashes, there was
no statistical significance for choosing one intersection over another.
However, when used in appropriate situations with appropriate data,
CARE^®^ yields extremely useful safety information, which can be
employed to help locate video surveillance units.

## 

## 

## General crash trends in Alabama

Crash data related to Alabama RLR, speeding, and railroad grade
crossings is summarized in Table 2-1. It can be seen from Table 2-1 that
the total number of railroad related crashes in the State is quite small
(946) when compared to speed (51,231) and RLR (31,750) related crashes.
This indicates that employing video surveillance to capture a railroad
related crash would be difficult, but traffic behavior at a railroad
grade crossing could be analyzed with the help of video.

[]{#__RefHeading___Toc36976511 .anchor}Table 2-. Summary of 1994-2000
Alabama accident data

  ---------------------- ------------------ ---------------- ----------------
                         **Red-light        **Speeding**     **Railroad**
                         running**                           

  Property damage        22145              25974            484

  Injuries               9533               23271            390

  Fatal                  72                 1986             72

  Total crashes          **31750**          **51231**        **946**

  **[High accident                                           
  rate]{.underline}**:                                       

  Month                  December           May              June

  Day of week            Friday             Saturday         Thursday

  Time                   3:00 PM - 4:00 PM  3:00 PM - 4:00   4:00 PM - 5:00
                                            PM               PM

  Speed limit            31 -- 35 mph       41 -- 45 mph     21 -- 25 mph

  Number of lanes        4                  2                2

  Weather                Clear              Clear            Clear
  ---------------------- ------------------ ---------------- ----------------

Although there were more speed related crashes in the State than
railroad or RLR related crashes, these crashes typically occur on
two-lane roads with posted speed limits between 41 -- 45 mph. This
indicates a rural area where it would be difficult to capture a crash or
even evaluate traffic behavior with video because the area to be covered
is very large. If one rural location produced a high crash rate, this
could be a location for video surveillance.

RLR is ideal for video surveillance. First, there is a high rate of this
type of crash. Second, these crashes occur at specific locations, which
are small enough to be captured on a single video system. Lastly, RLR
crashes occur in an urban development, which lends itself to permanent
video systems like the system in Tuscaloosa.

The annual statistics of Alabama-specific crash severity for the years
1994-2000 are reported in Tables 2-2, 2-3, and 2-4.

[]{#__RefHeading___Toc36976512 .anchor}Table 2-. Annual Alabama
red-light running crash data

[]{#__RefHeading___Toc36976513 .anchor}Table 2-. Annual Alabama
speed-related crash data

[]{#__RefHeading___Toc36976514 .anchor}Table 2-. Annual Alabama railroad
grade crossing crash data

The City of Tuscaloosa specific crash statistics for variables like
time, day of the week, month, age of the driver, condition of the
driver, gender, speed limit, number of traffic lanes, and weather
conditions are summarized in Table 2-5.

### 

[]{#__RefHeading___Toc36976515 .anchor}Table 2-. Summary of 1994-2000
Tuscaloosa accident data

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### 

### Red-light running crashes and trends

Red-light running crash data is reported in Table 2-6 for the counties
with the most crashes, and in Table 2-7 for cities with the most
crashes. Table 2-8 and column two of Table 2-5 present detailed findings
of RLR-related crashes in the City of Tuscaloosa for the years
1994-2000.

[]{#__RefHeading___Toc36976516 .anchor}Table 2-. Selected county
red-light running crash data

  ----------- --------------- ------------ ---------------- ------------- ---------------- -------------
  **Year**    **Jefferson**   **Mobile**   **Montgomery**   **Madison**   **Tuscaloosa**   **Calhoun**

  **1994**    1058            633          460              405           202              176

  **1995**    1082            622          458              391           229              154

  **1996**    1072            624          477              376           231              166

  **1997**    946             704          423              406           213              136

  **1998**    1010            676          453              424           258              117

  **1999**    930             621          470              470           266              119

  **2000**    889             625          407              427           229              117

  **Total**   **6987**        **4505**     **3148**         **2899**      **1628**         **985**
  ----------- --------------- ------------ ---------------- ------------- ---------------- -------------

[]{#__RefHeading___Toc36976517 .anchor}Table 2-. Selected city 1994-2000
RLR related crashes

  ----------------- -------------------------- ---------------------------
  **Ranking**       **City name**              **No. of crashes**

  1                 Birmingham                 4008

  2                 Mobile                     3353

  3                 Montgomery                 3129

  4                 Huntsville                 2717

  5                 Tuscaloosa                 1417

  6                 Florence                   857

  7                 Dothan                     816

  8                 Gadsden                    692

  9                 Decatur                    665

  10                Mobile Rural               663

  11                Auburn                     636

  12                Anniston                   621
  ----------------- -------------------------- ---------------------------

Table 2-7 shows that the City of Tuscaloosa stands fifth in the state
with 1417 RLR-related crashes out of a total of 31,750 RLR crashes in
Alabama. It should be noted that these numbers are not per capita;
therefore, one would expect larger cities to have a larger total number
of crashes. Table 2-8 shows RLR crashes in each of the severity
categories (property damage, injuries, and fatalities), which remained
approximately the same in Tuscaloosa between the years 1994 - 2000.

[]{#__RefHeading___Toc36976518 .anchor}Table 2-. Tuscaloosa red-light
running crash data

  ---------- --------------------------- -------------- ---------------- -----------
  **Year**   **Property damage only**    **Injuries**   **Fatalities**   **Total**

  **1994**   118                         56             0                174

  **1995**   133                         65             1                199

  **1996**   144                         62             2                208

  **1997**   128                         56             0                184

  **1998**   152                         75             2                229

  **1999**   152                         80             0                232

  **2000**   134                         57             0                191
  ---------- --------------------------- -------------- ---------------- -----------

About 36% of these crashes in Tuscaloosa happened on roads of six or
more lanes, and about 38% happened on roads with a speed limit between
41-45 mph (city major roads). Friday experienced relatively more crashes
than any other day in a week, and 5:00 PM-6:00 PM was the most crash
prone time of the day.

### Speed related crashes and trends

Speed-related crashes for the counties with the most accidents are
presented in Table 2-9, while Table 2-10 reports the cities with the
most accidents. Table 2-11 and column three of Table 2-5 present
detailed findings of speed-related crashes in the City of Tuscaloosa for
the years 1994-2000.

[]{#__RefHeading___Toc36976519 .anchor}Table 2-. Selected county
speed-related crash data

  ----------- --------------- ------------ ---------------- ------------- ------------- -------------
  **Year**    **Jefferson**   **Mobile**   **Tuscaloosa**   **Madison**   **Calhoun**   **Baldwin**

  **1994**    915             351          272              254           243           198

  **1995**    899             361          279              300           244           179

  **1996**    839             378          273              289           233           224

  **1997**    800             430          339              309           246           216

  **1998**    721             403          357              311           282           312

  **1999**    730             401          362              332           301           179

  **2000**    672             369          333              332           290           236

  **Total**   **5576**        **2693**     **2215**         **2127**      **1839**      **1544**
  ----------- --------------- ------------ ---------------- ------------- ------------- -------------

Tuscaloosa County had the third highest number of speed-related
accidents from 1994 through 2000. And, speed-related crashes were more
severe than general crashes. Speed related crashes in the City of
Tuscaloosa occurred on Friday more frequently than any other day and
almost 50% happened on roads with a speed limit of 21-25 mph (mostly
residential areas). The frequency of crashes was observed to be higher
between 3:00 PM - 4:00 PM.

[]{#__RefHeading___Toc36976520 .anchor}Table 2-. Selected city 1994-2000
speed-related crashes

  --------------- ----------------------------- -------------------------
  **Ranking**     **City name**                 **No. of crashes**

  1               Jefferson Rural               2108

  2               Birmingham                    1495

  3               Mobile Rural                  1314

  4               Tuscaloosa Rural              1291

  5               Baldwin Rural                 1212

  6               Madison Rural                 1193

  7               Calhou Rural                  1081

  8               Talladega Rural               1050

  9               Mobile                        989

  10              Walker Rural                  797

  11              Montgomery                    774

  12              Tuscaloosa                    708
  --------------- ----------------------------- -------------------------

[]{#__RefHeading___Toc36976521 .anchor}Table 2-. Tuscaloosa
speed-related crash data

  ---------- ------------------------- -------------- ---------------- -----------
  **Year**   **Property damage only**  **Injuries**   **Fatalities**   **Total**

  **1994**   67                        30             2                99

  **1995**   65                        35             1                101

  **1996**   67                        47             2                116

  **1997**   67                        33             3                103

  **1998**   70                        37             1                108

  **1999**   56                        41             1                98

  **2000**   50                        32             1                83
  ---------- ------------------------- -------------- ---------------- -----------

### Railroad (RR) related crashes and trends

Selected county and city crash data related to railroad grade crossing
accidents are summarized in Tables 2-12 and 2-13. Detailed accident
information for the City of Tuscaloosa during the years 1994 - 2000 is
summarized in Table 2-14 and in column four of Table 2-5. Due to the
small number of crashes in Tuscaloosa, almost no conclusions can be
drawn from the CARE^®^ data. From the data, the highest rate of railroad
related crashes occurred on Wednesdays between 8:00 - 9:00 AM and PM.
Although there were no fatalities, each railroad accident caused
property damage and the possibility of the accident being fatal was very
high. It should be noted that due to the small number of
railroad-related crashes, any spike or hotspot in the data is most
likely random and do not indicate a trend in these crashes.

[]{#__RefHeading___Toc36976522 .anchor}Table 2-. Selected county
railroad grade crossing crash data

  ----------- --------------- ------------ ---------------- -------------- --------------- ------------ ---------
  **Year**    **Jefferson**   **Mobile**   **Tuscaloosa**   **Escambia**   **Talladega**   **Dallas**   **Lee**

  **1994**    32              16           6                17             5               3            5

  **1995**    31              6            10               4              12              3            2

  **1996**    21              20           7                6              2               7            0

  **1997**    16              16           8                5              5               9            6

  **1998**    17              11           15               4              3               4            6

  **1999**    23              5            4                7              3               2            4

  **2000**    9               7            4                1              5               4            9

  **Total**   **149**         **81**       **54**           **44**         **35**          **32**       **32**
  ----------- --------------- ------------ ---------------- -------------- --------------- ------------ ---------

[]{#__RefHeading___Toc36976523 .anchor}Table 2-. Selected city 1994-2000
railroad related crashes

  ----------------- -------------------------- --------------------------
  **Ranking**       **City name**              **No. of crashes**

  1                 Brimingham                 95

  2                 Tuscaloosa                 36

  2                 Mobile                     36

  3                 Montgomery                 27

  3                 Mobile Rural               27

  4                 Colber Rural               25

  5                 Bessemer                   21

  5                 Selma                      21

  6                 Jefferson rural            18

  6                 Atmore                     18

  7                 Auburn                     17

  7                 Morgan Rural               17
  ----------------- -------------------------- --------------------------

[]{#__RefHeading___Toc36976524 .anchor}Table 2-. Tuscaloosa railroad
grade crossings accident data

  ---------- -------------------------- -------------- ---------------- -----------
  **Year**   **Property damage only**   **Injuries**   **Fatalities**   **Total**

  **1994**   4                          0              0                4

  **1995**   4                          3              0                7

  **1996**   4                          2              0                6

  **1997**   3                          3              0                6

  **1998**   7                          1              0                8

  **1999**   2                          0              0                2

  **2000**   3                          0              0                3
  ---------- -------------------------- -------------- ---------------- -----------

##  

## Video technology locations based on CARE^®^ data

Crash related information generated by CARE^®^ can be useful in
understanding crash scenarios. The dataset employed by CARE^®^ has
location information down to the intersection level, although in this
report City was the smallest geographic area reported. CARE^®^ retrieves
crash details of intersections that have the most incidences, which on
occasion may list intersections that had only one accident of a specific
type. An example of this is railroad-related crashes where there may
only be one occurrence in an entire City or County. Hence, it is
recommended to analyze other parameters like history of violations,
traffic volume, and neighborhood characteristics to select locations for
installing video equipment. Data acquisition from CARE^®^ can be used to
summarize the crash statistics at four different levels -- state,
county, city, and location (intersection or road link). This compilation
allows decision makers to select locations with the highest numbers of
specific types of crashes for further study. The use of parameters like
speed limit and traffic lanes for choosing video locations further
refines potential video locations.

It can be concluded that CARE^®^ can aid, but cannot be the sole basis
for development of the criteria to select locations for the deployment
of a video-based technology.

Section 3

# Compositional Traffic Counting Using Video Technology

## Introduction

To assess highway system performance, states are required to submit
numerous annual compositional traffic count reports to the Federal
Highway Administration (FHWA). Compositional counts represent the volume
by type (trucks, trailers, buses, recreational vehicle, automobiles,
motorcycles, etc.). These classifications are based on axle arrangement,
number of trailers, and other vehicle characteristics. This data is
typically collected manually or by automatic counters.

The primary objective of this portion of the research was to evaluate
the use of video-based technology for vehicle classification, and to
develop a methodology for obtaining compositional vehicle counts solely
using video.

## Background 

The FHWA's Office of Highway Information Management conducts a national
program that assembles traffic reports furnished by individual state
highway agencies into a national database entitled Highway Performance
Monitoring System (HPMS). These compositional traffic counts are
important traffic data used to support decision-making and design
processes. These counts can be used for a variety of purposes, some of
which are mentioned below (TMG, 2001):

1.  planning and programming of transportation facilities,

```{=html}
<!-- -->
```
1.  pavement design and rehabilitation,

2.  apportionment of pavement damage,

3.  compliance with vehicle weight regulations,

4.  development of geometric design standards,

5.  compliance and regulatory policy development of truck dimensions,

6.  safety analysis,

7.  traffic operation and control,

8.  analysis related to highway bridges,

9.  estimating truck traffic for highway cost allocation studies,

10. predicting traffic volumes for roadways, and

11. aiding in the formulation of air quality models.

HPMS reporting requirements suggest that States are to submit vehicle
classification data by 14 categories (13 vehicle categories and 1
unknown category) under each of the 12 roadway functional classes as
shown in Table 3-1. There is a growing controversy over this procedure
because the vehicle classification data collectors prefer to report
fewer classes while the users of the data prefer more classes.

[]{#__RefHeading___Toc37055678 .anchor}Table 3-. Vehicle classification
data by roadway functional class as reported to HPMS

<table style="width:100%;">
<colgroup>
<col style="width: 3%" />
<col style="width: 8%" />
<col style="width: 5%" />
<col style="width: 9%" />
<col style="width: 6%" />
<col style="width: 4%" />
<col style="width: 6%" />
<col style="width: 5%" />
<col style="width: 5%" />
<col style="width: 7%" />
<col style="width: 5%" />
<col style="width: 7%" />
<col style="width: 6%" />
<col style="width: 5%" />
<col style="width: 6%" />
<col style="width: 6%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="2" rowspan="4"><p>Functional</p>
<p>System</p></td>
<td><strong>1</strong></td>
<td><strong>2</strong></td>
<td><strong>3</strong></td>
<td><strong>4</strong></td>
<td><strong>5</strong></td>
<td><strong>6</strong></td>
<td><strong>7</strong></td>
<td><strong>8</strong></td>
<td><strong>9</strong></td>
<td><strong>10</strong></td>
<td><strong>11</strong></td>
<td><strong>12</strong></td>
<td><strong>13</strong></td>
<td><strong>14</strong></td>
</tr>
<tr class="even">
<td rowspan="3"><p>Motor</p>
<p>Cycle</p></td>
<td rowspan="2"><p>Passenger</p>
<p>car</p></td>
<td rowspan="2">Light truck</td>
<td rowspan="3">Bus</td>
<td colspan="3">Single Unit Truck</td>
<td colspan="3">Single Trailer Truck</td>
<td colspan="3">Multi-Trailer Truck</td>
<td rowspan="3"><p>Un-</p>
<p>known</p></td>
</tr>
<tr class="odd">
<td rowspan="2"><p>2 axle</p>
<p>6 tire</p></td>
<td rowspan="2">3 axle</td>
<td rowspan="2"><p>4 axle</p>
<p>or less</p></td>
<td rowspan="2"><p>4 axle</p>
<p>or more</p></td>
<td rowspan="2">5 axle</td>
<td rowspan="2"><p>6 axle</p>
<p>or more</p></td>
<td rowspan="2"><p>5 axle</p>
<p>or less</p></td>
<td rowspan="2">6 axle</td>
<td rowspan="2"><p>7 axle</p>
<p>or more</p></td>
</tr>
<tr class="even">
<td><p>2 axle,</p>
<p>4 tires</p></td>
<td><p>Other</p>
<p>2 axle,</p>
<p>4 tires</p></td>
</tr>
<tr class="odd">
<td colspan="16"><strong>RURAL</strong></td>
</tr>
<tr class="even">
<td><strong>1</strong></td>
<td>Interstate</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><strong>2</strong></td>
<td><p>Other</p>
<p>Principal</p>
<p>Arterial</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><strong>3</strong></td>
<td><p>Minor</p>
<p>Arterial</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><strong>4</strong></td>
<td><p>Major</p>
<p>Collector</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><strong>5</strong></td>
<td><p>Minor</p>
<p>Collector</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><strong>6</strong></td>
<td>Local</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td colspan="16"><strong>URBAN</strong></td>
</tr>
<tr class="odd">
<td><strong>7</strong></td>
<td>Interstate</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><strong>8</strong></td>
<td><p>Other</p>
<p>Principal</p>
<p>Arterial</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><strong>9</strong></td>
<td><p>Minor</p>
<p>Arterial</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><strong>10</strong></td>
<td><p>Major</p>
<p>Collector</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><strong>11</strong></td>
<td><p>Minor</p>
<p>Collector</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><strong>12</strong></td>
<td>Local</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

Until the development of non-intrusive technologies (video-based,
microwave radar, and infrared sensors), the methods for collecting
traffic volume and vehicle classification counts were limited to manual
counts, road tubes, and inductive loops. These conventional methods had
limitations such as traffic disruption, staff safety, and efficiency of
data collection. Recently developed technologies including video and
radar can be mounted overhead or to the side of the roadway. They do not
disrupt traffic, and they increase staff safety.

The advent of video with image processing created several options for
traffic data collection. Advantages of video vehicle detectors are:
multi-lane data collection by one detector, collection of a large
variety of traffic data, and visual traffic surveillance. Video systems
have disadvantages such as a drop in performance under certain
environmental conditions. In addition to environmental issues, video
technology classifies vehicles in fewer groups than the 14 FHWA classes.
Hence, there is a need for a methodology that can take video data and
produce the 14 FHWA classes. This was the focus of this research
section.

## Video Cameras and Image Processor

Video image processors employ machine vision technology to automatically
analyze video images. A video image processor system consists of one or
more cameras, whose data is processed and digitized either in the camera
unit itself or is fed to a computer. Application-specific software for
interpreting the images to extract information for traffic surveillance
and control is available (Sami Mohamed, 1996). Similar to inductive
loops, video places "virtual loops" in the area of interest to detect
changes as a vehicle passes. The image processing algorithms analyze the
variation of gray levels in the group of pixels contained in the video
image plane. Traffic flow parameters such as volume, flow rate, speed,
presence, occupancy, density, queue length, headway, dwell time, and
classification based on length can be calculated by analyzing successive
video frames (FHWA-PL-97-018, 1997).

Image processing technology does not require color for the detection
process and hence can use monochrome cameras. These cameras can provide
continuous live coverage of traffic, and the software allows placement
of virtual loop detectors and detection boxes on the TV or computer
screen without disrupting traffic. As in inductive loops, a signal is
generated when a car passes through a virtual detector and a single
video unit can manage multiple detectors placed in multiple lanes (Hani,
1999). Advantages include the ability to cover a wide area, along with
the ability to rapidly extract a variety of traffic parameters. Some
limitations of video include occlusion, shadowing, salt grime, icicles,
cobwebs, and inability to perform well in adverse weather conditions.

Some of the systems that are available in the market are Video Trak-900,
Autoscope Solo Pro used in this work and shown in Figure 3-1, TraFicon
VIP 3.1, and Vantage One video detection systems. Since these systems
are much safer and easier to use than loops or tubes, research is being
conducted to minimize the drawbacks associated with these systems.

![](media/image1.jpeg){width="1.7083333333333333in"
height="1.3520833333333333in"}

[]{#__RefHeading___Toc37055938 .anchor}Figure 3-. Autoscope Solo Pro
camera

Although the permanent installation costs of video systems are
expensive, their flexibility and low maintenance costs make them a
cost-effective means of obtaining traffic data. In addition, these
systems are portable and can be set up inexpensively for short-term
analyses.

