**Final Technical Report**

**TNW2006-02**

TransNow Budget No. 62-8992

Research Project Agreement No. T2695, Task 57\
Improving Dual-Loop Truck (and Speed) Data: Quick Detection of
Malfunctioning

Loops and Calculation of Required Adjustments

by

Nancy L. Nihan

Yinhai Wang

Patikhom Cheevarunothai

Department of Civil and Environmental Engineering

University of Washington

Seattle, Washington 98195-2700

**Report prepared for:**

**Transportation Northwest (TransNow)\
**Department of Civil Engineering\
129 More Hall\
University of Washington, Box 352700\
Seattle, WA 98195-2700

May 2006

TECHNICAL REPORT STANDARD TITLE PAGE

+---------------------------+-------------------------+--------+------+
| 1\. REPORT NO.            | 2\. GOVERNMENT          | 3\.    |      |
|                           | ACCESSION NO.           | RECIP  |      |
| TNW2005                   |                         | IENT'S |      |
|                           |                         | C      |      |
|                           |                         | ATALOG |      |
|                           |                         | NO.    |      |
+---------------------------+-------------------------+--------+------+
| 4\. TITLE AND SUBTITLE    |                         | 5\.    |      |
|                           |                         | REPORT |      |
| # Improving Dual-Loop Tru |                         | DATE   |      |
| ck (and Speed) Data: Quic |                         |        |      |
| k Detection of Malfunctio |                         | De     |      |
| ning Loops and Calculatio |                         | cember |      |
| n of Required Adjustments |                         | 2005   |      |
+---------------------------+-------------------------+--------+------+
|                           |                         | 6\.    |      |
|                           |                         | PERF   |      |
|                           |                         | ORMING |      |
|                           |                         | ORGANI |      |
|                           |                         | ZATION |      |
|                           |                         | CODE   |      |
+---------------------------+-------------------------+--------+------+
| 7\. AUTHOR(S)             |                         | 8\.    |      |
|                           |                         | PERF   |      |
| Nancy Nihan, Yinhai Wang, |                         | ORMING |      |
| and Patikhom              |                         | ORGANI |      |
| Cheevarunothai            |                         | ZATION |      |
|                           |                         | REPORT |      |
|                           |                         | NO.    |      |
|                           |                         |        |      |
|                           |                         | T      |      |
|                           |                         | NW2005 |      |
+---------------------------+-------------------------+--------+------+
| 9\. PERFORMING            |                         | 10\.   |      |
| ORGANIZATION NAME AND     |                         | WORK   |      |
| ADDRESS                   |                         | UNIT   |      |
|                           |                         | NO.    |      |
| Transportation Northwest  |                         |        |      |
| Regional Center X         |                         |        |      |
| (TransNow)                |                         |        |      |
|                           |                         |        |      |
| Box 352700, 123 More Hall |                         |        |      |
|                           |                         |        |      |
| University of Washington  |                         |        |      |
|                           |                         |        |      |
| Seattle, WA 98195-2700    |                         |        |      |
+---------------------------+-------------------------+--------+------+
|                           |                         | 11\.   |      |
|                           |                         | CO     |      |
|                           |                         | NTRACT |      |
|                           |                         | GRANT  |      |
|                           |                         | NO.    |      |
+---------------------------+-------------------------+--------+------+
| 12\. SPONSORING AGENCY    |                         | 13\.   |      |
| NAME AND ADDRESS          |                         | TYPE   |      |
|                           |                         | OF     |      |
|                           |                         | REPORT |      |
|                           |                         | AND    |      |
|                           |                         | PERIOD |      |
|                           |                         | C      |      |
|                           |                         | OVERED |      |
|                           |                         |        |      |
|                           |                         | Final  |      |
|                           |                         | Report |      |
+---------------------------+-------------------------+--------+------+
|                           |                         | 14\.   |      |
|                           |                         | SPON   |      |
|                           |                         | SORING |      |
|                           |                         | AGENCY |      |
|                           |                         | CODE   |      |
+---------------------------+-------------------------+--------+------+
| 15\. SUPPLEMENTARY NOTES  |                         |        |      |
|                           |                         |        |      |
| This study was conducted  |                         |        |      |
| in cooperation with the   |                         |        |      |
| University of Washington  |                         |        |      |
| and the US Department of  |                         |        |      |
| Transportation            |                         |        |      |
+---------------------------+-------------------------+--------+------+
| 16\. ABSTRACT             |                         |        |      |
|                           |                         |        |      |
| The capability of         |                         |        |      |
| measuring vehicle lengths |                         |        |      |
| makes dual-loop detectors |                         |        |      |
| a potential real-time     |                         |        |      |
| truck data source for     |                         |        |      |
| freight movement studies. |                         |        |      |
| However, a previous study |                         |        |      |
| found that the dual-loop  |                         |        |      |
| detection system of the   |                         |        |      |
| Washington State          |                         |        |      |
| Department of             |                         |        |      |
| Transportation (WSDOT)    |                         |        |      |
| was not consistently      |                         |        |      |
| reporting accurate truck  |                         |        |      |
| volumes because of its    |                         |        |      |
| sensitivity setting       |                         |        |      |
| problems. Specifically,   |                         |        |      |
| the sensitivity problems  |                         |        |      |
| found were: (1)           |                         |        |      |
| sensitivity discrepancies |                         |        |      |
| between the two single    |                         |        |      |
| loops that form a         |                         |        |      |
| dual-loop detector; and   |                         |        |      |
| (2) unsuitable            |                         |        |      |
| sensitivity level         |                         |        |      |
| settings for both single  |                         |        |      |
| loops even when           |                         |        |      |
| discrepancies weren't     |                         |        |      |
| significant. Both         |                         |        |      |
| problems can result in    |                         |        |      |
| erroneous vehicle length  |                         |        |      |
| estimates and,            |                         |        |      |
| consequently, inaccurate  |                         |        |      |
| truck counts.             |                         |        |      |
|                           |                         |        |      |
| As an extension of the    |                         |        |      |
| previous study, this      |                         |        |      |
| research project          |                         |        |      |
| developed an algorithm    |                         |        |      |
| for the identification    |                         |        |      |
| and correction of such    |                         |        |      |
| loop sensitivity          |                         |        |      |
| problems. The algorithm   |                         |        |      |
| identifies dual-loop      |                         |        |      |
| sensitivity problems      |                         |        |      |
| using individual vehicle  |                         |        |      |
| information extracted     |                         |        |      |
| from high-resolution loop |                         |        |      |
| event data and corrects   |                         |        |      |
| dual-loop sensitivities   |                         |        |      |
| through a two-step        |                         |        |      |
| procedure: 1) remove the  |                         |        |      |
| sensitivity discrepancy   |                         |        |      |
| between the two single    |                         |        |      |
| loops and 2) adjust their |                         |        |      |
| sensitivities to the      |                         |        |      |
| appropriate level. The    |                         |        |      |
| algorithm was also        |                         |        |      |
| implemented in a computer |                         |        |      |
| application named the     |                         |        |      |
| Advanced Loop Event Data  |                         |        |      |
| Analyzer (ALEDA) system   |                         |        |      |
| for convenient usage.     |                         |        |      |
|                           |                         |        |      |
| Elimination of dual-loop  |                         |        |      |
| sensitivity problems      |                         |        |      |
| significantly enhances    |                         |        |      |
| the reliability of the    |                         |        |      |
| dual-loop detection       |                         |        |      |
| system and improves the   |                         |        |      |
| quality of truck volume   |                         |        |      |
| data. The findings and    |                         |        |      |
| products from this study  |                         |        |      |
| will help the WSDOT to    |                         |        |      |
| obtain more accurate      |                         |        |      |
| speed and truck volume    |                         |        |      |
| data from the existing    |                         |        |      |
| dual-loop detectors.      |                         |        |      |
+---------------------------+-------------------------+--------+------+
| 17\. KEY WORDS            |                         | 18\.   |      |
|                           |                         | DISTRI |      |
| Truck Data, Dual-Loop     |                         | BUTION |      |
| Detectors, Loop           |                         | STA    |      |
| Sensitivity, Speed        |                         | TEMENT |      |
+---------------------------+-------------------------+--------+------+
| 19\. SECURITY CLASSIF.    | 20\. SECURITY CLASSIF.  | 21\.   | 22\. |
| (OF THIS REPORT)          | (OF THIS PAGE)          | NO. OF | P    |
|                           |                         | PAGES  | RICE |
| None                      | None                    |        |      |
+---------------------------+-------------------------+--------+------+