All video image systems collect classification information based on
vehicle length, which classifies vehicles into fewer and more general
categories than the 14 FHWA classes (TMG, 2001). This is because:

-   broad vehicle length categories reduce the amount of error
    associated with misclassification, as video classifiers are not
    accurate enough in measuring small differences in vehicle length.

-   video cannot differentiate between long vehicles (trucks) and small
    vehicles with trailers (boat trailers) of equal lengths.

-   small differences in truck lengths are difficult to identify and
    hence it is advisable to group all trucks together.

According to the Traffic Monitoring Guide (TMG), four vehicle length
categories are sufficient for most analytical purposes, which primarily
require data on heavy trucks and light vehicles. These four traditional
classes reflect passenger cars, single unit trucks, single-trailer
combination trucks, and multi-trailer trucks. As suggested in the TMG,
Table 3-2 shows the vehicle length boundaries typically used to estimate
the four vehicle classes (TMG, 2001).

[]{#__RefHeading___Toc37055679 .anchor}Table 3-. Vehicle classification
based on length given by Traffic Monitoring Guide

  --------------------------------------- -------------------------------
  **Vehicle Class**                       **Boundaries**

  Passenger vehicles                      0 - 13 ft

  Single unit trucks                      13 - 35 ft

  Combination trucks                      35 - 61 ft

  Multi-trailer trucks                    61 - 120 ft
  --------------------------------------- -------------------------------

## 

Advanced video cameras with machine vision processors (MVP), such as
Autoscope, can process and store up to five vehicle classes based on
length. This is insufficient for FHWA classification, hence, a
methodology was developed at the University of Alabama during this
project to generate the 14 FHWA vehicle classes from Autoscope classes.

## Experimental Design

A vehicle classification disaggregation (VCD) model was developed based
on a disaggregation process, which was introduced in stochastic
hydrology by Valencia and Schaake (1973) for generating (or
disaggregating) seasonal, monthly, weekly, daily, and even hourly
variations in rainfall using a series of historical annual rainfall
data. This method is very popular as it conserves all linear
relationships between variables at successive levels, and preserves the
long and short-term variance and covariance properties, including
seasonal variations (Valencia and Schaake, 1973). Using the principle of
disaggregation, a model was developed at The University of Alabama for
estimating the 14 FHWA vehicle classes from as few as two Autoscope
length-based classifications. The next section contains a detailed
description of the equipment used, site selection process, procedures
adopted, and problems encountered, to perform the traffic data
collection needed to develop the disaggregation methodology.

### Equipment and cost

For the purpose of this research, the Autoscope Solo Pro MVP was chosen
because of its enhanced features such as color imaging; integrated zoom
lens; and ability to detect, count, and store five vehicle categories
based on length (Econolite, 2001). The portable Autoscope Solo Pro MVP
setup used to perform the data collection consisted of the following
components.

1.  Autoscope Solo Pro MVP camera (\$8000)

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```
12. Interface panel and Minihub (\$1000)

13. Laptop or PC to store the data (\$1400)

14. Solo MVP 30 foot cable (\$350)

15. Gas generator to supply power to the above equipment (\$400)

The total cost of the portable setup was approximately \$11,000. Figure
3-2 shows the Autoscope installation in the field. It should be noted
that an Autoscope field setup takes very little space and can be
established in 15 minutes.

![](media/image2.jpeg){width="3.395138888888889in"
height="2.5569444444444445in"}

[]{#__RefHeading___Toc37055939 .anchor}Figure 3-. Autoscope field setup

### 

### Site Selection

For the purpose of selecting a site, ALDOT officials were contacted and
a list of locations was obtained where ALDOT maintains axle counters. As
shown in Figure 3-3, a site on Interstate 65 at milepost 210 in the City
of Clanton was selected for two reasons.

![](media/image3.jpeg){width="3.2604166666666665in"
height="3.1368055555555556in"}

[]{#__RefHeading___Toc37055940 .anchor}Figure 3-. Location of Autoscope
at milepost 210 on I-65

**(courtesy: Microsoft Streets & Trips 2001)**

First, an ALDOT axle counter is located there. Second, the presence of
an overhead bridge just above the axle counters provided a location for
temporary installation of an Autoscope camera. The camera was placed on
the bridge railing above the southbound lanes at a height of 23 feet.
The axle counters were functional for both northbound and southbound
traffic on I-65, which allowed data collection in either direction. The
field setup at the Clanton site is shown in Figure 3-2.

### Period of I-65 data collection

Video data was collected eight hours per day on three weekdays (Monday,
Wednesday, and Friday) for two weeks. A total of 28 hours of useful data
was obtained for this research. The majority of the data (25 hours) was
used to develop the vehicle classification disaggregation (VCD) model
while the remaining portion (3 hours) was used to evaluate the
performance of the model.

### Procedure for data collection by Autoscope 

Video detection requires a clear view of the area in which detection is
desired. The video image must be calibrated to establish a relationship
between the ground and the camera's field of view. For this purpose,
lane widths, down lane distances, and the camera height were measured.
Calibration lines representing the ground measurements were digitally
placed on the video image parallel and perpendicular to the traffic
lanes. By placing the calibration lines on the image in the same
location as the ground measurements, the image was calibrated to the
ground.

After calibrating the field of view, detector files were created using
the Autoscope software. These detectors were placed on the video image
and treated as "virtual" loops. Detector files play an integral part in
the accuracy of machine vision counts for volume, speed, classification,
and other parameters. While creating detector files, care should be
taken with areas of occlusion, areas of weaving traffic, and relative
vehicle position in the lane to avoid missing detection. Typical
detectors types are presence, count, speed, and station detectors, which
can be combined to create a "detector function" to provide customized
output.

Once detectors were in place and the detector configuration had been
downloaded into Autoscope, polling (recording) of data begun. Vehicle
detection was carried out with a superimposed image of the traffic on
the computer screen. Autoscope continuously provided detector outputs
and stored traffic data in its internal non-volatile memory to protect
against power failure. The collected traffic and detection data was made
available in a readily accessible ASCII (text) format. The computer
software provided file management routines for efficiently filing,
retrieving, and reporting of the collected traffic data.

### Autoscope Length Classifications

Autoscope has the capability to classify vehicles into a maximum of five
categories based on length. These categories are: vehicles less than 23
feet (class A), vehicles between 23 and 40 feet (class B), vehicles
between 40 and 75 feet (class C), vehicles between 75 and 80 feet (class
D), and vehicles greater than 80 feet (class E). A list of the 14 FHWA
classes, the corresponding aggregated five Autoscope classes, and their
length boundaries are summarized in Table 3-3. The table also shows two
additional Autoscope classifications (Autoscope 3 classes and Autoscope
2 classes) that were evaluated at the site. When comparing Autoscope and
FHWA data where more Autoscope classes were used, the Autoscope count
data became less accurate. Therefore, the disaggregation model was
developed on the Autoscope two-class grouping where vehicles were
classified as less than 23 feet (class A) and greater than 23 feet
(class B), as shown in the last column of Table 3-3.

[]{#__RefHeading___Toc37055680 .anchor}Table 3-. Grouped FHWA and
Autoscope classes used in VCD model development

+-----+---------------------------+-----------+-----------+----------+
| **C | FHWA Class                | **Au      | **Au      | **Aut    |
| las |                           | toscope** | toscope** | oscope** |
| s** |                           |           |           |          |
|     |                           | **5       | **3       | **2      |
| **N |                           | classes** | classes** | c        |
| o** |                           |           |           | lasses** |
+-----+---------------------------+-----------+-----------+----------+
| 1   | Motorcycles               |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 2   | Passenger cars            | A: 0 - 23 | A: 0 - 23 | A: 0 -   |
|     |                           | feet      | feet      | 23 feet  |
+-----+---------------------------+-----------+-----------+----------+
| 3   | Other 2-axle 4-tire       |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
|     |                           |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 4   | Buses                     |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 5   | Single Unit Trucks        | B: 23 -   |           |          |
|     | 2-axle, 6-tire            | 40 feet   |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 6   | Single Unit Trucks 3-axle |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
|     |                           |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 7   | Single Unit Trucks 4-axle |           | B: 23 -   | B: \> 23 |
|     | or more                   |           | 80 feet   | feet     |
+-----+---------------------------+-----------+-----------+----------+
| 8   | Single Trailer Truck      |           |           |          |
|     | 4-axle or less            |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 9   | Single Trailer Truck      | C: 40 -   |           |          |
|     | 5-axle                    | 75 feet   |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 10  | Single Trailer Truck      |           |           |          |
|     | 6-axle or more            |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 11  | Multi Trailer Truck       |           |           |          |
|     | 5-axle or less            |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
|     |                           |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 12  | Multi Trailer Truck       | D: 75 -   |           |          |
|     | 6-axle                    | 80 feet   |           |          |
+-----+---------------------------+-----------+-----------+----------+
|     |                           |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 13  | Multi Trailer Truck       |           |           |          |
|     | 7-axle or more            |           |           |          |
+-----+---------------------------+-----------+-----------+----------+
| 14  | Unknown                   | E: \> 80  | C: \> 80  |          |
|     |                           | feet      | feet      |          |
+-----+---------------------------+-----------+-----------+----------+

In the two-class grouping, class A corresponds to FHWA classes one to
three, and class B corresponds to FHWA classes four to 14. Table 3-4
illustrates the comparison of data collected by Autoscope and the FHWA
axle count data.

Columns one and two of Table 3-4 are Autoscope counts for Class A and B
denoted by X1 and X2. Columns three and four are a summation of the FHWA
axle count data for class one to three and class four-14 respectively.
Columns five and six show the difference between the two Autoscope
classes and the corresponding aggregated FHWA classes. A difference in
the total hourly volume counts is shown in column seven, which ranges
from --81 to +114, with a negligible average difference of --6 vehicles.
The mean and standard deviations of the corresponding columns shown in
the last two rows indicates the range of differences in Autoscope and
FHWA values for which the VCD model is developed. The differences can be
attributed to calibration errors, bad triggers caused by shadowing of
vehicles, occlusion, time setting differences, and lane changing of
vehicles near the axle counters. These values were obtained from 28
hourly volume datasets gathered and analyzed for this research.

[]{#__RefHeading___Toc37055681 .anchor}Table 3-. Comparison between
Autoscope and FHWA data

  ----------- ------------- -------- ---------- ----------- ---------------- ----------- --------------
  **S.No**    **Autoscope            **FHWA                 **Difference**               **Net
              Data**                 Data**                                              Difference**

              **X1**        **X2**   **sum      **sum (Y4 - **col 1 -- col   **col 2 --  **col 5 + col
                                     (Y1 -      Y14)**      3**              col 4**     6**
                                     Y3)**                                               

  **Hours**   **col 1**     **col    **col 3**  **col 4**   **col 5**        **col 6**   **col 7**
                            2**                                                          

  **1**       672           163      713        159         -41              4           -37

  **2**       696           193      674        150         22               43          65

  **3**       743           200      813        187         -70              13          -57

  **4**       700           195      796        180         -96              15          **-81 (min)**

  **5**       764           182      783        161         -19              21          2

  **6**       848           211      847        166         1                45          46

  **7**       891           161      896        140         -5               21          16

  **8**       724           161      672        146         52               15          67

  **9**       731           220      746        207         -15              13          -2

  **10**      798           191      819        196         -21              -5          -26

  **11**      811           206      848        188         -37              18          -19

  **12**      905           244      932        171         -27              73          46

  **13**      944           181      962        144         -18              37          19

  **14**      816           162      722        142         94               20          **114 (max)**

  **15**      1130          201      1147       173         -17              28          11

  **16**      1454          214      1526       178         -72              36          -36

  **17**      1638          218      1722       194         -84              24          -60

  **18**      1824          216      1911       185         -87              31          -56

  **19**      1721          221      1834       159         -113             62          -51

  **20**      1634          193      1620       148         14               45          59

  **21**      692           174      742        166         -50              8           -42

  **22**      798           198      861        196         -63              2           -61

  **23**      757           193      817        191         -60              2           -58

  **24**      781           220      844        218         -63              2           -61

  **25**      789           177      804        163         -15              14          -1

  **26**      847           192      869        163         -22              29          7

  **27**      847           158      855        137         -8               21          13

  **28**      829           142      826        140         3                2           5

                                                Mean =      **-29.18**       **22.82**   **-6.36**

  Standard                                                  **45.27**        **18.73**   **48.95**
  Deviation =                                                                            
  ----------- ------------- -------- ---------- ----------- ---------------- ----------- --------------

## Vehicle Classification Disaggregation Model (VCD Model)

The disaggregation process was introduced in stochastic hydrology for
generating (or disaggregating) seasonal, monthly, weekly, daily, and
even hourly variations in rainfall using a series of annual rainfall
data. This method is very popular as it conserves all linear
relationships between variables at successive levels, and preserves the
long and short-term variance and covariance properties, including
seasonal variations. The process has been successfully used for
disaggregating rainfall data in Puerto Rico, streamflow data for the
Colorado River the United States, and for the generation of hourly water
demands in the Boston water distribution system (Valencia and Schaake,
1973).

A similar model was developed at The University of Alabama during this
research for estimating (disaggregating) the 14 FHWA classes (similar to
12 months in a year) from the aggregated Autoscope classes (similar to a
aggregated seasonal or annual rainfall or stream flow data). The goal
was to develop a relationship between the two Autoscope classes and the
14 FHWA classes by taking into account the variance and covariance
properties of the data. Using these relations, the model would use
Autoscope data for any hour as input information to generate FHWA data
for that particular hour.

The disaggregation model takes a simple mathematical form

Y = AX + BW .......................................................(3.1)

where, Y is a (n × 1) vector of disaggregated values representing the 14
FHWA classes resulting from disaggregation process, X is a (m × 1)
vector of values obtained from Autoscope classes, A is a (n × m)
parameter matrix calculated to preserve covariance between aggregated
(X) and disaggregated (Y) vectors, B is a (n × n) coefficient matrix
calculated to preserve the proper covariance structure, and W is a (n
× 1) vector of independently distributed standard normal deviates, which
are randomly generated and normally distributed elements with a mean of
μ = 0 and variance of σ^2^ = 1. In the model developed in this research,
n = 14 represent the 14 FHWA classes and m = 2 represent the two
Autoscope classes. The approach assumes that all the data used to
develop the model follow a normal distribution and that the system
maintains consistency of the cumulative relation. In other words, the
sum of the generated disaggregated values should be equal to the given
Autoscope values. Practically, all these assumptions cannot be satisfied
(Valencia and Schaake, 1973). For example, the data used for this model
need not follow a normal distribution.

Parameter matrices A and B are obtained from the following equations:

A = S~yx~S~xx~^-1^
............................................................(3.2)

> BB*^T^* = S~yy~ -- S~yx~S~xx~^-1^S~xy~
> ...................................................(3.3)

where, S~xx~ is the generated sample variances and covariances of the
Autoscope classes, S~yy~ is the generated sample variances and
covariances of the 14 FHWA classes, and S~yx~ is the generated sample
variances and covariances between the Autoscope and FHWA classes. The
resulting BB*^T^* matrix is always a positive semidefinite and should be
decomposed to obtain matrix B (Valencia and Schaake, 1973). A
mathematical description of the disaggregation model, including a
detailed discussion of parameter estimation procedures and decomposition
of the BB*^T^* matrix is available in Rodriguez-Iturbe (1993). Since the
parameters for this calculation are determined from both Autoscope and
FHWA data, the model will have properties that preserve:

-   the mean of FHWA data,

-   the variance and covariance of elements of S~yy~,

-   the variances and covariances between Y and X, S~yx~, and

-   the cumulative relations of disaggregated FHWA values that add up to
    the aggregated Autoscope values.

After estimating the parameter matrices A and B, W matrix can be
randomly generated. Finally, using the X-values obtained from Autoscope
data, new set of Y-values can be estimated, which resemble the
historical data values of Y (14 FHWA classes) used to develop the model.
A flowchart representing the overview of the VCD model is shown in
Figure 3-4.

![](media/image4.jpeg){width="5.319444444444445in" height="2.925in"}

[]{#__RefHeading___Toc37055941 .anchor}Figure 3-. Flowchart representing
an overview of VCD model

### 

### 

### VCD model development

This section discusses the procedure used to develop the VCD model for
estimating the parameter matrices A and B. The model was developed using
weekday data on Interstate I-65 and can be retrained for other locations
and times. For training purposes, the 14 FHWA classes (Y's) were
obtained from axle counters and the aggregated values (X's) were
obtained from Autoscope. The VCD model was developed using two Autoscope
classes, where the first Autoscope class represented the aggregation of
the first three FHWA classes, and the second Autoscope class combind the
rest of the FHWA classes.

The data collected from the FHWA axle counters and Autoscope were
compiled and are presented in Table 3-5, wherein column headings Y1 to
Y14 are the 14 FHWA classes. Column headings X1 and X2 at the far right
of the table are Autoscope classes.

The rows numbered one through 28 contain the hourly volume in each
vehicle class. Below the 28 hours of data are the minimum and maximum
number (or range) of vehicles in each class used in the development of
the VCD model. The last two rows of the table are the calculated mean
number of vehicles and standard deviation in each of the FHWA and
Autoscope classes. The disaggregation model operates on aggregated
values, regardless of their origin. To evaluate the performance of the
VCD model, it was first trained and validated using only FHWA data.

[]{#__RefHeading___Toc37055682 .anchor}Table 3-. Compilation of FHWA
data and Autoscope data (28 datasets)

  ---------- ----------- ------------ ------------ ---------- ----------- ---------- ---------- ----------- ------------ ---------- ----------- ---------- ----------- ----------- ------------- ------------
  **S.No**   **Y1**      **Y2**       **Y3**       **Y4**     **Y5**      **Y6**     **Y7**     **Y8**      **Y9**       **Y10**    **Y11**     **Y12**    **Y13**     **Y14**     **X1**        **X2**

  **1**      7           560          146          9          27          4          2          2           105          2          0           1          0           7           672           163

  **2**      7           545          122          16         26          3          3          3           89           1          1           1          0           7           696           193

  **3**      8           680          125          5          28          8          1          1           119          2          3           1          0           19          743           200

  **4**      4           672          120          12         21          6          1          1           128          0          0           0          0           11          700           195

  **5**      6           642          135          5          15          4          1          3           128          0          0           0          0           5           764           182

  **6**      9           694          144          2          21          9          0          1           119          6          1           0          0           7           848           211

  **7**      11          737          148          2          18          5          0          2           99           4          1           1          0           8           891           161

  **8**      9           594          69           5          21          5          0          2           106          0          2           0          0           5           724           161

  **9**      8           624          114          7          21          8          0          3           156          5          1           0          0           6           731           220

  **10**     7           676          136          8          26          7          0          2           137          1          0           0          0           15          798           191

  **11**     7           713          128          4          22          5          0          2           146          3          0           0          0           6           811           206

  **12**     7           752          173          3          22          4          1          3           126          3          1           0          0           8           905           244

  **13**     8           785          169          5          15          1          0          3           109          1          3           0          0           7           944           181

  **14**     8           585          129          3          16          5          0          0           107          2          3           1          0           5           816           162

  **15**     15          950          182          10         28          6          0          2           109          5          2           0          0           11          1130          201

  **16**     11          1299         216          8          33          9          0          2           102          1          5           2          0           16          1454          214

  **17**     15          1471         236          7          37          5          0          6           119          1          3           2          0           14          1638          218

  **18**     10          1583         318          8          33          14         0          2           115          0          0           0          0           13          1824          216

  **19**     16          1550         268          5          23          3          0          0           111          1          1           0          0           15          1721          221

  **20**     7           1388         225          4          23          8          0          0           103          1          1           0          0           8           1634          193

  **21**     12          603          127          8          32          7          0          2           106          1          1           3          0           6           692           174

  **22**     7           664          190          13         38          10         0          1           113          6          0           2          0           13          798           198

  **23**     9           671          137          4          36          14         2          2           117          1          2           1          0           12          757           193

  **24**     4           707          133          12         29          3          1          3           150          4          2           0          0           14          781           220

  **25**     12          666          126          5          25          4          0          4           115          4          0           0          0           6           789           177

  **26**     10          719          140          2          22          5          1          2           116          4          1           0          1           9           847           192

  **27**     11          674          170          4          19          2          1          1           90           6          2           2          0           10          847           158

  **28**     9           673          144          3          15          7          0          0           101          2          1           1          0           10          829           142

  **Min**    4           545          69           2          15          1          0          0           89           0          0           0          0           5           672           142

  **Max**    16          1583         318          16         38          14         3          6           156          6          5           3          1           19          1824          244

  **Mean**   **9.29**    **853.57**   **163.89**   **6.14**   **24.71**   **6.39**   **0.43**   **2.00**    **117.57**   **2.50**   **1.46**    **0.68**   **0.07**    **10.11**   **996.86**    **193.46**

  **SD**     **3.005**   **316.97**   **52.39**    **3.63**   **6.705**   **3.14**   **0.79**   **1.319**   **16.478**   **1.97**   **1.249**   **0.87**   **0.189**   **3.893**   **347.429**   **24.102**
  ---------- ----------- ------------ ------------ ---------- ----------- ---------- ---------- ----------- ------------ ---------- ----------- ---------- ----------- ----------- ------------- ------------

#### Training and validating the model using only FHWA data: For this purpose, instead of using Autoscope data, the values of X1 and X2 were obtained by summing the FHWA classes one to three and FHWA classes four to 14. This was performed to comply with the underlying assumption of the cumulative relation. The eight-step procedure explained below was followed to train and validate the VCD model. 