**DISCLAIMER**

The contents of this report reflect the views of the authors, who are
responsible for the facts and accuracy of the data presented herein.
This document is disseminated through the Transportation Northwest
(TransNow) Regional Center under the sponsorship of the U.S. Department
of Transportation UTC Grant Program and through the Washington State
Department of Transportation. The U.S. Government assumes no liability
for the contents or use thereof. Sponsorship for the local match portion
of this research project was provided by the Washington State Department
of Transportation. The contents do not necessarily reflect the views or
policies of the U.S. Department of Transportation or Washington State
Department of Transportation. This report does not constitute a
standard, specification, or regulation.

**TABLE OF CONTENTS**

  ---------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------ ---
  Disclaimer............................................................................................\....ii                                                                                                     

  Table of Contents..................................................................\.................iii                                                                                                          

  List of Figures..........................................................................................v                                                                                                        

  List of                                                                                                                                                                                                           
  Tables..........................................................................\...\...\...\...\...\...\...vi                                                                                                    

  Executive Summary ...............................................................................vii                                                                                                              

                                                                                                                                                                                                                    

  CHAPTER 1                                                                                                        Introduction...........................................................................1         

  1.1                                                                                                              Research                                                                                         
                                                                                                                   Background...................................................\...\...\...\...\...\...\.....1     

  1.2                                                                                                              Problem Statement........................................................................3       

  1.3                                                                                                              Research Objectives......................................................................3       

                                                                                                                                                                                                                    

  CHAPTER 2                                                                                                        State of the Art.......................................................................5         

                                                                                                                                                                                                                    

  CHAPTER 3                                                                                                        Principles of Loop Detection.......................................................8             

  3.1                                                                                                              Loop Occupancy..........................................................................8        

  3.2                                                                                                              Loop Sensitivity Levels..................................................................9       

  3.3                                                                                                              Speed Measurements....................................................................10         

  3.4                                                                                                              Vehicle Length Measurements .................................\........................11         

                                                                                                                                                                                                                    

  CHAPTER 4                                                                                                        Research Approach.........................................................\........12            

  4.1                                                                                                              Identification of Dual-Loop Sensitivity Discrepancies.............................12             

  4.2                                                                                                              Detection of Dual-Loop Incorrect Sensitivity Levels......\.........................16            

  4.3                                                                                                              Development of An Algorithm for Correcting Dual-Loop Sensitivity                                 
                                                                                                                   Problems...........................................................................\........19   

                                                                                                                                                                                                                    

  CHAPTER 5                                                                                                        Development of the Advanced Loop Event Data Analyzer System...\......22                          

  5.1                                                                                                              Loop Event Data...\.......................................................................23     

  5.2                                                                                                              System Design and Components......................................................24             

  5.3                                                                                                              System Implementation.................................................................26         

  5.4                                                                                                              User Interface............................................................................27     

  5.5                                                                                                              Measurements of Speed and Vehicle Lengths.................................\......29              

                                                                                                                                                                                                                    

                                                                                                                                                                                                                    

                                                                                                                                                                                                                    

                                                                                                                                                                                                                    

  CHAPTER 6                                                                                                        System Testing and Discussion.........\...............\............................32            

  6.1                                                                                                              Test Data Collection\...............\.......................................................32   

  6.2                                                                                                              Data Extraction...............................................................\............34    

  6.3                                                                                                              System Testing...........................................................................35      

  6.4                                                                                                              Evaluating the Effectiveness of the Algorithm in Identifying Dual-Loop Sensitivity               
                                                                                                                   Problems.....................................................................36                  

  6.5                                                                                                              Evaluating the Effectiveness of the Algorithm in Correcting Dual-Loop Sensitivity                
                                                                                                                   Problems.....................................................................41                  

  6.6                                                                                                              Truck Data................................................................................44     

  6.7                                                                                                              Analysis Results Summary.............................................................46          

                                                                                                                                                                                                                    

  CHAPTER 7                                                                                                        Conclusions and Recommendations.............................................47                   

  7.1                                                                                                              Conclusions...........................................................................\....47    