The disaggregation process was developed and can be verified with the
following steps:

1.  To develop the model, the data is transformed to have a zero mean
    using the following equations 3.4 and 3.5.

    Y~ijt~ = Y~ij~ -- **Y^'^**~j~
    ........................................................(3.4)

    X~ikt~ = X~ik~ -- **X^'^**~k~
    .......................................................(3.5)

    where i = 1 to 28 and is the hourly datasets, j = 1 to 14 and is the
    FHWA classes, k = 1 and 2 and is the aggregated values from FHWA
    data representing Autoscope data, t is the transformed data,
    **Y^'^**~j~ is the mean number of vehicles in each of the FHWA
    classes, and **X^'^**~k~ is the average number of vehicles in each
    of the aggregated FHWA classes.

```{=html}
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```
16. The transformed data was used to compute the basic statistics of
    each column, which included mean, standard deviation, variance, and
    covariance.

17. All variances and covariances were determined to construct the
    S~xx~, S~yy~, and S~yx~ matrices, which were stored and used to
    obtain the parameter matrices A and B. It should be noted that the
    matrices remained constant throughout the model development process.

18. A MATLAB program was designed to perform the matrix operations
    associated with equations 3.2 and 3.3 to determine the parameter
    matrices A and B.

19. The MATLAB program used the calculated parameter matrices A, B, a
    dataset of transformed X-values, and a generated normally
    distributed random number array (W matrix) to perform the
    calculations associated with equation 3.1 to produce a set of
    Y-values.

20. To minimize bias in the VCD model due to the randomness of the W
    matrix, the model was run 10,000 times for one dataset of
    transformed X-values by generating the W matrix 10,000 times. This
    lead to the generation of a set of fourteen Y-values that were the
    averages over each 10,000 runs.

21. The generated Y-values (average over 10,000 runs) were
    re-transformed by adding the actual means, **Y^'^**~j~ to obtain a
    new set of final Y-values.

22. Likewise, the remaining datasets of transformed X-values were used
    to generate a total of 28 new sets of final Y-values. Statistics of
    these 28 new datasets were obtained and compared with the statistics
    of the input data to evaluate the performance of the model. If the
    VCD model output exhibited similar statistical properties, it was
    assumed to be viable.

The VCD model output is shown in Table 3-6 where the first 28 rows are
the generated Y-values, and the next two rows are the mean number of
vehicles and standard deviation observed in each of the 14 FHWA classes.
The differences between actual and generated values are shown in Table
3-7. It can be seen in Table 3-7 that the average of the differences for
all 28 sets for each of the 14 FHWA classes is almost zero, indicating a
negligible average difference between the true and predicted values. To
determine the proximity of VCD model generated values to the true
values, confidence intervals were estimated for the generated data. The
procedure adopted for determining the confidence intervals is described
in the following section.

[]{#__RefHeading___Toc37055683 .anchor}Table 3-. Output of VCD model (28
datasets)

  ---------- ---------- ------------ ------------ ---------- ----------- ---------- ---------- ---------- ------------ ---------- ---------- ---------- ---------- ----------
  **S. No**  **Y1**     **Y2**       **Y3**       **Y4**     **Y5**      **Y6**     **Y7**     **Y8**     **Y9**       **Y10**    **Y11**    **Y12**    **Y13**    **Y14**

  **1**      8.41       542.70       117.72       5.65       20.95       5.15       0.63       1.52       107.34       2.46       1.14       0.90       0.04       7.52

  **2**      7.73       587.43       124.32       6.95       24.15       5.73       0.78       2.13       121.71       2.93       1.12       0.59       0.05       8.97

  **3**      7.73       634.26       131.29       7.17       24.91       5.92       0.75       2.25       124.50       2.93       1.14       0.50       0.05       9.40

  **4**      7.57       592.53       124.90       6.99       24.21       5.79       0.78       2.13       122.85       2.94       1.10       0.54       0.05       9.02

  **5**      8.35       638.63       132.17       6.32       23.14       5.64       0.63       1.87       114.94       2.62       1.18       0.71       0.05       8.65

  **6**      7.97       735.74       146.96       7.45       26.31       6.26       0.71       2.41       127.64       2.91       1.19       0.42       0.05       10.22

  **7**      9.60       734.57       147.68       5.16       21.45       5.47       0.42       1.38       101.38       1.99       1.32       0.98       0.04       8.14

  **8**      8.72       587.05       124.62       5.53       20.95       5.20       0.57       1.46       105.31       2.31       1.19       0.93       0.04       7.61

  **9**      7.09       639.46       131.59       8.00       26.86       6.27       0.87       2.65       134.82       3.34       1.08       0.27       0.06       10.25

  **10**     8.26       676.62       137.98       6.71       24.21       5.83       0.67       2.03       118.47       2.68       1.20       0.62       0.04       9.28

  **11**     7.87       699.05       141.54       7.23       25.59       6.11       0.72       2.30       125.96       2.92       1.16       0.45       0.05       9.90

  **12**     7.31       811.91       158.18       8.72       29.70       6.92       0.83       3.02       142.73       3.39       1.20       0.08       0.05       11.87

  **13**     9.30       796.76       156.86       5.86       23.53       5.94       0.44       1.71       110.52       2.25       1.33       0.75       0.04       9.23

  **14**     9.18       668.51       137.72       5.40       21.35       5.41       0.47       1.42       103.64       2.18       1.24       0.95       0.04       7.93

  **15**     9.78       976.80       183.71       6.41       26.04       6.53       0.37       2.04       116.21       2.19       1.46       0.59       0.03       10.72

  **16**     11.11      1273.00      229.00       6.33       28.16       7.22       0.10       2.11       115.37       1.75       1.69       0.51       0.02       12.28

  **17**     11.95      1439.00      254.69       6.20       29.18       7.62       -0.07      2.07       113.36       1.45       1.85       0.51       0.02       13.14

  **18**     13.01      1601.90      279.83       5.73       29.50       7.89       -0.27      1.92       108.02       1.03       2.02       0.60       0.01       13.66

  **19**     12.31      1514.90      266.57       6.09       29.76       7.83       -0.14      2.09       113.12       1.34       1.91       0.52       0.02       13.55

  **20**     12.65      1415.70      251.81       5.12       26.66       7.22       -0.20      1.55       100.80       1.07       1.89       0.80       0.01       12.07

  **21**     8.18       568.72       121.68       6.10       22.20       5.40       0.67       1.76       112.47       2.58       1.15       0.78       0.05       8.04

  **22**     8.06       681.15       138.66       6.96       24.87       5.94       0.71       2.16       122.03       2.81       1.19       0.55       0.05       9.51

  **23**     8.02       641.42       132.64       6.79       24.24       5.77       0.72       2.11       120.27       2.80       1.18       0.60       0.05       9.17

  **24**     7.33       683.90       138.27       7.90       27.04       6.33       0.83       2.62       133.64       3.22       1.14       0.30       0.06       10.43

  **25**     8.65       657.02       135.33       6.03       22.63       5.58       0.58       1.72       111.69       2.45       1.20       0.77       0.04       8.51

  **26**     8.52       719.71       144.79       6.57       24.46       5.97       0.60       2.02       118.13       2.62       1.24       0.62       0.04       9.39

  **27**     9.48       692.46       141.14       5.11       20.91       5.34       0.42       1.32       100.76       2.05       1.28       1.00       0.04       7.76

  **28**     9.85       664.15       136.86       4.45       19.24       4.98       0.35       1.01       93.26        1.81       1.30       1.16       0.03       7.07

  **Mean**   **9.07**   **816.97**   **159.59**   **6.39**   **24.72**   **6.12**   **0.50**   **1.96**   **115.75**   **2.39**   **1.32**   **0.64**   **0.04**   **9.76**

  **SD**     **1.68**   **315.17**   **48.27**    **0.98**   **2.95**    **0.81**   **0.33**   **0.45**   **11.40**    **0.65**   **0.28**   **0.24**   **0.01**   **1.87**
  ---------- ---------- ------------ ------------ ---------- ----------- ---------- ---------- ---------- ------------ ---------- ---------- ---------- ---------- ----------

[]{#__RefHeading___Toc37055684 .anchor}Table 3-. Summary of differences
between FHWA data and Autoscope data (28 datasets)

  ---------- ---------- ----------- ----------- ---------- ----------- ----------- ---------- ---------- ----------- ---------- ---------- ---------- ---------- -----------
  **S. No**  **Y1**     **Y2**      **Y3**      **Y4**     **Y5**      **Y6**      **Y7**     **Y8**     **Y9**      **Y10**    **Y11**    **Y12**    **Y13**    **Y14**

  **1**      -1.41      17.30       28.28       3.35       6.05        -1.15       1.37       0.48       -2.34       -0.46      -1.14      0.10       -0.04      -0.52

  **2**      -0.73      -42.43      -2.32       9.05       1.85        -2.73       2.22       0.87       -32.71      -1.93      -0.12      0.41       -0.05      -1.97

  **3**      0.27       45.74       -6.29       -2.17      3.09        2.08        0.25       -1.25      -5.50       -0.93      1.86       0.50       -0.05      9.60

  **4**      -3.57      79.47       -4.90       5.01       -3.21       0.21        0.22       -1.13      5.15        -2.94      -1.10      -0.54      -0.05      1.98

  **5**      -2.35      3.37        2.83        -1.32      -8.14       -1.64       0.37       1.13       13.06       -2.62      -1.18      -0.71      -0.05      -3.65

  **6**      1.03       -41.74      -2.96       -5.45      -5.31       2.74        -0.71      -1.41      -8.64       3.09       -0.19      -0.42      -0.05      -3.22

  **7**      1.40       2.43        0.32        -3.16      -3.45       -0.47       -0.42      0.62       -2.38       2.01       -0.32      0.02       -0.04      -0.14

  **8**      0.28       6.95        -55.62      -0.53      0.05        -0.20       -0.57      0.54       0.69        -2.31      0.81       -0.93      -0.04      -2.61

  **9**      0.91       -15.46      -17.59      -1.00      -5.86       1.73        -0.87      0.35       21.18       1.66       -0.08      -0.27      -0.06      -4.25

  **10**     -1.26      -0.62       -1.98       1.29       1.79        1.17        -0.67      -0.03      18.53       -1.68      -1.20      -0.62      -0.04      5.72

  **11**     -0.87      13.95       -13.54      -3.23      -3.59       -1.11       -0.72      -0.30      20.04       0.08       -1.16      -0.45      -0.05      -3.90

  **12**     -0.31      -59.91      14.82       -5.72      -7.70       -2.92       0.17       -0.02      -16.73      -0.39      -0.20      -0.08      -0.05      -3.87

  **13**     -1.30      -11.76      12.14       -0.86      -8.53       -4.94       -0.44      1.29       -1.52       -1.25      1.67       -0.75      -0.04      -2.23

  **14**     -1.18      -83.51      -8.72       -2.40      -5.35       -0.41       -0.47      -1.42      3.36        -0.18      1.76       0.05       -0.04      -2.93

  **15**     5.22       -26.80      -1.71       3.59       1.96        -0.53       -0.37      -0.04      -7.21       2.81       0.54       -0.59      -0.03      0.28

  **16**     -0.11      26.00       -13.00      1.67       4.84        1.78        -0.10      -0.11      -13.37      -0.75      3.31       1.49       -0.02      3.72

  **17**     3.05       32.00       -18.69      0.80       7.83        -2.62       0.07       3.93       5.64        -0.45      1.15       1.49       -0.02      0.86

  **18**     -3.01      -18.90      38.17       2.27       3.50        6.11        0.27       0.08       6.98        -1.03      -2.02      -0.60      -0.01      -0.66

  **19**     3.69       35.10       1.43        -1.09      -6.76       -4.83       0.14       -2.09      -2.12       -0.34      -0.91      -0.52      -0.02      1.45

  **20**     -5.65      -27.70      -26.81      -1.12      -3.66       0.78        0.20       -1.55      2.20        -0.07      -0.89      -0.80      -0.01      -4.07

  **21**     3.82       34.28       5.32        1.90       9.80        1.60        -0.67      0.24       -6.47       -1.58      -0.15      2.22       -0.05      -2.04

  **22**     -1.06      -17.15      51.34       6.04       13.13       4.06        -0.71      -1.16      -9.03       3.19       -1.19      1.45       -0.05      3.49

  **23**     0.98       29.58       4.36        -2.79      11.76       8.23        1.28       -0.11      -3.27       -1.80      0.82       0.40       -0.05      2.84

  **24**     -3.33      23.10       -5.27       4.10       1.97        -3.33       0.17       0.38       16.36       0.78       0.86       -0.30      -0.06      3.57

  **25**     3.35       8.98        -9.33       -1.03      2.37        -1.58       -0.58      2.28       3.31        1.55       -1.20      -0.77      -0.04      -2.51

  **26**     1.48       -0.71       -4.79       -4.57      -2.46       -0.97       0.40       -0.02      -2.13       1.38       -0.24      -0.62      0.96       -0.39

  **27**     1.52       -18.46      28.86       -1.11      -1.91       -3.34       0.58       -0.32      -10.76      3.95       0.72       1.00       -0.04      2.24

  **28**     -0.85      8.85        7.14        -1.45      -4.24       2.02        -0.35      -1.01      7.74        0.19       -0.30      -0.16      -0.03      2.93

  **Mean**   **0.00**   **0.07**    **0.05**    **0.00**   **-0.01**   **-0.01**   **0.00**   **0.01**   **0.00**    **0.00**   **0.00**   **0.00**   **0.00**   **-0.01**

  **SDD**    **2.48**   **34.21**   **20.48**   **3.50**   **6.03**    **3.04**    **0.72**   **1.24**   **11.84**   **1.85**   **1.22**   **0.83**   **0.19**   **3.42**
  ---------- ---------- ----------- ----------- ---------- ----------- ----------- ---------- ---------- ----------- ---------- ---------- ---------- ---------- -----------

#### Training and verifying the model using Autoscope data: After training the VCD model developed from FHWA data, the aggregated X1 and X2 values were replaced with actual Autoscope X-values. The purpose of verification was to evaluate the statistical properties of the generated data and to compare those properties with similar properties of the input data. To verify and quantify the accuracy of the VCD model, three datasets were randomly selected for testing purposes, which were excluded from the 28 datasets. The model was first trained with 25 datasets. Table 3-8 shows the compiled 25 sets of both FHWA and Autoscope data. The calculated mean number of cars and the standard deviation for each class (designated by each column) is shown in the last two rows. The eight-step procedure discussed above was repeated and the VCD model generated a new series of 25 datasets. 

[]{#__RefHeading___Toc37055685 .anchor}Table 3-. Compilation of FHWA
data and Autoscope data (25 datasets)

  ---------- ---------- ------------ ------------ ---------- ----------- ---------- ---------- ---------- ------------ ---------- ---------- ---------- ---------- ---------- ------------ ------------
  **S. No**  **Y1**     **Y2**       **Y3**       **Y4**     **Y5**      **Y6**     **Y7**     **Y8**     **Y9**       **Y10**    **Y11**    **Y12**    **Y13**    **Y14**    **X1**       **X2**

  **1**      7          545          122          16         26          3          3          3          89           1          1          1          0          7          696          193

  **2**      8          680          125          5          28          8          1          1          119          2          3          1          0          19         743          200

  **3**      4          672          120          12         21          6          1          1          128          0          0          0          0          11         700          195

  **4**      6          642          135          5          15          4          1          3          128          0          0          0          0          5          764          182

  **5**      9          694          144          2          21          9          0          1          119          6          1          0          0          7          848          211

  **6**      11         737          148          2          18          5          0          2          99           4          1          1          0          8          891          161

  **7**      9          594          69           5          21          5          0          2          106          0          2          0          0          5          724          161

  **8**      7          676          136          8          26          7          0          2          137          1          0          0          0          15         798          191

  **9**      7          713          128          4          22          5          0          2          146          3          0          0          0          6          811          206

  **10**     7          752          173          3          22          4          1          3          126          3          1          0          0          8          905          244

  **11**     8          785          169          5          15          1          0          3          109          1          3          0          0          7          944          181

  **12**     8          585          129          3          16          5          0          0          107          2          3          1          0          5          816          162

  **13**     11         1299         216          8          33          9          0          2          102          1          5          2          0          16         1454         214

  **14**     15         1471         236          7          37          5          0          6          119          1          3          2          0          14         1638         218

  **15**     10         1583         318          8          33          14         0          2          115          0          0          0          0          13         1824         216

  **16**     16         1550         268          5          23          3          0          0          111          1          1          0          0          15         1721         221

  **17**     7          1388         225          4          23          8          0          0          103          1          1          0          0          8          1634         193

  **18**     12         603          127          8          32          7          0          2          106          1          1          3          0          6          692          174

  **19**     7          664          190          13         38          10         0          1          113          6          0          2          0          13         798          198

  **20**     9          671          137          4          36          14         2          2          117          1          2          1          0          12         757          193

  **21**     4          707          133          12         29          3          1          3          150          4          2          0          0          14         781          220

  **22**     12         666          126          5          25          4          0          4          115          4          0          0          0          6          789          177

  **23**     10         719          140          2          22          5          1          2          116          4          1          0          1          9          847          192

  **24**     11         674          170          4          19          2          1          1          90           6          2          2          0          10         847          158

  **25**     9          673          144          3          15          7          0          0          101          2          1          1          0          10         829          142

  **Mean**   **8.96**   **829.72**   **161.12**   **6.12**   **24.64**   **6.12**   **0.48**   **1.92**   **114.84**   **2.20**   **1.36**   **0.68**   **0.04**   **9.96**   **970.04**   **192.12**

  **SD**     **2.91**   **328.36**   **54.50**    **3.73**   **7.02**    **3.28**   **0.77**   **1.38**   **15.18**    **1.94**   **1.29**   **0.90**   **0.20**   **4.00**   **358.97**   **24.14**
  ---------- ---------- ------------ ------------ ---------- ----------- ---------- ---------- ---------- ------------ ---------- ---------- ---------- ---------- ---------- ------------ ------------

## 

The generated data sets, calculated means, and standard deviations for
each class are shown in Table 3-9.

[]{#__RefHeading___Toc37055686 .anchor}Table 3-. Output of VCD model (25
datasets)

  ---------- ----------- ------------- ------------- ----------- ------------ ----------- ----------- ----------- ------------- ----------- ----------- ----------- ----------- -----------