  7.2                                                                                                              Recommendations for Further Study.............................................\.....48           

                                                                                                                                                                                                                    

  Acknowledgements.........................................................................................50                                                                                                       

                                                                                                                                                                                                                    

  Bibliography .................................................................................................51                                                                                                  

                                                                                                                                                                                                                    

                                                                                                                                                                                                                    
  ---------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------ ---

**LIST OF FIGURES**

  ---------- ---------------------------------------------------------------------------------------------------------
  Figure 4-1 The M Loop Is Over-Sensitive and the S Loop Is at the Correct Sensitivity Level\......14

  Figure 4-2 The M Loop Is at the Right Sensitivity Level and the S Loop Is Over-Sensitive........15

  Figure 4-3 SV Length Histogram of Too-High Dual-Loop Sensitivity.................................18

  Figure 4-4 SV Length Histogram of Too-Low Dual-Loop Sensitivity..............................\....18

  Figure     Sample File of Event Data....................................................................\....23
  5-1.       

  Figure     ALEDA's Component Connections......\......................................................25
  5-2.       

  Figure 5-3 ALEDA's Data Flow.............................................................................26

  Figure 5-4 ALEDA User Interface for Collecting Traffic Data..........................................27

  Figure 5-5 ALEDA User Interface for Solving the Dual-Loop Sensitivity Problems.............\....28

  Figure 5-6 ALEDA's Flow Chart for Speed and Length
             Calculations.....................................................................................\...30

  Figure 6-1 Dual-Loop On-Time Differences for Lane 1 at ES-172R..............................\...\...37

  Figure 6-2 Dual-Loop On-Time Differences for Lane 2 at ES-172R ..................................37

  Figure 6-3 Dual-Loop On-Time Differences for Lane 3 at ES-172R...................................38

  Figure 6-4 Estimated SV Median Length at ES-172R (SB I-5 & Metro Base).....................\...40

  Figure 6-5 Variation of DIFF% in Lane 3 at ES-172R (SB I-5 & Metro Base).......................42

             
  ---------- ---------------------------------------------------------------------------------------------------------

**LIST OF TABLES**

  -------- ----------------------------------------------------------------------------------------------------------- ------
  Table    Threshold Values for Percentage Change of Inductance (Eight Level System)......\.....10                     
  3-1                                                                                                                  

  Table    Threshold Values for Percentage Change of Inductance (Sixteen Level System)........10                       
  3-2                                                                                                                  

  Table    TDAD Volume Data at the ES-172R Station (NB I-5 & Metro Base) on November 28^th^,                           
  6-1      2004..............................................................................................\....32   

  Table    TDAD Volume Data at the ES-137R Station (NB I-5 & NE 45^th^ St.) on November 28^th^,                        
  6-2      2005..............................................................................................\....33   

  Table    Comparison of Vehicle Count Data at the ES-137R Station (NE I-5 & NE 45^th^ NB) on November 30,             
  6-3      2005...............................................................................36                       

  Table    The Variation of DIFF% for Lane 3 at the ES-172R Station (NB I-5 & Metro                                    
  6-4      Base).............................................................................................\....42   

  Table    TDAD Volume Data at the ES-137R Station (NB I-5 & NE 45^th^ NB) on December 2,                              
  6-5      2005.................................................................................................43     

  Table    Vehicle Count Data for Lane 1 at ES-137R (SB I-5 & NE 45^th^ St. NB) BEFORE and AFTER the Sensitivity       
  6-6      Tune-Up............................................................\.....44                                 

                                                                                                                       

                                                                                                                       

                                                                                                                       

                                                                                                                       

                                                                                                                       

                                                                                                                       

                                                                                                                       

                                                                                                                       

                                                                                                                       
  -------- ----------------------------------------------------------------------------------------------------------- ------

**EXECUTIVE SUMMARY**

A previous study found that the dual-loop detection system of the
Washington State Department of Transportation (WSDOT) was not
consistently reporting accurate truck volumes due to dual-loop
sensitivity problems. Two common types of sensitivity problems found
were: (1) sensitivity discrepancies between the two single loops that
form a dual-loop detector; and (2) unsuitable sensitivity level settings
for both single loops. Both problems can result in erroneous vehicle
length estimates and, consequently, inaccurate truck counts.

This research project developed, tested, and evaluated an algorithm for
identifying and correcting dual-loop sensitivity problems. The algorithm
identifies dual-loop sensitivity problems using individual vehicle
information extracted from high-resolution loop event data and corrects
dual-loop sensitivities through a two-step procedure: 1) remove the
sensitivity discrepancy between the two single loops; and 2) adjust
their sensitivities to the correct level. The algorithm was also
implemented in a computer application named the Advanced Loop Event Data
Analyzer (ALEDA) system for convenient usage.

The algorithm was tested at two dual-loop stations on the Interstate-5
corridor in the Greater Seattle area. The ALEDA system was applied to
identify and correct the dual-loops with sensitivity problems at these
two stations. The tests were conducted for approximately 24 hours at
each loop station to check whether the severity of the sensitivity
problems was affected by traffic conditions. The analysis results showed
that the proposed algorithm was effective in identifying and fixing the
dual-loop sensitivity problems and therefore could improve the
performance and effectiveness of the WSDOT dual-loop detection systems.

The principal findings of this research are as follows:

1)  The same sensitivity level settings at the Detector Electronic Units
    (DEUs) cannot assure that the on-times (an on-time is defined as the
    duration that a loop detector is occupied by a vehicle) measured by
    the upstream loop (the M loop) and the downstream loop (the S loop)
    of a dual-loop detector are identical. Large on-time differences
    between the M and S loops can cause erroneous measurements of
    vehicle speed and hence vehicle length.

2)  Sensitivity discrepancies between the M and S loops may be
    eliminated by adjusting the sensitivity level settings at the DEUs.
    ALEDA makes this process easy.

3)  The on-time difference between the M and S loops is also impacted by
    other factors. For example, it can be intermittently large because
    of temporary cross-talk impacts.

4)  The sensitivities of the M and S loops may be the same (average
    on-time difference = 0) while both loops are at incorrect
    sensitivity levels (i.e., both single loops are over sensitive or
    under sensitive). In this case speed measurements are accurate, but
    measurements of vehicle lengths are incorrect because the on-times
    measured by the M and S loops are either too long or too short.
    Vehicle classification based on these imprecise lengths will be
    incorrect. Features of vehicle length distribution can be used to
    set the two single loop sensitivities to the correct sensitivity
    level.

5)  The proposed algorithm and the ALEDA system were demonstrated to be
    effective for tuning dual loop detectors. Further improvements on
    ALEDA will make it a handy tool for loop detector maintenance staffs
    to identify and correct dual-loop sensitivity problems.

**CHAPTER 1 INTRODUCTION**

**1.1 RESEARCH BACKGROUND**

Real-time traffic data collected by loop detectors are a primary data
source for automated traffic monitoring or information systems (Al-Deek
1991; Chen and Chang 1993; Chen et al. 2001). The archived traffic data
are used for a variety of transportation applications including
transportation planning, infrastructure management, model calibration,
and traffic simulation and operations (Clark et al. 2001; Cleghorn et al
1991; Wang and Nihan 2003, 2004).

The Washington State Department of Transportation (WSDOT) has made an
enormous investment in the installation of loop detectors throughout the
freeway network in the Puget Sound region. Almost half of the
loop-detector stations in the central Seattle area freeway network are
equipped with dual-loop detectors for the purpose of measuring speed and
classified vehicle volume data. Vehicles are classified based on their
lengths, and the WSDOT dual-loop detection system assigns each vehicle
to one of the following four bins: (a) Bin 1 -- Passenger Cars (PCs),
pickups, and other smaller vehicles (length 26 ft or less); (b) Bin 2 --
single-unit trucks and small vehicles pulling trailers. (26 ft to 39
ft); (c) Bin 3 - combination trucks and buses (39 ft to 65 ft); and (d)
Bin 4 -- multi-trailer trucks (length greater than 65 ft). Since the
majority of vehicles in Bins 2 through 4 represent trucks, correct bin
volume counts in those bins should yield reliable truck flow data along
the freeway network. However, a preliminary study on Interstate-5 (Zhang
et al. 2003) found that the WSDOT dual-loop detection system was not
consistently reporting accurate truck volumes. In that study, the
accuracy of dual-loop collected bin volumes was evaluated using video
captured ground-truth data, and the major findings included:

-   Dual-loop detectors under-count vehicle volumes. This is a very
    common problem in the dual-loop detection system. More than 80
    percent of the dual-loop detectors have significant under-count
    errors.