  **S.No**   **Y1**      **Y2**        **Y3**        **Y4**      **Y5**       **Y6**      **Y7**      **Y8**      **Y9**        **Y10**     **Y11**     **Y12**     **Y13**     **Y14**

  **1**      7.522       588.800       124.820       6.786       24.214       5.585       0.791       2.066       120.260       2.748       1.149       0.630       0.050       9.321

  **2**      7.446       636.330       132.460       7.041       25.100       5.768       0.818       2.155       122.430       2.682       1.152       0.552       0.054       9.803

  **3**      7.455       594.220       125.570       6.868       24.504       5.660       0.808       2.100       121.150       2.732       1.155       0.599       0.052       9.461

  **4**      8.286       638.840       131.090       5.986       22.881       5.623       0.592       1.819       114.300       2.497       1.231       0.726       0.049       8.832

  **5**      7.597       738.670       148.760       7.377       26.882       6.173       0.815       2.339       125.550       2.620       1.181       0.463       0.053       10.720

  **6**      9.666       732.690       144.320       4.770       20.570       5.670       0.237       1.340       102.640       2.039       1.414       0.977       0.034       8.044

  **7**      8.847       585.370       121.610       5.105       20.180       5.288       0.419       1.433       105.630       2.341       1.294       0.938       0.044       7.593

  **8**      8.141       677.070       137.990       6.402       24.098       5.766       0.655       1.973       117.640       2.522       1.220       0.643       0.045       9.486

  **9**      7.578       701.540       142.860       7.180       26.134       6.045       0.806       2.259       123.880       2.626       1.198       0.506       0.049       10.381

  **10**     6.735       817.870       163.500       8.889       31.114       6.535       1.112       2.982       138.770       2.835       1.119       0.155       0.061       12.741

  **11**     9.273       797.050       155.170       5.560       23.111       5.934       0.380       1.715       110.560       2.149       1.372       0.776       0.039       9.245

  **12**     9.213       667.360       134.040       4.924       20.461       5.514       0.320       1.394       104.550       2.195       1.342       0.935       0.040       7.838

  **13**     10.691      1275.800      230.830       6.154       28.325       7.244       0.177       2.103       115.350       1.522       1.633       0.568       0.023       12.443

  **14**     11.500      1441.800      256.310       6.017       29.216       7.560       0.026       2.092       113.590       1.195       1.769       0.575       0.013       13.121

  **15**     12.550      1603.400      280.530       5.465       29.336       7.907       -0.197      1.950       108.960       0.862       1.940       0.641       0.006       13.489

  **16**     11.800      1517.800      267.570       5.985       29.817       7.805       -0.040      2.101       113.540       1.076       1.828       0.560       0.012       13.513

  **17**     12.407      1416.600      250.000       4.750       26.111       7.344       -0.234      1.622       102.680       0.963       1.874       0.811       0.011       11.694

  **18**     8.093       568.740       120.050       5.819       21.694       5.396       0.584       1.697       112.430       2.527       1.220       0.779       0.043       8.262

  **19**     7.840       683.510       139.030       6.741       24.950       5.851       0.735       2.129       120.800       2.547       1.221       0.572       0.050       9.849

  **20**     7.841       642.690       133.070       6.648       24.339       5.714       0.730       2.040       119.180       2.612       1.193       0.632       0.054       9.524

  **21**     6.998       687.670       141.890       7.961       27.939       6.098       0.993       2.570       130.510       2.839       1.125       0.386       0.058       11.080

  **22**     8.591       656.670       133.760       5.685       22.291       5.616       0.514       1.710       111.630       2.401       1.291       0.787       0.043       8.656

  **23**     8.329       720.780       144.510       6.361       24.353       5.904       0.623       1.989       116.970       2.431       1.276       0.649       0.047       9.627

  **24**     9.524       690.930       137.310       4.646       19.906       5.475       0.237       1.311       102.380       2.112       1.394       0.964       0.039       7.657

  **25**     10.022      661.510       131.600       3.856       17.895       5.304       0.106       1.007       95.617        1.936       1.467       1.132       0.034       6.837

  **Mean**   **8.958**   **829.748**   **161.146**   **6.119**   **24.617**   **6.111**   **0.480**   **1.916**   **114.840**   **2.200**   **1.362**   **0.678**   **0.040**   **9.969**

  **SD**     **1.690**   **326.354**   **50.476**    **1.135**   **3.464**    **0.803**   **0.367**   **0.428**   **9.775**     **0.607**   **0.250**   **0.212**   **0.016**   **1.941**
  ---------- ----------- ------------- ------------- ----------- ------------ ----------- ----------- ----------- ------------- ----------- ----------- ----------- ----------- -----------

The differences between the actual and generated series of datasets are
shown in Table 3-10. The values in this table indicate the number of
cars in each class that were under-or-over estimated by the VCD model
using only Autoscope data, when compared to the actual count obtained by
the FHWA axle counters. Although the cumulative relation did not
strictly hold true, the model produced 25 values for each class whose
average was approximately zero. The last row of Table 3-10 indicates the
standard deviation values of the differences (SDD). If the means of the
differences (second to last row in Table 3-10) are non-zero whole
numbers, then the differences are considered to be significant,
indicating either inapplicability of the model, programming errors, or
both. This was not the case.

[]{#__RefHeading___Toc37055687 .anchor}Table 3-. Summary of differences
between FHWA data and Autoscope data (25 datasets)

## 

## 

## 

## 

## 

## 

## 

## 

## 

## 

## 

## Results

## 

## To test the VCD model performance, three datasets not included in training the model were run through the VCD model. The first Autoscope dataset generated a set of 14 FHWA values, which were compared with the actual set of 14 FHWA values to determine the accuracy of the generated values. To report the accuracy of the VCD model output, confidence intervals were estimated. For the purpose of research, 95% and 99% confidence intervals were estimated from the following expressions:

95% confidence Interval: mean value from VCD model ± 1.96 ×
(SDD)..................(3.6)

99% confidence Interval: mean value from VCD model ± 2.575 ×
(SDD).................(3.7)

SDDs are the conditioned standard deviation values obtained for each
class in Table 3-10. This process was employed on all three datasets and
a summary of the confidence intervals for these sets is tabulated in
Tables 3-11, 3-12, and 3-13. The second and third columns in these
tables represent the actual FHWA data and VCD model generated data,
respectively. Lower and upper 95% confidence limit values are shown in
fourth and fifth columns, followed by 99% confidence limit values in
sixth and seventh column. As is expected, the three sets that the 95%
confidence interval obtained using the VCD model output included 95% of
actual FHWA classes. Outliers are in bold. The confidence interval
obtained with 99% confidence contained all the actual FHWA classes.

[]{#__RefHeading___Toc37055688 .anchor}Table 3-. Testing dataset 1, 95%
and 99% confidence interval for the VCD model output

+----+----------+----------+---------+---------+----------+----------+
|    | **       | **VCD    | **95%   |         | **99%    |          |
|    | Actual** | model**  | Con     |         | Co       |          |
|    |          |          | fidence |         | nfidence |          |
|    |          |          | Int     |         | In       |          |
|    |          |          | erval** |         | terval** |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | **FHWA   | **Mean   | **Lower | **Upper | **Lower  | **Upper  |
| S. | values** | Output** | C.L**   | C.L**   | C.L**    | C.L**    |
| No |          |          |         |         |          |          |
| ** | **(2)**  | **(3)**  | **(4)** | **(5)** | **(6)**  | **(7)**  |
|    |          |          |         |         |          |          |
| *  |          |          |         |         |          |          |
| *( |          |          |         |         |          |          |
| 1) |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 7        | 8.46     | 3.79    | 13.12   | 2.33     | 14.59    |
| *1 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 560      | 541.50   | 471.89  | 611.11  | 450.02   | 632.98   |
| *2 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 146      | 115.28   | 74.97   | 155.59  | 62.31    | 168.25   |
| *3 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 9        | 5.30     | -1.69   | 12.28   | -3.88    | 14.47    |
| *4 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 27       | 20.29    | 8.26    | 32.33   | 4.48     | 36.11    |
| *5 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 4        | 5.27     | -0.97   | 11.51   | -2.93    | 13.47    |
| *6 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | ***2***  | **       | ***-    | ***     | -1.24    | 2.24     |
| *7 |          | *0.50*** | 0.83*** | 1.82*** |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 2        | 1.48     | -1.09   | 4.05    | -1.90    | 4.86     |
| *8 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 105      | 107.70   | 84.90   | 130.50  | 77.74    | 137.66   |
| *9 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 2        | 2.49     | -1.12   | 6.09    | -2.25    | 7.23     |
| 10 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 0        | 1.22     | -1.27   | 3.70    | -2.05    | 4.49     |
| 11 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 1        | 0.89     | -0.83   | 2.61    | -1.37    | 3.15     |
| 12 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 0        | 0.04     | -0.35   | 0.43    | -0.47    | 0.56     |
| 13 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 7        | 7.56     | 0.69    | 14.43   | -1.47    | 16.59    |
| 14 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+

[]{#__RefHeading___Toc37055689 .anchor}Table 3-. Testing dataset 2, 95%
and 99% confidence interval for the VCD model output

+----+----------+----------+----------+----------+---------+---------+
|    | **       | **VCD    | **95%    |          | **99%   |         |
|    | Actual** | model**  | Co       |          | Con     |         |
|    |          |          | nfidence |          | fidence |         |
|    |          |          | In       |          | Int     |         |
|    |          |          | terval** |          | erval** |         |
+----+----------+----------+----------+----------+---------+---------+
| ** | **FHWA   | **Mean   | **Lower  | **Upper  | **Lower | **Upper |
| S. | values** | Output** | C.L**    | C.L**    | C.L**   | C.L**   |
| No |          |          |          |          |         |         |
| ** | **(2)**  | **(3)**  | **(4)**  | **(5)**  | **(6)** | **(7)** |
|    |          |          |          |          |         |         |
| *  |          |          |          |          |         |         |
| *( |          |          |          |          |         |         |
| 1) |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| *  | 8        | 6.72     | 2.056    | 11.39    | 0.59    | 12.85   |
| *1 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| *  | 624      | 643.44   | 573.830  | 713.05   | 551.96  | 734.92  |
| *2 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| *  | 114      | 134.87   | 94.561   | 175.18   | 81.90   | 187.84  |
| *3 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| *  | 7        | 8.04     | 1.060    | 15.02    | -1.13   | 17.22   |
| *4 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| *  | 21       | 27.73    | 15.693   | 39.77    | 11.91   | 43.55   |
| *5 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| *  | 8        | 5.96     | -0.281   | 12.20    | -2.24   | 14.16   |
| *6 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| *  | 0        | 1.03     | -0.291   | 2.36     | -0.71   | 2.78    |
| *7 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| *  | 3        | 2.59     | 0.017    | 5.16     | -0.79   | 5.96    |
| *8 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| *  | *        | ***1     | ***10    | ***1     | 101.49  | 161.41  |
| *9 | **156*** | 31.45*** | 8.652*** | 54.25*** |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| ** | 5        | 2.95     | -0.656   | 6.56     | -1.79   | 7.69    |
| 10 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| ** | 1        | 1.09     | -1.398   | 3.58     | -2.18   | 4.36    |
| 11 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| ** | 0        | 0.36     | -1.361   | 2.08     | -1.90   | 2.62    |
| 12 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| ** | 0        | 0.06     | -0.326   | 0.45     | -0.45   | 0.58    |
| 13 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+
| ** | 6        | 10.97    | 4.098    | 17.84    | 1.94    | 20.00   |
| 14 |          |          |          |          |         |         |
| ** |          |          |          |          |         |         |
+----+----------+----------+----------+----------+---------+---------+

[]{#__RefHeading___Toc37055690 .anchor}Table 3-. Testing dataset 3, 95%
and 99% confidence interval for the VCD model output

+----+----------+----------+---------+---------+----------+----------+
|    | **       | **VCD    | **95%   |         | **99%    |          |
|    | Actual** | model**  | Con     |         | Co       |          |
|    |          |          | fidence |         | nfidence |          |
|    |          |          | Int     |         | In       |          |
|    |          |          | erval** |         | terval** |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | **FHWA   | **Mean   | **Lower | **Upper | **Lower  | **Upper  |
| S. | values** | Output** | C.L**   | C.L**   | C.L**    | C.L**    |
| No |          |          |         |         |          |          |
| ** | **(2)**  | **(3)**  | **(4)** | **(5)** | **(6)**  | **(7)**  |
|    |          |          |         |         |          |          |
| *  |          |          |         |         |          |          |
| *( |          |          |         |         |          |          |
| 1) |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | ***15*** | **       | ***     | ***1    | 3.36     | 15.62    |
| *1 |          | *9.49*** | 4.83*** | 4.16*** |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 950      | 978.49   | 908.88  | 1048.10 | 887.01   | 1069.97  |
| *2 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 182      | 184.31   | 144.00  | 224.62  | 131.34   | 237.28   |
| *3 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 10       | 6.28     | -0.70   | 13.26   | -2.90    | 15.45    |
| *4 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 28       | 26.17    | 14.13   | 38.20   | 10.35    | 41.99    |
| *5 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 6        | 6.49     | 0.25    | 12.73   | -1.71    | 14.69    |
| *6 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 0        | 0.41     | -0.91   | 1.74    | -1.33    | 2.15     |
| *7 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 2        | 2.02     | -0.55   | 4.58    | -1.36    | 5.39     |
| *8 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| *  | 109      | 115.45   | 92.65   | 138.25  | 85.49    | 145.41   |
| *9 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 5        | 2.01     | -1.60   | 5.62    | -2.73    | 6.75     |
| 10 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 2        | 1.45     | -1.03   | 3.94    | -1.82    | 4.72     |
| 11 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 0        | 0.64     | -1.08   | 2.36    | -1.62    | 2.90     |
| 12 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 0        | 0.04     | -0.35   | 0.43    | -0.48    | 0.55     |
| 13 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+
| ** | 11       | 10.89    | 4.02    | 17.75   | 1.86     | 19.91    |
| 14 |          |          |         |         |          |          |
| ** |          |          |         |         |          |          |
+----+----------+----------+---------+---------+----------+----------+

## Conclusions and Recommendations

During the Vehicle Classification Disaggregation (VCD) model
development, training, and validation, video and axle data were
employed. With a trained VCD model and using only Autoscope data, 10,000
runs were made to produce an output distribution for the 14 FHWA
classes. A summary of FHWA axle counter data and Autoscope data is shown
in Table 3-5, and the results of the disaggregation model are presented
in Table 3-6. A comparison was performed on these two tables and the
average deviation of the difference between the FHWA axle count data and
the VCD model output is provided in Table 3-7. It can be seen from the
tables that for most of the classes, the generated values maintained
statistical properties similar to the actual data, and the mean of the
differences is close to zero indicating a negligible average difference
between the actual and VCD model output data.

The VCD model was verified using three datasets. A comparison was
performed between the actual data and the VCD generated classes for the
three datasets. Confidence intervals at 95% and 99% were calculated for
the generated values, and are presented in Tables 3-11, 3-12, and 3-13.

Based on a review of various methodologies used for vehicle
classification, and the findings from the vehicle disaggregation
analyses, the following conclusions and recommendations are outlined for
using video technology for compositional traffic counts.

-   Initial installation costs of employing video for compositional
    traffic data collection is approximately \$11,000.

-   Video is inexpensive, portable, and can be installed on a bridge
    railing or on a side pole in approximately 15 minutes without
    disrupting traffic. Maintenance costs of video equipment are low.

-   Any number of video "virtual" loops can be placed on the target
    roadway. This translates into less cost when compared to inductive
    loop detectors.

-   Video technology offers simple monitoring of multilane freeways that
    produces no disruption of traffic, and better safety for data
    collection staff.

-   Autoscope was inefficient in grouping long vehicles. One possible
    solution would be to mount the camera higher (more than 30 feet
    above the target roadway).

-   The current VCD model produces accurate results at the site where it
    was trained.

-   To further analyze the VCD model, the performance on weekends, peak
    and off-peak periods, and holidays must be evaluated. This will
    allow the development of specific models for different time periods.

-   Further, to analyze the applicability of VCD model, its performance
    must be evaluated at different locations.

Section 4

# 15^th^ Street Corridor Modeling

## Introduction

Video technology can be used to obtain information about traffic flow.
Parameters such as volume, speed, headway, and queue length can be
determined through careful analysis of recorded video. Using video
cameras installed and operated by the City of Tuscaloosa and portable
University of Alabama video units, video recording was performed on
intersections along the 15^th^ Street corridor in Tuscaloosa. Traffic
flow parameters were determined from the video recording, and were used
to develop a traffic flow model using an industrial engineering
simulation software package called ARENA developed by Rockwell Software
Inc. (ARENA User Manual, 1999).

Output volume results from the new ARENA model were compared to the
actual field data, and ARENA delay values were compared with Institute
of Transportation Engineers (ITE) intersection field delay values,
Highway Capacity Manual (HCM) 1994, and Highway Capacity Software (HCS)
2000 calculations. Input parameters for the ARENA model and the other
methodologies were obtained from video data. Information on traffic
flow, signalization, and intersection geometry were provided by the
Tuscaloosa Department of Transportation (TDOT).

The objective of this task was to determine if video could be used to
collect the required traffic information to calculate volume and delay
by different methodologies, one of which, the ARENA model, was developed
during this work. Although multiple methods were compared, two
methods 1) the new ARENA simulation model based on the arrival
distribution, and 2) the ITE intersection formula, were compared in
detail.

## Background

For this work it was assumed that traffic simulation is analogous to
manufacturing. The manufacturing process involves the arrival of raw
material at a machine, processing by the machine, and departure as a
finished product. A similar methodology was employed to calculate the
efficiency of signalized intersections using ARENA. In this process, a
vehicle arrives at an intersection, experiences delay due to signal
operations, and departs from the intersection as a turn movement or
through movement.

Existing intersection traffic models use traffic volumes as input, and
do not consider the actual arrival distribution of traffic at an
intersection. Two of these models, HCM 1994 and HCS 2000 were
investigated. Although these methods consider arrival patterns, the
actual arrival

distributions are not included as part of the software. This was not as
desirable as software that allowed designation of the distributions.

ARENA was selected for simulating signalized intersections based on the
following factors:

1.  ARENA uses data such as arrival distributions, turn volumes, and
    signalization phase plan to predict volume and intersection stopped
    delay associated with a signalized intersection,

2.  ARENA provides visualization capabilities, and

3.  the output format can be customized to obtain parameters such as
    volume, queue length, stopped delay, and process delay.

## 

## 

## Methodology

### Introduction

Video cameras were used to collect field data for this study. A
simulation model was developed for one intersection and applied to three
additional intersections along the same corridor. The research approach
employed in this work is shown in Figure 4-1. Column 1 in Figure 4-1
shows the initial step, video data collection. Input parameters for the
ARENA model and ITE method were identified and collected as part of this
step. Video technology was used to collect traffic data during morning
and evening peak hours.

> ![](media/image5.jpeg){width="6.4in" height="1.9166666666666667in"}

[]{#__RefHeading___Toc37055952 .anchor}Figure 4-. Schematic
representation of the study approach

As shown in column 2 of Figure 4-1, the recorded videotapes were cleaned
and analyzed to determine the input parameters for the methods. Column 3
of Figure 4-1 shows the computation portion of this work, which is
divided into two parts: ARENA simulation modeling and operational
analysis. Finally, as shown in column 4 of Figure 4-1, ARENA volume and
delay results were analyzed and validated; specifically against the
field values, ITE method, HCM 1994 and HCS 2000 procedures.

### Video Data Collection

The traffic data utilized for this study were obtained at four
intersections on 15^th^ Street in Tuscaloosa, Alabama. The 15^th^ street
corridor is considered to be one of the busiest corridors in the City,
with several intersections experiencing saturation flow during the peak
hours of the day, i.e., the morning 7:00 to 9:00 AM period and the
evening 4:00 to 6:00 PM period. The following intersections were used
for analysis in this research:

1.  15^th^ Street and McFarland Boulevard

2.  15^th^ Street and 6^th^ Avenue East

3.  15^th^ Street and Hackberry Lane

4.  15^th^ Street and 10^th^ Avenue

As shown in Figure 4-2, these intersections are along 15^th^ Street
between US 82 and Interstate 359. These intersections were chosen
because of the heavy traffic volumes, the associated high delay values,
and the location of permanent video cameras owned by TDOT.

A group of six students collected morning peak data from 7:00 to 9:00
and evening peak data from 4:00 to 6:00 during weekdays. The data
collection process was spread over a two-month period with the amount of
data varying from one day to seven days depending on the approach. Video
cameras were used to record traffic movements at these intersections.
TDOT provided assistance in the video data collection.

> ![](media/image6.jpeg){width="5.736805555555556in"
> height="3.5416666666666665in"}

[]{#__RefHeading___Toc37055953 .anchor}Figure 4-. Map of the 15th Street
corridor

#### ARENA Input Parameters: Various traffic parameters were measured or calculated based on the input requirements for the simulation model and the operational analysis calculations. The following parameters were identified as input for the ARENA simulation. 

1.  arrival distributions and time headways (time between two
    consecutive vehicles in a particular lane),

2.  turn volume percentages,

3.  signalization data, and

4.  number of lanes and signals.

#### ITE Method Input Parameters: Input data for the ITE method, which involves the calculation of delay based on the number of stopped vehicles and the volume in a given time period, are as follows (Traffic Engineering Handbook, 1994):

1.  lane volumes for each approach, calculated from the video data,

2.  stopped vehicle-volume data, i.e., the total number of stopped
    vehicles counted every 15 seconds in a given period of time, and

3.  flow rate for each lane/lane group, i.e., the total traffic volume
    in a lane/lane group in a given time.

    A total of 240 hours of videotape were collected during this study.
    Cleaning of the videotape to collect the required parameters took
    approximately 360 hours. Although this was a time consuming process,
    the data produced was very accurate because any tape could be viewed
    any number of times to verify the data.

### ARENA Model Development

ARENA uses modules to define different processes. There are two types of
ARENA modules: 1) flowchart modules and 2) data modules. Flowchart
modules are connected to form logical relationships dealing with
processing a vehicle through an intersection. Data modules, which can be
edited by a spreadsheet (ARENA User Manual, 1999), are connected to
flowchart modules and control the behavior of those modules, i.e. actual
volumes of vehicles arriving or signal timings. ARENA employs a model
window that consists of two regions: 1) the model workspace that
contains all the model graphics, including the flowchart and animation,
and 2) the spreadsheet view that displays the model data.

The development of the ARENA simulation model is based on:

1.  arrivals,

2.  turn movements, yield on green (YOG) left-turns, and right turns on
    red (RTORs), and

3.  intersection operations.

Actual arrival information was employed to calculate arrival
distributions that were used in "Arrive" blocks in the ARENA model. Peak
hour field data were collected in eight 15-minute intervals. These eight
data sets were used to develop Poisson distributions for arrivals. The
method employed in determining Poisson distributions can be found in
research done by Pagadala (2001).

Vehicles arriving at an intersection must turn or go through an
intersection. An ARENA "Chance" block was utilized to separate YOGs and
to delineate RTOR percentages. An ARENA "Inspect" block was used to
separate right-turn vehicles from through vehicles in the case of a
shared right turn lane.

Finally, each "Arrive" block was assigned a server that processes
vehicles. In this model, the server represents the signal for that
particular lane. Signal green time and red time are achieved by creating
active and inactive phases of the server with the help of logic blocks.

Figure 4-3 illustrates the building units of the ARENA intersection
simulation model. Traffic is generated using the ARENA "Arrive" block
using Poisson distributions. Each vehicle goes into the server, which is
analogous to the traffic signal at the intersection. The vehicle is
processed, i.e., the vehicle remains in the queue until the server
becomes active (signal turns green) and then it is released to the ARENA
"Depart" block that counts the number of departures. The servers follow
the actual signal phasing at an intersection. By combining a series of
arrive-server-depart building units along with chance and inspection
blocks representing turns, an entire intersection can be modeled.

![](media/image7.jpeg){width="5.9118055555555555in"
height="1.3333333333333333in"}

[]{#__RefHeading___Toc37055954 .anchor}Figure 4-. Basic intersection
process

Figure 4-4 shows a signalized intersection simulation model developed
using ARENA. Additional details on ARENA model development for
intersection modeling can be found in Pagadala (2001).

### Operational Analysis

In addition to developing an ARENA model, an operational analysis was
performed based on ITE procedures published in the Traffic Engineering
Handbook. This procedure is recommended by the Highway Capacity Manual.
Video data was analyzed to evaluate the field stopped delay value.

> ![](media/image8.jpeg){width="5.416666666666667in"
> height="4.279861111111111in"}

[]{#__RefHeading___Toc37055955 .anchor}Figure 4-. ARENA simulation model
for a signalized intersection

Equation 4.1 gives the stopped delay value.

> d = V~s~ × t/V ...............................................(4.1)

where d is the stopped delay, t is delay analysis interval, V~s~ is
number of stopped vehicles in the given time, and V is total volume at
the approach in the given time. The underlying assumption in this method
is that a stopped vehicle during any interval stops for the entire
interval length. Hence, the calculation involves t, which is the
analysis interval. The methodology is based on stopped vehicle counts at
intervals of 10 to 20 seconds. Fifteen seconds is taken as the average
count interval (Guidebook for Transportation Corridor Studies). Delay
was initially calculated for all lanes and lane groups, and then
combined to give the approach delay (equation 4.2). The intersection
delay was calculated based on the approach delay (equation 4.3).

> d~A~ = Σ (d~i~ × v~i~)/ Σ (v~i~)
> .......................................(4.2)
>
> d~I~ = Σ (d~A~ × v~A~) / Σ (v~A~)
> .....................................(4.3)

where d~A~ and d~I~ are approach delay and intersection delay, d~i~ is
the lane group delay, v~i~ is the flow rate in the lane group, v~A~ is
approach flow rate.

## Results and Discussion

This task employed video data to develop a new traffic simulation model
using ARENA, an industrial engineering software package. The model was
developed for one intersection, 6^th^ Avenue East and 15^th^ Street in
Tuscaloosa, Alabama, and results from the model were compared against
field volumes obtained manually and delay values by the ITE method at
that intersection and three other intersections: McFarland Boulevard,
Hackberry Lane, and 10^th^ Avenue. Apart from comparison of delay
results, this task also performed a comparative study of level of
service (LOS) results based on the delay values obtained by ITE, HCM
1994, and HCS 2000 methods.

## *ARENA Simulation Model Results*

To begin the evaluation of the ARENA model, it was first necessary to
determine the number of replications required to produce consistent
results. Because the ARENA model is based on probability distributions,
each run produces different results. It was found that twenty
replications of two-hour runs were required. The corresponding half
width of the ARENA results (average delay value) was found to be within
two percent of the mean. Hence, the number of replications was enough
for analysis of the model. Volume and delay obtained from the model were
compared with field values. In some cases, field data was unavailable
for certain approaches because of improper video recording, glare due to
excess light, or poor lighting. In those cases the model was compared to
those approaches for which there was available field data.

The ARENA volume results for 6^th^ Avenue East and 15^th^ Street, during
the AM peak are compared to the field data in Table 4-1. Column one
lists ARENA volume, field volume, and the deviation of ARENA from the
field data. Four traffic movements are displayed: left-turn lane (left)
and the through lanes (East 1, East 2, and East 3). Columns two through
nine show volume data and deviation of the two methods for eight
15-minute intervals. These intervals are additive and show how the ARENA
model performs for total run times between 15 and 120 minutes.

Table 4-1 shows that the ARENA model yields values within 14 to 20
percent (column nine) of actual field volumes when the total two-hour
volumes are considered. Shorter time periods typically produce less
accurate results. These discrepancies may be attributed to the fact that
the actual vehicle arrivals in the field may not be Poisson distributed,
which is an assumption of the model, but rather arrive in platoons or
groups because of the signal progression.

The ARENA model was then used to simulate the three remaining signalized
intersections with similar conditions for both AM and PM peak hours.
Pagadala (2001) provides a detailed comparison of the ARENA model
results and field values for the remaining intersections.

[]{#__RefHeading___Toc37053850 .anchor}Table 4-. 6th Avenue East and
15th Street AM peak East approach volume data

+-----------+------+------+------+------+------+------+------+------+
| *         | **15 | **30 | **45 | **60 | **75 | **90 | *    | *    |
| *Volume** | mi   | mi   | mi   | mi   | mi   | mi   | *105 | *120 |
|           | n.** | n.** | n.** | n.** | n.** | n.** | mi   | mi   |
| \(1\)     |      |      |      |      |      |      | n.** | n.** |
|           | \    | \    | \    | \    | \    | \    |      |      |
|           | (2\) | (3\) | (4\) | (5\) | (6\) | (7\) | \    | \    |
|           |      |      |      |      |      |      | (8\) | (9\) |
+-----------+------+------+------+------+------+------+------+------+
| **Left**  | 10   | 21   | 32   | 43   | 55   | 66   | 77   | 89   |
|           |      |      |      |      |      |      |      |      |
| ARENA     |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| Field     | \-   | 12   | 21   | 35   | 56   | 67   | 81   | 103  |
| Volume    |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| \%        | \-   | 75   | 52   | 23   | 2    | 1    | 5    | **   |
| Deviation |      |      |      |      |      |      |      | 14** |
+-----------+------+------+------+------+------+------+------+------+
|           |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| **East    | 63   | 138  | 210  | 284  | 360  | 432  | 504  | 580  |
| 1**       |      |      |      |      |      |      |      |      |
|           |      |      |      |      |      |      |      |      |
| ARENA     |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| Field     | \-   | 42   | 113  | 214  | 293  | 349  | 407  | 482  |
| Volume    |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| \%        | \-   | 228  | 86   | 33   | 23   | 24   | 24   | **   |
| Deviation |      |      |      |      |      |      |      | 20** |
+-----------+------+------+------+------+------+------+------+------+
|           |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| **East    | 63   | 137  | 209  | 281  | 355  | 429  | 505  | 575  |
| 2**       |      |      |      |      |      |      |      |      |
|           |      |      |      |      |      |      |      |      |
| ARENA     |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| Field     | \-   | 49   | 118  | 210  | 283  | 353  | 425  | 498  |
| Volume    |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| \%        | \-   | 179  | 77   | 34   | 25   | 21   | 19   | **   |
| Deviation |      |      |      |      |      |      |      | 15** |
+-----------+------+------+------+------+------+------+------+------+
|           |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| ###       | 29   | 64   | 97   | 131  | 163  | 197  | 235  | 269  |
| ## East 3 |      |      |      |      |      |      |      |      |
|           |      |      |      |      |      |      |      |      |
| ARENA     |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| Field     | \-   | 25   | 63   | 119  | 175  | 224  | 271  | 327  |
| Volume    |      |      |      |      |      |      |      |      |
+-----------+------+------+------+------+------+------+------+------+
| \%        | \-   | 156  | 54   | 10   | 7    | 12   | 13   | **   |
| Deviation |      |      |      |      |      |      |      | 18** |
+-----------+------+------+------+------+------+------+------+------+

Table 4-2 shows the summary of model and field results for 6^th^ Avenue
East and 15^th^ Street for the AM peak. This table illustrates typical
intersection results, while the remaining three intersections in this
study can be seen in Pagadala (2001).

#### 

[]{#__RefHeading___Toc37053851 .anchor}Table 4-. 6th Avenue East and
15th Street AM peak volume and delay comparison

+--------------+-------+-------+---------+---------+-------+---------+
| ###          | **AR  | **Fi  | **Per   | **      | **Fi  | **Per   |
| ### Approach | ENA** | eld** | centage | ARENA** | eld** | centage |
|              |       |       | Devi    |         |       | Devi    |
| **Lane/Lane  | **Vol | **Vol | ation** | **      | **    | ation** |
| Group**      | ume** | ume** |         | Delay** | Delay |         |
|              |       |       |         |         | (s    |         |
|              |       |       |         | **      | ec)** |         |
|              |       |       |         | (sec)** |       |         |
+--------------+-------+-------+---------+---------+-------+---------+
| \(1\)        | \(2\) | \(3\) | \(4\)   | \(5\)   | \(6\) | \(7\)   |
+--------------+-------+-------+---------+---------+-------+---------+
| EAST         |       |       |         |         |       |         |
+--------------+-------+-------+---------+---------+-------+---------+
| Left         | 89    | 103   | 14      | 55.02   | 21.55 | 155     |
+--------------+-------+-------+---------+---------+-------+---------+
| East 1       | 580   | 482   | 20      | 29.51   | 23.99 | 23      |
+--------------+-------+-------+---------+---------+-------+---------+
| East 2       | 575   | 498   | 15      | 29.53   | 23.22 | 27      |
+--------------+-------+-------+---------+---------+-------+---------+
| East 3       | 269   | 327   | 18      | 29.34   | 9.39  | 212     |
+--------------+-------+-------+---------+---------+-------+---------+
| **WEST**     |       |       |         |         |       |         |
+--------------+-------+-------+---------+---------+-------+---------+
| Left         | 114   | 95    | 20      | 53.88   | 36.97 | 46      |
+--------------+-------+-------+---------+---------+-------+---------+
| West 1       | 488   | 434   | 12      | 29.2    | 19.25 | 52      |
+--------------+-------+-------+---------+---------+-------+---------+
| West 2       | 516   | 456   | 13      | 29.46   | 19.73 | 49      |
+--------------+-------+-------+---------+---------+-------+---------+
| West 3       | 251   | 281   | 11      | 29.28   | 19.06 | 54      |
+--------------+-------+-------+---------+---------+-------+---------+
| **NORTH**    |       |       |         |         |       |         |
+--------------+-------+-------+---------+---------+-------+---------+
| Left Through | 135   | 146   | 8       | 53.56   | 32.05 | 67      |
| Right        |       |       |         |         |       |         |
+--------------+-------+-------+---------+---------+-------+---------+
| **SOUTH**    |       |       |         |         |       |         |
+--------------+-------+-------+---------+---------+-------+---------+
| Left         | 148   | 136   | 9       | 41.88   | 27.46 | 53      |
+--------------+-------+-------+---------+---------+-------+---------+
| South 1      | 176   | 164   | 7       | 41.4    | 11.55 | 258     |
+--------------+-------+-------+---------+---------+-------+---------+

Column one of Table 4-2 shows the approach direction (bold) along with
the lane or lane group. Column two provides volume data generated by the
ARENA model, and the corresponding field volumes are in Column three.
Column four computes the amount of deviation of the ARENA volume from
the field volume. Columns five and six give ARENA delay and field delay
values respectively. The deviation of ARENA delay results from field
delay values are computed in Column seven.

Over the two-hour period, the ARENA model generates traffic volumes with
an observed deviation of 7 to 20 percent as seen in Column four of Table
4-2. ARENA was found to greatly over-predict intersection delay by 23 to
258 percent as shown in column seven of Table 4-2. This is shown
graphically in Figure 4-5, which is a typical plot produced by plotting
the 15-minute interval delay values from ARENA and the field values for
the left-turn lane of the eastbound approach. This plot includes the
delay values in seconds on the Y-axis and the eight 15-minute intervals
in the two-hour peak period on the X-axis. Although the ARENA model
generates a delay pattern that is similar to the field values, which is
encouraging, the actual value produced by the two methods is quite
different, up to 258 percent different. The dissimilarity between the
field plots and ARENA plots may be due to the fact that the field values
were not following a true Poisson distribution, which was assumed in the
ARENA model.

![](media/image9.jpeg){width="5.152083333333334in"
height="3.207638888888889in"}

[]{#__RefHeading___Toc37055956 .anchor}Figure 4-. 6th Avenue East AM
peak model validation: east approach -- left lane

In addition, turn lanes or shared through-turn lanes performed worse (53
to 258 percent deviation) when compared to strictly through lanes (23 to
52 percent deviation). This indicates that the ARENA model requires more
refinement with respect to turn movements.

Table 4-3 illustrates the LOS of the four intersections in this study in
terms of average stopped delay. Column one lists the intersection, while
column two indicates the AM and PM peak hours.

[]{#__RefHeading___Toc37053852 .anchor}Table 4-. LOS Table: A
comparative study

<table>
<colgroup>
<col style="width: 16%" />
<col style="width: 7%" />
<col style="width: 10%" />
<col style="width: 8%" />
<col style="width: 10%" />
<col style="width: 8%" />
<col style="width: 11%" />
<col style="width: 6%" />
<col style="width: 12%" />
<col style="width: 7%" />
</colgroup>
<tbody>
<tr class="odd">
<td><h1 id="intersection">Intersection</h1></td>
<td><strong>Peak Hour</strong></td>
<td><h1 id="ite-field-sec">ITE Field (sec)</h1></td>
<td><p><strong>ITE</strong></p>
<p><strong>LOS</strong></p></td>
<td><strong>HCM 1994 (sec)</strong></td>
<td><strong>LOS</strong></td>
<td><strong>HCS 2000 (sec)</strong></td>
<td><strong>LOS</strong></td>
<td><p><strong>ARENA</strong></p>
<p><strong>(sec)</strong></p></td>
<td><strong>LOS</strong></td>
</tr>
<tr class="even">
<td>(1)</td>
<td>(2)</td>
<td>(3)</td>
<td>(4)</td>
<td>(5)</td>
<td>(6)</td>
<td>(7)</td>
<td>(8)</td>
<td>(9)</td>
<td>(10)</td>
</tr>
<tr class="odd">
<td><strong>15<sup>th</sup> Street and</strong></td>
<td colspan="9"></td>
</tr>
<tr class="even">
<td rowspan="3">6th Avenue East</td>
<td>AM</td>
<td>19.99</td>
<td>C</td>
<td>28.17</td>
<td>D</td>
<td>41.60</td>
<td>E</td>
<td>33.08</td>
<td>D</td>
</tr>
<tr class="odd">
<td>PM</td>
<td>18.72</td>
<td>C</td>
<td>28.18</td>
<td>D</td>
<td>43.20</td>
<td>E</td>
<td>33.00</td>
<td>D</td>
</tr>
<tr class="even">
<td colspan="9"></td>
</tr>
<tr class="odd">
<td rowspan="3">McFarland Blvd.</td>
<td>AM</td>
<td>29.38</td>
<td>D</td>
<td>33.00</td>
<td>D</td>
<td>71.80</td>
<td>F</td>
<td>-</td>
<td>-</td>
</tr>
<tr class="even">
<td>PM</td>
<td>25.14</td>
<td>D</td>
<td>150.63</td>
<td>F</td>
<td>125.30</td>
<td>F</td>
<td>-</td>
<td>-</td>
</tr>
<tr class="odd">
<td colspan="9"></td>
</tr>
<tr class="even">
<td rowspan="3">Hackberry Lane</td>
<td>AM</td>
<td>22.75</td>
<td>C</td>
<td>77.43</td>
<td>F</td>
<td>58.40</td>
<td>E</td>
<td>40.45</td>
<td>E</td>
</tr>
<tr class="odd">
<td>PM</td>
<td>19.23</td>
<td>C</td>
<td>124.91</td>
<td>F</td>
<td>61.10</td>
<td>F</td>
<td>38.36</td>
<td>D</td>
</tr>
<tr class="even">
<td colspan="9"></td>
</tr>
<tr class="odd">
<td rowspan="2">10th Avenue</td>
<td>AM</td>
<td>22.27</td>
<td>C</td>
<td>41.50</td>
<td>E</td>
<td>67.30</td>
<td>F</td>
<td>-</td>
<td>-</td>
</tr>
<tr class="even">
<td>PM</td>
<td>21.07</td>
<td>C</td>
<td>30.93</td>
<td>D</td>
<td>55.30</td>
<td>E</td>
<td>45.71</td>
<td>E</td>
</tr>
</tbody>
</table>

Column three provides ITE delay values in seconds, and column four
provides the corresponding LOS based on the HCM 1994. Columns five and
six show the HCM 1994 results, while columns seven and eight provides
HCS 2000 results. Column 9 shows the ARENA model results, while column
ten provides the LOS associated with ARENA delay values.

The ITE field delay values at all the four intersections correspond to
LOS C and D, where C represents a good intersection performance and D
represents a congested condition. It should be noted that ITE field
values were obtained from the volume of stopped vehicles in a particular
interval of time, while the HCM 1994 and HCS 2000 methods considered
delays observed during stopping, starting up, and red time at an
intersection. Hence, ITE field values always produce better LOS than
these other methods.

The ARENA model produced LOS (column nine) results that were one and two
levels lower than the ITE field values, and between the HCM 1994 and HCS
2000 results. This again indicates the need for additional model
calibration and refinement. Once the calibration of the model is
improved, Table 4-3 can be used to study the change in LOS of a
signalized intersection by varying the phase plan. It is a future goal
of this work that the LOS estimated by the ARENA model can be studied to
evaluate potential improvements to signal timings.

## Conclusions 

This study used conventional video units and TDOT's traffic surveillance
cameras to gather traffic data. The data was used to develop a
simulation model for signalized intersections using ARENA software. The
ARENA model was developed at one of four intersections and evaluated at
all four intersections. An existing intersection performance evaluation
method, the ITE field delay estimation method, was used to perform
operational analysis on the four intersections. The results of this
method were compared to the ARENA results. Although ARENA is an
industrial engineering simulation software, it showed encouraging but
inaccurate results when used to simulate signalized intersections.
Further research is required to improve ARENA's calibration,
specifically improving turn lanes and stopped delay calculations.

Conclusions and recommendations about video data collection for model
development and analysis are presented here:

-   Data collection using video proved to be useful in obtaining
    information about traffic flow parameters such as volume, turn
    movements, headway, and delay.

-   Unlike manual field data collection, there is always a chance to
    double-check data that is collected using video technology, which is
    beneficial for model development.

-   Conventional video cameras are efficient for collecting traffic data
    that are lane-specific, such as left-only turn lanes and through
    lanes.

-   Surveillance cameras can be operated remotely and allow tilting and
    zooming of the view for traffic recording.

-   Video data collection and reduction processes involve a considerable
    amount of time (the data collection for this study encompassed a
    two-month period) and resources (several persons were required to
    handle the video cameras in the field and to collect vehicle arrival
    time data for estimating arrival distributions).

-   Errors can be introduced in video data collection from camera
    location, camera view, and parallax associated with simultaneous
    platooning in different lanes. To minimize these problems, cameras
    should be located at high vantage points that provide a clear view
    of the intersection.

-   Data requirements for a project should be addressed and a
    comprehensive data collection scheme should be prepared for projects
    involving video. This enables effective data collection.

-   ARENA modeling software has the tools required to model a signalized
    intersection.

-   Although the ARENA model developed in this work was not calibrated
    to a degree that allowed it to be employed in a real life
    signal-timing study, the results show potential for future model
    development.

Section 5

# Railroad-Grade Crossing Automated Video Enforcement Analysis

## Introduction

There are 252,341 highway-rail grade crossings in America, of which
154,087 are public-at-grade crossings. These crossings can be dangerous
when traffic laws are violated. Continual police enforcement at all
railroad-grade crossings is infeasible and cost prohibitive. An
alternative method of enforcement is automated enforcement. The use of
automated enforcement commonly involves video-based technology to
photograph the license tag and driver during a violation. Despite the
fact that automated enforcement has expanded rapidly worldwide and in
the United States, Alabama has yet to implement any such an enforcement
program. The state does not have legislation in place that would allow
for the operation of an automated enforcement program. However, as
automated enforcement continues to grow, pressure will mount on the
state government to investigate the implementation of automated
enforcement technologies.

The objective of this task was to investigate the use of video-based
technology to study traffic behavior and possible violation enforcement
at railroad-grade crossing in the State of Alabama. Safety measures at
railroad-grade crossings were investigated and recommendations were made
for the use of automated enforcement and advance warning of train
arrival systems.

### 

## Study Approach

The objective of this task was accomplished by:

1.  Reviewing literature regarding accident trends and safety measures
    at railroad-grade crossings;

2.  Selecting sites, based on accident history and safety concern, for
    data collection;

3.  Video recording of selected sites; and

4.  Analyzing video data to report the type of violations that may lead
    to an accident.

### 

The scope of this task was to study warning time, frequency, and type of
violations at railroad-grade crossings. Further research should be done
to study the effect of crossing parameters such as sight distance, speed
on approach lane, type of warning device, etc. all of which govern the
safety at railroad-grade crossings.

## Background

According to 2001 railroad fatality data, the State of Alabama ranks
10^th^ nationally in the number of fatal railroad-grade crossing
crashes. Alabama ranked 11^th^ in 2000. Table 5-1 shows fatality
rankings by state for years 2000 and 2001. This table lists the total
number of fatalities, and is not normalized by population, number of
grade crossings, or number of vehicle miles traveled.

Train-vehicle collisions are considered to be more dangerous than
vehicle-vehicle collisions. "Compared to a collision between two highway
vehicles, a collision with a train is 11 times more likely to result in
a fatality, and 5.5 times more likely to result in a disabling injury"
(EPA, 2000). Train operations cause delays to motorists at
railroad-grade crossings. Collisions at grade crossings are a direct
result of misjudgment, or traffic violations by motorists. Research has
shown that installing advance train detection systems coupled with
visible, high profile variable message signs (VMS), and implementing law
enforcement programs are effective in reducing delays, crossing
violations, and fatalities at railroad grade crossings.

[]{#__RefHeading___Toc37053957 .anchor}Table 5-. Highway-railroad grade
crossing fatalities and rankings: 2000-2001

+----------------+-----------+------+-----------+-----------+-------+
| #### State     | ####      | **Ra | #         | **Number  | **R   |
|                | # Number  | nk** | ### State | of        | ank** |
|                | of fatali |      |           | f         |       |
|                | ties 2000 |      |           | atalities |       |
|                |           |      |           | 2001**    |       |
+----------------+-----------+------+-----------+-----------+-------+
| Texas          | 52        | 1    | C         | 53        | 1     |
|                |           |      | alifornia |           |       |
+----------------+-----------+------+-----------+-----------+-------+
| Illinois       | 31        | 2    | Texas     | 39        | 2     |
+----------------+-----------+------+-----------+-----------+-------+
| Arkansas       | 27        | 3    | Illinois  | 31        | 3     |
+----------------+-----------+------+-----------+-----------+-------+
| California     | 27        | 3    | Louisiana | 22        | 4     |
+----------------+-----------+------+-----------+-----------+-------+
| Indiana        | 23        | 4    | Mi        | 22        | 4     |
|                |           |      | ssissippi |           |       |
+----------------+-----------+------+-----------+-----------+-------+
| Missouri       | 17        | 5    | Ohio      | 22        | 4     |
+----------------+-----------+------+-----------+-----------+-------+
| Florida        | 15        | 6    | Georgia   | 19        | 5     |
+----------------+-----------+------+-----------+-----------+-------+
| Mississippi    | 15        | 6    | Indiana   | 19        | 5     |
+----------------+-----------+------+-----------+-----------+-------+
| Ohio           | 15        | 6    | Iowa      | 16        | 6     |
+----------------+-----------+------+-----------+-----------+-------+
| Wisconsin      | 15        | 6    | Florida   | 14        | 7     |
+----------------+-----------+------+-----------+-----------+-------+
| Louisiana      | 14        | 7    | Michigan  | 11        | 8     |
+----------------+-----------+------+-----------+-----------+-------+
| North Carolina | 14        | 7    | Kentucky  | 10        | 9     |
+----------------+-----------+------+-----------+-----------+-------+
| Michigan       | 13        | 8    | Tennessee | 10        | 9     |
+----------------+-----------+------+-----------+-----------+-------+
| Oklahoma       | 12        | 9    | Wisconsin | 10        | 9     |
+----------------+-----------+------+-----------+-----------+-------+
| Idaho          | 11        | 10   | **        | **9**     | *     |
|                |           |      | Alabama** |           | *10** |
+----------------+-----------+------+-----------+-----------+-------+
| Kansas         | 11        | 10   | Arkansas  | 9         | 10    |
+----------------+-----------+------+-----------+-----------+-------+
| **Alabama**    | **10**    | **   | Minnesota | 9         | 10    |
|                |           | 11** |           |           |       |
+----------------+-----------+------+-----------+-----------+-------+
| Georgia        | 10        | 11   | Oklahoma  | 9         | 10    |
+----------------+-----------+------+-----------+-----------+-------+
| South Carolina | 10        | 11   | \-        | \-        | \-    |
+----------------+-----------+------+-----------+-----------+-------+

Safety at railroad-grade crossings can be improved by carefully
monitoring crossings. Video technology can be used to record driver
behavior at railroad-grade crossings, and to understand the effect of
crossing parameters such as sight distance, speed limit on the approach
lane, warning time, and type of warning device. Based on such studies,
measures can be implemented to improve the safety at railroad-grade
crossings.

Warning time is considered to be one of the most important parameters
that govern safety at railroad-grade crossings. According to the Manual
on Uniform Traffic Control Devices (MUTCD), there should be a minimum
warning time of 20 seconds before a train approaches a crossing (MUTCD,
2001). Sensors, which activate warning devices, are placed on the tracks
at a specific distance from the crossing. Therefore, a constant warning
time at railroad-grade crossings is difficult because trains move at
different speeds. The distance, and therefore the time, is set for the
fastest expected train. When a slow moving train approaches warning
times increase, which gives motorists a false concept of warning time.
As a result motorists try to beat the train and may end up in
collisions. Automated enforcement could be implemented to increase the
safety by discouraging motorists from trying to beat the train.

## Literature Review

The laws that define violations at railroad-grade crossing were
examined. A comprehensive review was performed on accident history and
safety measures at railroad-grade crossings.

### Definition of Railroad-Grade Crossing Violation

"Alabama prohibits motorists from driving through, around, or under any
crossing gate or barrier while the gate or barrier is closed or in the
process of being opened or closed." (State Laws, 2001)

From the above law a motorist is said to commit a violation if he or
she:

1.  Drives under the gates as they are descending;

2.  Drives through or around the gates when they are in horizontal
    position; or

3.  Drives under the gates as they are ascending.

Any one of the above maneuvers can cause a train-vehicle collision.
Driving under the gates when they are descending is dangerous especially
with fast moving trains when warning times are reduced. Motorists'
misjudgment of train speeds is the most common reason for train-vehicle
collisions. This typically occurs when motorists drive through or around
the gates before a train approaches. Other train-vehicle collisions
occur when motorists cross tracks as the gates are ascending and do not
notice a train coming in the opposite direction on a second track.

### Railroad-Grade Crossing Accident History and Safety Measures 

According to Federal Railroad Administration (FRA) statistics, someone
is hit by a train every 115 minutes in America. Also, there are more
than nine tractor truck-train collision per week, and there are many
more near-hits than collisions. Table 5-2 shows that the total number of
collisions and fatalities in the nation were reduced over the past two
decades, although the number of fatalities per collision has increased
from 0.078 in 1981 to 0.130 in 2001. Based on these statistics, it could
be concluded that the accidents that have been reduced are not as severe
as the accidents that are still occurring (OLS, 2001). There is no
particular trend in the number of injuries per collision.

[]{#__RefHeading___Toc37053958 .anchor}Table 5-. National
Highway-railroad grade crossing collisions and casualties: 1981-2001

Table 5-3 illustrates the number of collisions and fatalities at
railroad crossings in Alabama for years 1998-2001.

[]{#__RefHeading___Toc37053959 .anchor}Table 5-. Alabama
highway-railroad grade crossing collisions and fatalities: 1998-2001

  ---------- ---------------- ---------------- ------------------------------
  **Year**   **Collisions**   **Fatalities**   **Number of fatalities per
                                               collision**

  2001       103              9                0.087

  2000       95               10               0.105

  1999       124              12               0.097

  1998       145              11               0.076
  ---------- ---------------- ---------------- ------------------------------

As stated earlier, train-vehicle collisions are the direct result of
violations of traffic laws by motorists at railroad crossings. Safety
measures like highway traffic signals, raised medians, and four-quadrant
gates were designed to prevent violations at railroad-grade crossings.
Flashing red lights at railroad crossings may be replaced by highway
traffic signals based on the assumption that drivers will comply with
the traffic signals more often than flashing red lights. While raised
medians and four-quadrant gates prevent drive-around violations, they do
not prevent violations that occur while the gates are ascending or
descending (Fitzpatrick, et al. 1997).

In Alabama, a traffic law violator can be ticketed only by a police
officer. There are many safety concerns that limit police enforcement of
traffic laws at railroad-grade crossings; sometimes a police officer
would have to violate a traffic law (go around the gates) to apprehend a
violator. The safety of a police officer is at risk if this is done.
Also, police enforcement at 252,341 private and public railroad-grade
crossings is not possible. Based on a study in Los Angeles, California
it can be said, "the use of photo enforcement cameras for the
enforcement of traffic laws at railroad-grade crossings is significantly
less costly than the use of police officers." (LACMTA, 1997) Therefore,
advanced safety improvement programs like real time surveillance, and
automated enforcement could be enhanced by the aid of video technology
in enforcing traffic laws to reduce violations and severe collisions at
railroad-grade crossings in Alabama.

## Video Data Collection

An effort was made in this research project to determine the rate of
violations at three railroad-grade crossings in Alabama. The following
sections describe the video data collection procedure and data analysis
of recorded video. Data were collected at three railroad-grade
crossings, two in Tuscaloosa, Alabama and one in Birmingham, Alabama. A
team of two graduate students was involved in collecting data over a
period of eight months from September 2001 to April 2002. Video
technology was used to record traffic movements at railroad-grade
crossings for 24 hours a day. TDOT and the Birmingham Traffic Management
Center provided assistance in the data collection.

### Site Selection and Description

Based on traffic volumes and guidance from TDOT officials, two railroad
grade crossings in Tuscaloosa were selected. As shown in Figure 5-1, the
Kansas City Southern Railway Company (KCS) and Norfolk Southern Railway
Company (NS) line has two tracks, which run in an east-west direction.
These tracks run parallel to 15^th^ Street and University Boulevard,
between U.S Route 82 and 10^th^ Avenue, before intersecting 15^th^
Street at an angle. 15^th^ Street is considered to be one of the busiest
streets in Tuscaloosa, Alabama. The two selected crossings are located
at the intersection of the railway tracks with 10^th^ Avenue (Site 1)
and Hackberry lane (Site 2). Both sites are equipped with flashing
lights and dropping gates. The reason for selecting these crossings is
their close proximity to University Boulevard and The University of
Alabama Campus, and because they fall within TDOT's traffic surveillance
camera locations. Two traffic surveillance cameras (Figure 5-1) located
at the target intersections were used to collect video data at these
crossings.

![](media/image10.jpeg){width="5.295138888888889in"
height="2.9652777777777777in"}

[]{#__RefHeading___Toc37055974 .anchor}Figure 5-. Tuscaloosa sites and
camera locations

After contacting the Assistant Traffic Engineer for the City of
Birmingham, the crossing on 40^th^ Street Southwest (Site 3) was
selected based on the high volume of gasoline tanker trucks that use the
crossing daily. Figure 5-2 shows the location of Site 3, which is
equipped with flashing lights and dropping gates. Two tracks owned by
Norfolk Southern Railway Company run in a northeast to southwest
direction at this crossing. Officials from the City of Birmingham
installed a University of Alabama Autoscope camera (camera 3, Figure
5-2) on a power pole located at the intersection of Gray Avenue and
40^th^ Street Southwest.

![](media/image11.jpeg){width="4.915972222222222in"
height="2.6881944444444446in"}

[]{#__RefHeading___Toc37055975 .anchor}Figure 5-. Birmingham site and
camera location

### Data Collection

TDOT's traffic monitoring video cameras were used to collect video data
at sites 1 and 2. The first camera is located at the intersection of
15^th^ Street and 10^th^ Avenue and recorded traffic movements at
Site 1. Traffic movements at Site 2 were recorded with camera two
located at the intersection of 15^th^ Street and Hackberry lane. Data
were collected for five days in October 2001 and for five days in April
2002. Videotapes were changed every 24 hours by TDOT.

Data were collected for 17 days in March 2002 at Site 3 with the
assistance of the City of Birmingham. Officials from the City installed
the camera and also a control box on a power pole. The control box
stored the Autoscope equipment, computer, and three video cassette
recorders (VCR) that were pre-timed to record data for 24 consecutive
hours. A team of two graduate students changed the tapes daily.

## Analysis and Results 

Analysis of video data from the three sites was performed to study the
effect of train arrival time on the number of violations. The following
section provides a brief discussion on data reduction, processing, and
analysis of train arrival timings and violations.

### Data Reduction

Data in this study mainly focused on train arrival times, and the types
of traffic violations at railroad-grade crossings. An undergraduate
student viewed the videocassettes and recorded the following timings
manually:

1.  Flashing light activation time;

2.  Violation time;

3.  3-point and U-turn time (a 3-point turn is a turn made in three
    stages to travel in the opposite direction from the crossing. A
    U-turn is similar to a 3-point turn, but is made in one continuous
    loop);

4.  Train arrival time;

5.  Train departure time; and

6.  Time when the gates were in a vertical position.

Violations were grouped into two categories after the initial data
analysis process. The first category, termed "before train" (BT)
violations, occurred in the time interval between the activation of
flashing signals and the train entering the crossing. The second
category, termed "after train" (AT) violations, occurred after the train
left the crossing and before the gates were completely raised. A
combination of BT and AT violations is referred to as "sneak through"
violations. Any 3-point or U-turn is referred to as TPUT.

Although data were collected for 10 days at Site 1, only nine days of
data were available for analysis as the TDOT's traffic management center
lost signals from the traffic monitoring cameras for a day due to
technical problems. Similarly, for Site 2 only eight days of data were
available. Seventeen days of complete data were available for Site 3.
Thus, 34 days of data (24 hours a day) were available for data analysis.

### Violation Data and Observations

Table 5-4 shows a summary of recorded BT violations, AT violations, and
TPUTs. A total of 168 sneak through violations and 238 TPUTs were
observed in 34 days. The average rate of sneak through violations was
4.94 per day, while the average rate of TPUTs was 7.00 per day. A closer
look into the violation rate at each site indicated that the railroad
grade crossing at 40^th^ Street Southwest in Birmingham had the highest
rate, 7.65 sneak through violations per day. On the other hand, the
crossing at 10^th^ Avenue in Tuscaloosa had the highest TPUT rate, 19.44
per day.

[]{#__RefHeading___Toc37053960 .anchor}Table 5-. Summary of violations
and TPUTs for sites 1-3

  ---------- -------- -------- -------------- ----------- ------------ ------------- ----------
  **Site**   **BT**   **AT**   **Total sneak  **TPUTs**   **Data       **Number of   **Number
                               through                    collection   sneak through of TPUTs
                               violations                 period       violations    per day**
                               (BT+AT)**                  (Days)**     per day**     

  1          23       7        30             175         9            3.33          19.44

  2          8        0        8              44          8            1.00          5.50

  3          128      2        130            19          17           7.65          1.12

  Total      159      9        168            238         34           4.94          7.00
  ---------- -------- -------- -------------- ----------- ------------ ------------- ----------

A summary of train arrivals and gate activations for these three sites
is illustrated in Table 5-5. A total of 788 trains and 809 gate
activations were observed. The difference in the number of trains and
gate activations is attributed to the fact that there were 33 gate
activations without a train approaching the crossing. Also, there were
two instances when a utility truck approached the crossing without the
gates being activated.

In addition to the values and information discussed above, there were a
few special observations made at these sites:

1.  10^th^ Avenue, Tuscaloosa, Alabama:

    a.  Five violations were observed when the gates were activated
        without a train approaching the crossing;

    b.  Six violations were observed when a utility truck took 155
        seconds to reach the crossing after the gates had been
        activated;

    c.  One violation was observed when a freight train took 141 seconds
        to reach the crossing after the gates have been activated;

    d.  One train approached the crossing just two seconds after a
        motorist snuck through the crossing (a near hit); and

    e.  Once a train approached the crossing within five seconds after
        the flashing lights were activated; the gates just started
        moving down when the train reached the crossing. There was no
        traffic on the approach lane because it was very early (1:10 AM)
        in the morning.

2.  40^th^ Street Southwest, Birmingham, Alabama:

    a.  Due to technical problems a train stopped on the tracks and
        blocked the crossing for 21,442 sec (nearly 6 hours). 23 TPUTs
        were observed during this time, but are not reported in Table
        5-5 because the crossing was blocked due to technical problems
        and not due to train operations.

[]{#__RefHeading___Toc37053961 .anchor}Table 5-. Summary of number of
trains and gate activations

+-----------------------------+---------+---------+---------+---------+
|                             | **Site  | **Site  | **Site  | **      |
|                             | 1**     | 2**     | 3**     | Total** |
+-----------------------------+---------+---------+---------+---------+
| Freight Trains              | 180     | 130     | 403     | 713     |
+-----------------------------+---------+---------+---------+---------+
| Passenger Trains            | 16      | 13      | 28      | 57      |
+-----------------------------+---------+---------+---------+---------+
| Utility Trucks              | 9       | 8       | 1       | 18      |
+-----------------------------+---------+---------+---------+---------+
| Total Trains                | 205     | 151     | 432     | 788     |
+-----------------------------+---------+---------+---------+---------+
| Gate Activations            | 222     | 158     | 429     | 809     |
+-----------------------------+---------+---------+---------+---------+
| Utility Trucks without      | 1       | 1       | \-      | 2       |
|                             |         |         |         |         |
| Gate Activation             |         |         |         |         |
+-----------------------------+---------+---------+---------+---------+
| Gate Activations without a  | 20      | 10      | 3       | 33      |
| Train                       |         |         |         |         |
+-----------------------------+---------+---------+---------+---------+
| Two Tracks Occupied         | 2       | 1       | 6       | 9       |
+-----------------------------+---------+---------+---------+---------+

### Violations and TPUTs vs. Train Timings

Figure 5-3 shows a histogram of train approach timings and the number of
before-train sneak-through-violations. Approach time is defined as the
time taken by a train to reach the crossing after the flashing lights
have been activated. The maximum number of violations (69) occurred when
the train reached the crossing 41-50 seconds after the flashing lights
were activated.

There were not many after-train sneak-through-violations. Figure 5-4
shows a plot of the number of ATs versus train departure times.
Departure time is defined as the time required for the gates to return
to the vertical position after the train left the crossing. Four
violations were observed when the departure time for a utility truck was
91 to 100 sec. Excluding these four violations, only five AT violations
were observed in 34 days.

Figure 5-5 illustrates the number of TPUTs for every 30-second time
interval when the crossing was blocked. No particular trend was observed
for TPUTs. However, the maximum number of TPUTs was observed when the
train blocked the crossing for 151-180 seconds.

![](media/image12.wmf)

[]{#__RefHeading___Toc37055976 .anchor}Figure 5-. Sneak through
violations before train

![](media/image13.jpeg){width="4.374305555555556in"
height="2.6041666666666665in"}

[]{#__RefHeading___Toc37055977 .anchor}Figure 5-. Sneak through
violations after train

![](media/image14.wmf)

[]{#__RefHeading___Toc37055978 .anchor}Figure 5-. 3-point and U-turns

A comparison of day and night violations for sneak through and TPUTs can
be seen in Figure 5-6. There were more sneak through violations and
TPUTs during the day. This is because there are more traffic movements
at railroad-grade crossings during the day.

![](media/image15.jpeg){width="4.520138888888889in"
height="2.4784722222222224in"}

[]{#__RefHeading___Toc37055979 .anchor}Figure 5-. Comparison of day and
night violations and TPUTs

### Discussion

From the above observations it can be seen that there is a need for
traffic law enforcement and safety improvement programs at rail-grade
crossings in Alabama. Long delays or faulty gate activations without a
train approaching the crossing may entice motorists to violate traffic
laws, jeopardizing their safety. The scope of this report was limited to
studying the effect of train arrival times and total blockage times on
the number of violations and TPUTs. Other geometric factors such as
sight distance, speed limits on approach lanes, and distance between two
tracks that affects the number of traffic violations were not
investigated. More research is recommended to study the effects of these
factors on motorists, and to address safety improvements at rail-grade
crossings.

## Conclusions 

Video data collection was helpful in obtaining information regarding
train arrival times and the number of violations at railroad-grade
crossings. The greatest advantage of video data collection is the
ability to double check arrival times to eliminate errors in data
reduction. However, care should be exercised in installing and operating
video equipment; the recorded video was not always clear due to
technical problems. The equipment should be checked frequently to
eliminate these types of errors because important information might be
lost.

A violation rate of 7.65 sneak through violations a day at Site 3 in
Birmingham emphasizes the need for automated enforcement of traffic laws
in Alabama. A rate of 19.44 TPUTs per day at Site 1 in Tuscaloosa
illustrated the need to deploy advance warning of train arrival systems
at similar railroad-grade crossings in Alabama.

## Recommendations for Future Study

This task was limited to investigate the effect of train arrival times
on the number of violations and TPUTs. Other crossing parameters like
sight distance, speed limits on approach lanes, number of approach
lanes, and type of control device could be studied to predict rate of
violations at railroad-grade crossings.

During data collection, care should be exercised in installing the video
equipment so that a clear image of the gate activations is visible;
video equipment at TDOT had to be adjusted several times to get an
optimal view of railroad-grade crossings without compromising the view
of the queue length (queue length of vehicles stopped at railroad-grade
crossings is helpful in checking TPUTs).

An automated enforcement pilot project should be implemented to gauge
the effect of automated enforcement on violators of traffic laws at
railroad-grade crossings in Alabama. Similar kind of pilot study at
several other critical sites throughout the state is highly recommended
to help propose a new legislation on automated enforcement in Alabama.

Section 6

# Variable Message Signs for Violation Notification

## Introduction

Aggressive driving is one of the most prevalent factors contributing to
traffic crashes in the United States. The National Highway Traffic
Safety Administration (NHTSA) estimated that about one-third of all
crashes and two-thirds of resulting fatalities in the U.S. can be linked
to aggressive driving behavior. Speeding and red light running are
considered to be the most common characteristics of aggressive driving
(NHTSA, 2002). It has been shown that VMS can be employed as a speed
deterrent. Mobile VMS, coupled with speed detection devices, can be
installed at places where speeding has been a problem. This provides
drivers with real-time messages indicating their speeds as well as the
posted speed limit. A similar application was examined in this research
to improve driver compliance at intersections by providing real-time
messages for red-light-runners indicating that they ran a red light.

The objective of this study was to develop a methodology for using
traffic-monitoring cameras already installed at highway intersections to
detect motorists who had run a red light.

The following tasks were performed in this regard:

1.  Twenty seven traffic-monitoring cameras owned by TDOT were checked
    to determine which had favorable angles and elevations to detect
    violations;

2.  Autoscope software and hardware were combined with conventional
    traffic surveillance cameras (instead of regular Autoscope cameras)
    to detect vehicles; and

3.  Autoscope hardware was examined to determine if it could trigger a
    VMS.

## Methodology

The Autoscope Solo Wide Area Video Detection System developed by the
University of Minnesota and Image Sensing Systems, Inc., uses machine
vision technology to collect traffic data. This system can be used to
gather vehicle speed, length, traffic volumes, delays, and queue lengths
at intersections. Figure 6-1 shows the setup for the Autoscope system.
It consists of a machine vision processor (MVP) camera, hub-interface
panel, hub, and supervisor computer. MVP is a video processor that
accurately detects vehicles by combining a video camera with electronic
lens control, a digital image processor, and a communications port. The
hub-interface panel serves as a mediator and provides the connection
between the MVP and the hub. The hub communicates with the MVP to
provide detection information, detection outputs, and video signals
which are sent to external devices. The hub is also connected to the
supervisor computer.

The Autoscope software on the supervisor computer is used to configure
the MVP and the hub, and to set different types of virtual detectors
used to monitor traffic (Autoscope User Guide).

![](media/image16.jpeg){width="4.7131944444444445in"
height="3.879166666666667in"}

## 

[]{#__RefHeading___Toc37055984 .anchor}Figure 6-. Autoscope system setup

The MVP can be installed in the camera or in the Hub. Autoscope systems
with the MVP installed in the hub can be connected to common
traffic-monitoring cameras to detect vehicles. These cameras are typical
video cameras, which are unlike the Autoscope cameras that have the MVP
built into the cameras.

TDOT maintains 27 traffic-monitoring cameras for traffic surveillance.
They are operated from the traffic management center at TDOT. The TMC
has a complete Autoscope system. The MVP and hub-interface panel are at
the TMC, thereby allowing any of the 27 traffic-monitoring cameras to
act as an Autoscope camera.

After evaluating the field of view provided by all the
traffic-monitoring cameras in Tuscaloosa, a camera (camera one)
installed at the intersection of Lurleen Wallace North (LWN) and
Stillman Boulevard was selected for this study. Figure 6-2 shows the
camera location at this intersection. With the help of a technician at
TDOT, video output from this camera was fed into the Autoscope hub at
the TMC. The image provided by the camera was calibrated using Autoscope
software. Figure 6-3 shows the speed detectors and calibration of video
image setup for this camera. Speed detectors were placed in each of the
three lanes. When the system was run with this setup (Figure 6-4), the
software detected vehicle speeds from the video image. Later the setup
was checked with a count detector and a presence detector. The software
detected vehicles for each of the detector types. It should be restated
that this image was produced by a traffic-surveillance camera and not by
a relatively expensive Autoscope camera. It can be concluded from this
study that typical traffic-monitoring cameras can be used, instead of a
regular Autoscope camera, as long as the image is sent to MVP and then
run through the Autoscope software.

![](media/image17.jpeg){width="3.7083333333333335in"
height="2.4715277777777778in"}

[]{#__RefHeading___Toc37055985 .anchor}Figure 6-. Camera location at
Lurleen Wallace North (LWN) and Stillman Blvd

![](media/image18.jpeg){width="4.1875in" height="3.3333333333333335in"}

[]{#__RefHeading___Toc37055986 .anchor}Figure 6-. Speed detectors and
calibration of video image obtained from camera one

This setup can be extended to detect red-light violators, by providing
signal phase timings from the signal controller to the Autoscope
software. For the purpose of detecting red-light violators, the signal
phases (red, green, yellow) were split into two divisions; the red phase
(red signal time) and the not-red phase (green + yellow phase time).
When these two phases (red and not red) were detected, a signal was sent
to the Autoscope software using the input/output port on the hub.

![](media/image19.jpeg){width="1.698611111111111in"
height="3.2215277777777778in"}

## 

[]{#__RefHeading___Toc37055987 .anchor}Figure 6-. Autoscope setup to
detect vehicle speeds

Figure 6-5 shows the setup for detecting red light violators. Autoscope
only monitors vehicles that pass the detection zone during the red
phases. Since the hub at the TMC and the traffic controller at the
intersection were far apart, a time lag was observed while detecting red
and not red phases. Because of this, the Autoscope red light violation
count might not be accurate.

![](media/image20.jpeg){width="3.2916666666666665in"
height="2.6055555555555556in"}

[]{#__RefHeading___Toc37055988 .anchor}Figure 6-. Autoscope setup to
detect red-light violations at LWN and Stillman Blvd

An alternative method for accurately detecting red light violators at
intersections with a pre-timed signal plan is to set up a "dummy traffic
controller" at the TMC. The dummy traffic controller has the same
signal-timing plan as the traffic controller in the field. Except for
the traffic-monitoring camera, the entire Autoscope and controller setup
can be installed in the TMC. Figure 6-6 shows the Autoscope setup to
detect red light violators at a location with a pre-timed signal plan.
The output of this setup will be a count of red light violations at an
intersection and a voltage (Figure 6-7) that can trigger a VMS
indicating a violation. Video output from traffic-monitoring cameras can
be recorded to check the accuracy of the system.

![](media/image21.jpeg){width="4.016666666666667in"
height="2.859722222222222in"}

[]{#__RefHeading___Toc37055989 .anchor}Figure 6-. Autoscope Setup to
detect red light violations with pre-timed traffic control plan

![](media/image22.jpeg){width="5.636805555555555in"
height="2.676388888888889in"}

## 

[]{#__RefHeading___Toc37055990 .anchor}Figure 6-. Autoscope output used
to trigger VMS

Although, the setup was theoretically viable for detection of red light
violations, it was not practically tested due to technical issues
associated with connections in the TMC besides the time frame and scope
of this research project.

## Conclusions

A methodology to detect red-light running vehicles with
traffic-monitoring cameras was proposed. A quality video image is
critical to accurately detect red-light violations. To improve the video
image, the camera location should be high (30 feet or more) with a clear
view of the intersection. The traffic-monitoring cameras installed by a
traffic management center can be used effectively to detect red-light
violators, in addition to general surveillance.

The proposed setup to detect red light violators can be tested and
verified with video recording of the intersection to determine the
accuracy of using traffic-monitoring cameras with Autoscope software,
which has the capability of activating a VMS. Triggering a VMS upon
detection of a red-light running vehicle appears to be feasible.

Section 7

# Mobile Video Units for Intersection Analysis

## Introduction

Mobile video units can be used for temporary monitoring of traffic flow
for traffic research purposes. Mobile video units can determine traffic
flow parameters to suggest specific permanent video applications for
traffic management and safety. This section of the report discusses the
application and testing of such mobile video units.

The objective of this task was to evaluate mobile video units for
temporarily monitoring traffic flow for research purposes. This was
accomplished by determining the feasibility of using portable digital
video cameras for roadside traffic data collection. Two mobile video
units were tested in this regard, a conventional video camera recorder,
and a digital video camera recorder.

## Background

Video installations can be permanent (pole or building mounted), or
mobile (vehicle or tripod-mounted). Each type of installation has its
own advantages and disadvantages. The scope of this section of report
was limited to only mobile video units that can be used for traffic
research purposes. Traffic data collected from these mobile video units
can be used to select the best locations to install permanent
enforcement cameras. Mobile video units can be used to positively
demonstrate the existence of traffic violators at intersections and at
rail-grade crossings, proving the need for permanent installation of
automated enforcement technologies (McFadden and Graettinger, 2000).

### Conventional Video Camera Recorder

Portable conventional video recorders were used to collect traffic data
at four intersections on 15^th^ Street in Tuscaloosa, Alabama. A group
of six students collected morning peak data from 7:00 to 9:00 AM and
evening peak data from 4:00 to 6:00 PM Monday through Friday. The data
collection process was spread over a two-month period. Video cameras
(Figure 7-1) were set up on a 60-inch tripod by the side of the road to
record traffic movements at these intersections. The recorded videotapes
were analyzed to obtain volumes, headways, arrival distributions, and
queue lengths. For each approach, this data was used to compute the
field value of delay at these intersections. Refer to Section 4 of this
report for a detailed description of data collection and analysis.

![](media/image23.png)

[]{#__RefHeading___Toc37055993 .anchor}Figure 7-. Conventional video
recording camera

### Digital Video Camera Recorder (Camcorder)

Camcorders were used to record traffic movements at three railroad-grade
crossings in Tuscaloosa, Alabama. Figure 7-2 shows the setup of a
camcorder at a rail-grade crossing. A group of three graduate students
collected morning peak data from 7:00 to 9:00 AM, afternoon peak data
from 11:00 AM to 1:00 PM, and evening peak data from 4:00 to 6:00 PM,
for three consecutive days. Camcorders were set on 60-inch tripods,
approximately 10 feet from the edge of the road and 200-300 feet from
the crossing, to record traffic movements.

All three railroad-grade crossings were equipped with flashing lights
and gates. A total of 18 hours of video data were analyzed to collect
violation data with respect to gate, light, and train timings. A student
viewed the recorded videocassettes and recorded the following timings
manually:

1.  flashing lights activation time;

2.  violation time;

3.  3-point and U-turn time;

4.  train arrival time;

5.  train departure time; and

6.  time when the gates were in the vertical position.

    For each train crossing the videocassettes were viewed several times
    to record the exact time of violations and train arrivals. Based on
    this preliminary data, extensive data collection was carried out at
    two of the three railroad-grade crossings using surveillance cameras
    installed and operated by the City of Tuscaloosa. Refer to Section
    5.0 of this report for a detailed description of data collection and
    analysis.

![](media/image24.png)

[]{#__RefHeading___Toc37055994 .anchor}Figure 7-. Digital video camera
recorder

## Comparison of Conventional and Digital Video Camera Recorders

Both conventional video camera recorders and camcorders were used to
collect traffic data for this task. Though camcorders are more expensive
than conventional video cameras, they have more capabilities. Unlike
conventional video recorders, camcorders are lightweight and smaller,
which makes them easy to transport and install. Camcorders record
high-resolution digital video, which can be transferred to videotape or
a computer. Selected still images and movie files of a violation can be
taken from the digital video, which is not possible with the analog
recording from a conventional video recorder. Battery life, which is
critical for field data collection, is much higher for camcorders
compared to conventional video cameras. A camcorder can run for more
than 8 hours on a single battery charge. Unlike conventional video
recording, video recording from camcorders can be enlarged or zoomed at
a later time to get a clear view of the image. For example, this feature
is helpful in reading license plates for origin-destination surveys.
Camcorders can be assigned an internet protocol address to view real
time data from a remote location like the TMC. This feature can be used
at locations that have temporary traffic management issues that do not
justify permanent camera installations.

## I^3^ Cam Van

The *I*ntelligent Transportation Systems *I*nformation and
*I*nfrastructure Laboratory (I^3^ Lab) of the University of Nebraska,
Omaha provides real-time traffic data from a fully self-contained mobile
machine vision and ITS laboratory, named I^3^ Cam Van. As shown in
Figure 7-3, I^3^ Cam Van consists of two Autoscope Solo Pro cameras
mounted 42 feet high on a telescoping mast. The mobile lab is equipped
with a computer and a video recording system to store traffic data (I^3^
Lab Website). Unlike conventional and digital camera recorders, the I^3^
Cam Van is capable of processing a wide range of traffic parameters, and
allows multi-tasking on multi-lane roads with ease and safety in various
weather conditions.

![](media/image25.jpeg){width="2.984722222222222in"
height="3.977777777777778in"}

[]{#__RefHeading___Toc37055995 .anchor}Figure 7-. I^3^ Cam Van

## 

## 

## Conclusions

Mobile video cameras can accurately collect traffic data. However, care
should be exercised when collecting data on multi-lane roads. The camera
should be located high enough to capture a clear image of vehicles
traveling on each lane at all times. Lightning conditions can affect
recording quality, and weather conditions should be checked before
planning field data collection using video. During data cleaning of
recorded video, videocassettes were viewed several times to collect the
exact times when events occurred, which obviously cannot be done for
manually collected data. Video data collection gives a permanent record
of traffic situations, ensuring that accurate and vital information is
not lost. For research purposes, a permanent record always provides an
opportunity to check videotapes at any time for additional traffic data.

The implementation of mobile video systems and the newly developed cam
van technologies depend on factors such as amount of data required,
location characteristics, number of intersections or road segments under
study, weather conditions, and funds allocated for the task. Mobile
video systems can be potentially employed in Alabama for temporary and
permanent traffic management and safety purposes.

Section 8

# Video for Emergency Response Management

## Introduction 

Effective detection, response, clearance, and recovery from disabled
vehicles or vehicle crashes can save lives, reduce delays, save money,
and enhance safety for motorists. Identifying and locating traffic
incidents and responding to these emergencies involves many agencies.
Coordination between these agencies is very important so that they can
verify, locate, and respond appropriately to each emergency. The
responsibility of these agencies becomes even more significant during
severe transportation conditions like an emergency evacuation when
resource allocation is critical. Hence, there is a need for an
integrated system that coordinates information about an incident among
response teams. Research has demonstrated that incident detection and
observation with video technology, coupled with response team
communication, offers real-time visual information that enhances
response, thereby potentially saving lives and money.

The goal of this task was to cast light on the use of video for
emergency response (ER) in Alabama, and to determine if video
(surveillance) cameras currently in place and owned by the City of
Tuscaloosa could be employed for ER management. This section of the
report describes case studies where video applications have been
successfully employed for ER.

## Background

Disruption of traffic flow can be caused by any number of uncontrollable
variables. Congestion, obstructions, power-outages, crashes, etc., are
all incidents that have the potential to disrupt traffic. Regardless of
the reasons for these incidents, it is the job of emergency response
teams to attend to the situation. To successfully respond, interagency
coordination is crucial between transportation management and public
safety agencies. These agencies use different systems for detecting
incidents and collecting information to serve their needs and purposes.
An integrated system could improve the response time and resource
allocation. The ability of video detection systems to provide accurate,
real-time information about an incident will allow transportation
officials to almost instantaneously initiate a response plan and make
crucial decisions.

## Video for Emergency Response Management -- National Case Studies

The following sub-sections briefly describe some of the successful
national experiences related to the use of video for improving emergency
response and incident management.

### Multi-Jurisdictional Live-Aerial Video Surveillance System (1991)

The Virginia Department of Transportation (VDOT) used information
obtained from live aerial video cameras for incident and congestion
management. The live video was provided by a helicopter operated by the
Virginia Police Department (VPD) of Fairfax County. A project was
conducted to evaluate:

-   the use of video imaging for incident and traffic management,

-   the capture and transmission of the video images,

-   the effectiveness and limitations of traffic surveillance from a
    helicopter, and

-   a video application for multiple agency benefits.

Real-time images from video cameras were provided to VDOT, VPD,
emergency services, and a mobile law enforcement van through links to
the Fairfax County Public Safety Communication Center (TESCNET, 2001).
This concept provided video surveillance over a large geographic area at
a low initial cost. The video was used to gather traffic condition
information over a planned route, to identify and resolve congestion
problems, for traffic studies, and in areas that did not have full time
surveillance.

### Houston TranStar (1993)

TranStar is a unique multi-agency program developed to provide a
coordinated approach toward transportation and emergency operations in
the Greater Houston area. Components managed by TranStar include 257
closed circuit television (CCTV) freeway cameras, 100 variable message
signs, emergency management operations, and a flood alert system. The
video cameras provide continuous images of over 160 miles of freeway,
out of a total of 300 miles. This video is sent to all the participating
agencies helping them to improve on various transportation practices and
emergency management response and recovery functions (TESCNET, 2001).
This way of obtaining real-time information created an environment that
improved: responsiveness, personnel and equipment resources management,
pooling of finances, and eliminated administrative restraints (Houston
TranStar, 1999).

### NAVIGATOR, Atlanta, Georgia (1993)

One source of data to Georgia's Intelligent Transportation System is
NAVIGATOR, which gathers traffic incident information by using a video
and traffic detection system. This system provides real-time video
images of road conditions that are sent to TMC operators. This video
data serves as an incident verification tool, which reduces response
time and expedite the removal of incidents thereby minimizes congestion
(ITSNEWS, Feb 2001). Interstates 75 and 85 are equipped with over 300
black and white cameras and 67 pan, tilt, and zoom full color cameras,
which continuously gather data on incidents, average speed, traffic
volume, and vehicle classification. In addition, a camera mounted on a
helicopter provides live video within a 50-mile radius of Atlanta.

With the combination of video monitoring, detection, data management,
and telecommunications technologies, NAVIGATOR is able to detect,
verify, and quickly respond to highway accidents, stalls, or debris
(TESCNET, 2001). The agencies that benefit from this system are the
Georgia DOT, Highway Emergency Response Operator, fire, police, EMS, and
Motor Vehicle Emergency Response.

### TransGuide, San Antonio, Texas (1993)

TransGuide is one of the nation's best Intelligent Transportation
Systems. It was designed by the Texas Department of Transportation's San
Antonio's district. This system was designed to provide real-time
information to motorists about traffic condition such as accidents,
congestion, and construction. It uses cameras, changeable message signs
(CMS), and a fiber optic network to gather traffic information, provide
travel times to motorists, and respond rapidly to emergencies on the
freeways (TESCNET, 2001). The system has been operational since 1995 and
currently utilizes 118 video cameras to monitor 72 of the 289 miles of
San Antonio's highways. Maps showing the location of incidents, traffic
congestion, and lane closure information are provided on the Internet
every five minutes. In addition to location information, the cameras
provide live video images to the Internet. The operation cycle of
TransGuide is shown in Figure 8-1, wherein an incident occurs on the
left side of the figure and within two minutes the TMC detects,
verifies, begins response, and posts information for motorists on a VMS.

![](media/image26.jpeg){width="4.583333333333333in"
height="3.3979166666666667in"}

# 

[]{#__RefHeading___Toc37056002 .anchor}Figure 8-. Operation cycle of
TransGuide

### AZTech, Phoenix, Arizona (1996) 

The goal of the AZTech project was to develop an integrated ITS for the
Phoenix metropolitan area. This system was designed to provide travel
information such as real-time traffic conditions, related road closures,
and accident information. AZTech incorporated isolated systems of
traffic management, incident management, and transit agencies into one
integrated system that could provide information to motorists through
the use of live traffic cameras and variable message signs. This system
is linked through a fiber optic communication system.

AZTech was designed to improve emergency services and incident response
by coordinating police and fire departments, the Department of Public
Safety, and 911 emergency personnel. The AZTech project included a
private company, TRW, which created the integrated system to exchange
data and video information between agencies. Information obtained from
video is analyzed and information about delay, detour information, etc.,
are disseminated to motorists through CMSs. The Phoenix Fire Department
is able to immediately analyze an incident by monitoring video cameras,
which provides an incident management tool (TESCNET, 2001).

### Smart Trek, Seattle, Washington (1996)

Smart Trek is an ITS project designed to deliver regional, multimodal
traveler information to help motorists and commuters make more
intelligent transportation decisions. Communication links were
established between the Washington DOT's TMC and local and regional
emergency response centers. This transfer of information delivers
real-time video traffic data to emergency response personnel, which
improves the response times and emergency services. Incident response
vehicles are equipped with two-way video communication systems to allow
images to be sent to the TMC from the scene of an accident for regional
(upstream) traffic management, and to the vehicle for enhanced on-site
traffic management. These images are also available through the Internet
for use by other emergency responders (TESCNET, 2001).

The City of Bellevue and Washington DOT engineers use the traffic
cameras to provide real-time traffic congestion patterns to motorists,
to monitor ramps, to evaluate signal performance, and to confirm traffic
incidents. Depending upon the traffic flow, appropriate alterations are
made to the traffic signal systems. Similarly, Washington DOT's Tacoma
Traffic Flow Map uses video detection technology to continuously monitor
highway congestion and speeds on the Tacoma Narrows Bridge. The results
are shorter traffic delays, faster emergency response, and improved
public safety. A screen capture from a Smart Trek traffic television is
shown in Figure 8-2.

### 

### CommuterLink, Salt Lake City, Utah (2000)

CommuterLink is a computer-controlled system designed to monitor and
manage traffic flow on freeways and surface streets through the use of
150 CCTV cameras, 57 variable message signs, 550 traffic signals, ramp
meters, traffic speed and volume sensors, pavement sensors, and weather
sensors (USDOT News, 1999). Data from these components are gathered and
analyzed in the Utah Department of Transportation. The data is then
transmitted to the respective Salt Lake City and Salt Lake County
traffic control centers by fiber optic cables. CommuterLink uses over
150 CCTV cameras along surface streets and freeways to monitor traffic
and identify the exact location and cause of problems. Appropriate
emergency response teams are then dispatched to the accident sites to
reduce the delays caused by accidents. For minor accidents or stalled
vehicles, operators can notify UDOT\'s Incident Management Team to
provide assistance. For major accidents, the Utah Highway Patrol and
other emergency service providers can be alerted (TESCNET, 2001).

![](media/image27.jpeg){width="3.4375in" height="2.3194444444444446in"}

# 

[]{#__RefHeading___Toc37056003 .anchor}Figure 8-. SmartTrek-traffic TV

### Summary of existing systems

This review of nationwide experiences with video systems highlights the
applications of video in the areas of emergency management and public
safety. It is a fact that state and local governments suffer from the
costs of congestion. Innovative and cost-effective techniques such as
video are being implemented around the nation to tackle the challenges
of congestion. Real-time video data sharing, either through direct feed
or the Internet, has become a common practice between transportation
management and public safety agencies. These video technologies allow
the state and local agencies to:

-   detect congestion caused by accidents and initiate response quicker
    than before,

-   verify accident sites and determine the appropriate emergency
    response required,

-   respond by dispatching appropriate resources,

-   provide delay times and detour information to motorists through CMS,

-   continuously monitor real-time traffic condition during peak hours,
    and

-   build interagency communications by simultaneously providing the
    real-time video data to all the participating agencies in emergency
    management program.

## Video for Emergency Response in Tuscaloosa

The primary purpose of this section of the research was to evaluate the
use of video camera system currently in place and owned by the City of
Tuscaloosa for the purpose of identifying incidents and determining
appropriate responses by emergency service vehicles. The analysis
focused on ensuring rapid response and efficient use of resources.

The City of Tuscaloosa was chosen because of its developing ITS network,
which includes the expansion of the City's traffic control center and
installation of CCTV cameras at various locations throughout the city.
The City also involves stakeholders such as the Tuscaloosa Police and
Fire Departments, and the City of Northport in this program (McFadden
and Graettinger, 2000). Tuscaloosa currently has an Advanced Traffic
Management System that consists of 27 CCTV cameras on major arterials,
and eight video detection systems (Autoscopes) at the TMC. The system
will be expanded to include 11 dynamic message signs, a traffic data
geographic information system, an integrated emergency management
system, and a real-time traffic congestion analyzer.

### 

### Autoscope for Emergency Response

Autoscope is a proven video vehicle detection system that combines
real-time image processing and computerized pattern recognition within a
flexible software platform. This technology offers fully automated
remote video detection of incidents and visual verification, thereby
permitting early notification of events and improving emergency response
times and resource allocation. In addition, the system has the ability
to monitor multiple lanes with one camera. The wide range of information
gathered provides traffic managers with a means to reduce roadway
congestion, improve roadway planning, and provide real-time detection
information.

Typically, any incident such as a vehicle pulling over to the side of
the road, leads to a slowdown of traffic. As speeds decrease, the
slowdown ripples back through the traffic with a certain velocity. An
incident detector can process the rate at which a queue builds to
determine whether an incident has occurred. Other traffic parameters are
monitored that can suggest an incident has occurred, such as an accident
or a stalled car that produces a sudden variation in roadway capacity.
These monitors actually detect the shock wave (sudden change in traffic
flow), which propagates upstream from the site of the incident (Incident
Detectors, 2001).

To provide incident detection, the video detection system is interfaced
with a machine vision-based Automated/Autoscope Incident Detection
Algorithm (AIDA). After these virtual incident detectors are overlaid
onto the field of view, the detectors calculate traffic parameters such
as speed, volume, occupancy, stopped vehicles, and density. These
incident detectors then process information generated by count and speed
detector pairs to measure changes in the traffic flow. When the AIDA
detects a sudden speed drop, the incident detector generates an audible
alarm, a live video is displayed, and the TMC operators take the
necessary action to verify a potential emergency. The video obtained
from this system can be broadcast to the participating ER teams through
television monitors. Although the whole process is not yet operational
in the City of Tuscaloosa, efforts are in progress to implement this
system (Michalopoulos and Samartin, 1998).

One of the improvements in AIDA is the development of a stopped vehicle
detector, which could be used to simply detect stopped vehicles on the
shoulder within the camera's field of view. To minimize false alarms due
to regular congestion during peak hours, a scheduler can be used to vary
parameter thresholds at different times of the day. This helps operators
at the TMC and other ER teams identify actual incidents. These systems
can use pan and zoom cameras to scan the area to determine the cause or
condition of the incident (Samartin, 1997). In addition, videotapes can
be recorded when incident alarms are detected, thereby allowing for
off-line testing and evaluation of the response efforts. To minimize
congestion, a VMS on the upstream side of an incident can be triggered
by the Autoscope to display messages indicating the downstream
conditions.

### Surveillance Cameras for Emergency Response

TDOT currently has 27 CCTV cameras and eight Autoscope cameras placed
throughout the city for traffic safety and management purposes. Unlike
an Autoscope camera with a built-in machine vision processor, TDOT has a
common machine vision processor that allows simultaneous video input
from all eight Autoscope cameras, which are controlled remotely from the
TMC. An Autoscope camera provides the video feed to a monitor where the
image is captured and calibrated. Detection zones are overlaid and the
image is sent to the MVP, which analyze the video and process
information on speed, volume, occupancy, stopped vehicles, etc.

Any surveillance camera can be used in place of an Autoscope by putting
the feed through a bi-link computer that is interfaced with the
Autoscope software and the common MVP. The CCTV image shown in Figure
8-3 can be processed by Autoscope and used for traffic detection. This
was successfully tested and demonstrated that surveillance cameras can
supply video feed in a format that can be processed by MVP. This
observation is very significant as the inexpensive CCTV cameras
currently in place and owned by the City of Tuscaloosa could be used for
incident detection and could improve ER time.

![](media/image28.jpeg){width="3.5840277777777776in"
height="1.9194444444444445in"}

[]{#__RefHeading___Toc37056004 .anchor}Figure 8-. Video image monitor

## 

## Conclusions

The need for a reliable video system that can improve efficiency by
establishing a communication link between various ER teams was
investigated. The use of video detection to provide real-time traffic
data on incident and emergency management was evaluated from some of the
nation's successful video/emergency experiences. The project also
evaluated the current status of Tuscaloosa's growing ITS network
relative to video/emergency detection and response. Using a video camera
and the AIDA software, early detection of incidents can be accomplished.
It can be concluded that inexpensive CCTV cameras can be deployed in
place of Autoscope cameras to provide video input to the MVP interface.
Thus video can be effectively used for the purpose of emergency
response.

Section 9

# Conclusions and Recommendations

The objective of this research was to evaluate various applications of
low-cost video technology systems in areas of transportation management
and safety in Alabama. Several video systems such as conventional video
recorders, digital video cameras, Autoscope cameras, and surveillance
cameras were applied and tested at various locations. Listed below are
the transportation areas that were investigated in this project, and
that showed a potential benefit from video technology:

*Conventional video cameras*: intersections and mid-blocks,

*Digital video cameras*: railroad grade crossings,

*Autoscope cameras*: compositional traffic counts, red-light running,
speed violation study, emergency response management, and activating
variable message signs, and

*Surveillance cameras*: red-light running study using Autoscope
software, video recording of intersections.

It was shown that video technology could provide an alternative to
standard data collection methods like loop detectors, pitot tubes, and
manual traffic counters. Video data collection is inexpensive,
cost-effective, low-maintenance, provides more data, easy to setup, and
requires fewer infield personnel. Video data can be analyzed manually,
or automatically using machine vision technology such as Autoscope for
vehicle classification of traffic data. In studies related to corridor
analysis and railroad grade crossing safety, video allowed examination
of recorded data any number of times to report accurate traffic
parameters.

In this work, the literature review provided valuable information and
identified several areas where video technology can be employed, such
as:

-   traffic surveillance on freeways,

-   incident detection and response,

-   electronic enforcement,

-   travel time studies, and

-   origin-destination survey.

Some problems and issues that were of concern during this research are
summarized below:

-   safety of the equipment in the field,

-   safety of the crew working with the video equipment on high volume
    roads,

-   roadside video setup distracts drivers and gains unwanted public
    attention,

-   video data reduction process involves a considerable amount of time,

-   water and dust on the camera lens,

-   improper video recording due to technical problems,

-   unclear recorded image due to dull lighting conditions,

-   blooming of image due to headlights of vehicles at nights,

-   occlusion of small vehicles because of tall vehicles moving in an
    adjacent lane, and

-   inaccurate machine counts due to vehicle occlusion.

Some recommendations for successful field video data collection are:

-   check weather conditions and perform a preliminary site survey
    before planning field data collection,

-   train the crew in setting up the equipment before conducting the
    field installation,

-   exercise care when installing and operating the video equipment in
    the field,

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-   check equipment to eliminate technical problems that might cause
    loss of vital information,

-   provide a housing for the camera to protect the camera and improve
    the image by reducing distortions caused by wind, snow, rain, frost,
    and fog, and

-   mount video cameras high enough (greater than 30 feet) to obtain a
    clear view and eliminate obstruction of smaller vehicles by large
    vehicles.

Based on this research, it can be concluded that agencies in Alabama can
employ video technology for traffic data collection and implementation
in advanced traffic management systems. This technology can help reduce
traffic congestion, improve roadway planning, and provide traffic
research opportunities to ensure better traffic management and safety.

Section 10

# References

*ARENA* *User Manual*. Systems Modeling Corporation, 1999.

*Autoscope User Guide*, Econolite Control Products, Inc., Anaheim, CA.

Brown, David B. and Turner, Daniel S. "The Critical Analysis Reporting
Environment (CARE), A versatile tool to obtain optimal accident
countermeasure strategies," The University of Alabama, February 2002.

Econolite, http://www.econolite.com/product/autoscope/solopro.htm, 2001.

EPA, United States Environmental Protection Agency, Federal Register
Document,
http://www.epa.gov/fedrgstr/EPA-IMPACT/2000/January/Day-13/i4.htm,
January 2000.

FHWA-PL-97-018. "Field test of monitoring of urban vehicle operations of
non-intrusive technologies," US DOT, FHWA, 1997.

Fitzpatrick, K., Bartoskewitz, R., Bean, J., Carlson, P. "Traffic
violations at gated highway-railroad grade crossings," Texas
Transportation Institute Research Report 2987-1, College Station, Texas,
October 1997.

*Guidebook for Transportation Corridor Studies: A process of effective
decision-making*, National research Council, Transportation Research
Board, Washington, D.C.

Hani, Mahmassani S., Carl Haas, Sam Zhou, and Josh Peterman. "Evaluation
of incident detection methodologies," FHWA/TX-00/1795-1, October 1999.

Houston TranStar, A case study on Metropolitan Transportation Management
Center, www.its.dot.gov, October 1999.

Incident Detectors, Autoscope Detector Editor Help, Autoscope Solo
Software Suite, Ver 4.20, 24^th^ May 2001.

ITSNEWS, "Image Sensing System\'s Autoscope delivered for expansion in
Atlanta, Georgia," www.itsa.org, February 2001

Keith, Kerri M. and Brown, David B. "Saving lives with CARE -- CARE
training," The University of Alabama, January 2002.

Los Angeles County Metropolitan Transportation Authority (LACMTA).
"Photo Enforcement at Long Beach Blue Line Grade Crossing," Final
Report, State of California Department of Transportation, Public
Transportation, Ride Sharing, and Rail Branch, Sacramento, California,
December 1997.

*Manual on Uniform Traffic Control Devices (MUTCD)*, U.S. Department of
Transportation, Federal Highway Administration, Washington, D.C., 2001.

McFadden, J. and Graettinger, A. "Potential Applications of Video
Technology for Traffic Management and Safety in Alabama," UTCA Report
00102, December 20, 2000.

Michalopoulos, Panos G., Samartin Kevin, "Deployment of state-of-the-art
technology

> for incident management: The Gowanus Expressway Project," TRB Annual
> Meeting, 1998.

National Highway Traffic Safety Administration (NHTSA), U.S. Department
of Transportation, http://www-fars.nhtsa.dot.gov/, 2002

Operation Lifesaver (OLS), http://www.oli.org/library/stats.html,
November 2001.

Pagadala, Nagendranath. "ARENA simulation modeling and operational
analysis of signalized intersections," Thesis, The University of
Alabama, 2001.

Rafael Bras L., and Ignacio Rodriguez-Iturbe. Textbook on "Random
Functions and Hydrology," August 1993.

Samartin Kevin, "Under Detection," Intelligent Transport Systems
International, May/June 1997.

Sami, Mohamed. "Evaluation of the Econolite Control Products Inc.,
Machine Vision Vehicle Detection System," Indiana DOT, 1996.

*State Laws, Alabama Code, 1975, § 32-5A-150 (a)(b)*, 1999, Federal
Railroad Administration, U.S. Department of Transportation,
http://www.fra.dot.gov/safety/statelaws.htm, 2001.

TESCNET (Transportation & Emergency Services Communications Network),
URS/BRW, Inc., "National Experiences in Emergency Management and
Integrated Communication Systems," February 2001.

*Traffic Engineering Handbook.* Institute of Transportation Engineers,
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collection equipment," FHWA, May 1, 2001.

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City Traffic Operations Center, April 1999.

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stochastic hydrology," Water Resources Research, Vol. 9, No. 3, pp:
580-585, June 1973

# 

## Web resources

AZTech web page, www.aztech.org

CARE^®^ software, http://care.cs.ua.edu

CommuterLink web page, http://www.utahcommuterlink.com

I^3^ Lab Website, http://www.i3lab.unomaha.edu

Intelligent Highway System,
http://www.sti.nasa.gov/tto/spinoff1996/36.html

Navigator web page, www.georgia-navigator.com

San Antonio Area Freeway System, www.texhwyman.com/san.htm

SmartTrek web page, http://www.smarttrek.org

TranStar web page, www.houstontranstar.org

TranStar description, http://traffic.tamu.edu/central2.html

TransGuide web page, http://www.transguide.dot.state.tx.us/index.php

www.imagesensing.com

www.autoscope.com

# Appendix

**Program Code Comments**

**function UTCA25(Syx,Sxx,Syy,X,AvgY)** Defines function name with input
variables

**A=Syx × inv(Sxx);** Calculates matrix A by performing matrix
operations on Syx and Sxx

**BBt=Syy-( Syx × inv(Sxx) × (Syx)\' );** Calculates matrix BBt by
performing matrix operations on Syx, Sxx, and Syy

**d=det(BBt);** Computes the determinant of matrix BBt

**R=rank(BBt);** Computes the rank of matrix BBt

**E=eig(BBt);** Computes eigen values of BBt and reads them into Matrix
E to check for positive semi-definiteness of matrix BBt.

**\[V,D\]=eig(BBt);** Generates eigen vector and eigen value matrices of
BBt and reads into V and D matrices respectively

**B=V × sqrtm(D);** Decomposes matrix BBt into matrix B by performing
matrix operations on eigen vectors and eigen values

**Z=zeros(14,1);** Generates a zero matrix Z of order 14 **×** 1

**for i=1:10000** Executes loop for 10000 iterations to calculate a set
of Y values

**W=randn(14,1);** Generates a 14 **×** 1 matrix of random numbers which
follow standard normal distribution (Mean = zero and Standard Deviation
= 1)

**Y=(A × X\' ) + (B × W);** Calculates one set of Y values for each
iteration

**G(1:14,i)=Y;** Reads all Y values, generated by each iteration, into
matrix G

**Z=Z + Y;** Stores the cumulative sum of generated 10000 sets of
Y-values into an array "Z" for further statistical analysis

**end** Terminates the iteration process

**MatY = ( Z/i );** Matlab output of a set of Y-values from 10000
iterations

**Final=MatY+AvgY\';** Stores the sum of corresponding Mean Y values of
input data from Excel and Matlab output Y-values into an array "Final"

**Final** Displays the array "Final"