-   Dual-loop detectors misclassify vehicles across bins, especially
    between Bins 1 and 2, and Bins 3 and 4. For off-peak hours, observed
    errors in truck misclassifications ranged from 30 to 41 percent and,
    for peak hours, observed errors in bin assignments for trucks ranged
    from 33 to 55 percent.

The major cause of the poor performance of dual-loop detectors appears
to be the remarkable lane-occupancy difference between the direct
measurements of the two single-loop detectors that form a dual loop
(Zhang et al. 2003). When the occupancy difference calculated from the
direct measurements of the two single loops exceeds a certain threshold,
the current WSDOT dual-loop algorithm discards the vehicle from the data
set before the length calculation and classification operations are
performed. Such occupancy discrepancies can be generated by any of the
following factors:

-   Incorrect mode setting for one or both of the single loop detectors
    in a dual loop system.

-   Inconsistent sensitivity levels for the two loops.

-   Other hardware malfunctions.

Since the sensitivity of a single-loop detector is determined by many
factors (e.g. maker-specific standards, roadway material, construction
method, and environmental conditions) in addition to operator judgement
for the operator-set sensitivity level, it is not an easy job to place a
single-loop detector's sensitivity at the appropriate level. The
empirical procedures currently used for loop sensitivity adjustments
cannot usually achieve favorable results. A new tool for identifying and
correcting dual-loop sensitivity problems is desired.

**1.2 PROBLEM STATEMENT**

Almost half of the loop stations in the central Seattle-area freeway
network are equipped with dual-loop detectors for collecting speed and
truck volume data. These dual-loop detectors should be a reliable data
source for Advanced Traffic Management Systems (ATMS) and Advanced
Traveler Information Systems (ATIS). However, previous research (Zhang
et al. 2003) found that the WSDOT dual-loop detection system was not
consistently reporting accurate truck volumes. More than 80 percent of
the dual-loop detectors had significant under-count errors due to large
differences (\>10 percent) in vehicle on-times (an on-time is defined as
the duration that a loop detector is occupied by a vehicle) measured by
the two single loops that form each dual-loop detector. Zhang et al.
(2003) showed that this remarkable on-time difference was primarily a
result of dual-loop sensitivity problems. Therefore, fixing such
sensitivity problems is an urgent task for collecting reliable truck and
speed data using existing dual-loop detectors. The proposed algorithm
would use high resolution (60 Hz) loop event data to identify and repair
such dual-loop sensitivity problems. Once a dual-loop detector's
sensitivity is correctly tuned, it will be a reliable source for
real-time speed and truck data.

**1.3 RESEARCH OBJECTIVE**

This study aims at developing a solution to detect and fix the two major
types of sensitivity problems with dual-loop detectors: sensitivity
discrepancy between the two single loops that form a dual loop detector
and the incorrect sensitivity levels for the two single loops.
Specifically, there are two major objectives for this research:

-   Develop an algorithm that can identify and correct dual-loop
    sensitivity problems using individual vehicle information extracted
    from high-resolution loop detector event data; and

-   Develop a computer system that can correctly tune dual-loop
    detectors by incorporating the proposed algorithm as its core
    component.

**cHAPTER 2 State of the Art**

Loop malfunctions are the major causes of errors in loop detector data.
Erroneous loop measurements seriously degrade the performance of ATMS
and ATIS. Several procedures have been proposed for detecting and
correcting loop malfunctions. In general, these procedures can be
classified into two categories based on the type of data used: (*1*)
validity test based on interval-aggregated loop data and (*2*)
high-resolution loop-event-data-based test. Sample studies of category
*(1)* include Chen and May (1987), Nihan et al. (1990), Jacobson et al.
(1990), Cleghorn et al. (1991), and Turner (2000). Data used for these
studies were interval loop measurements of 20 or 30 seconds. Traffic
variables calculated from aggregated volume and occupancy measurements
were compared with empirical thresholds for malfunction identification.
Since individual vehicle information is not available in interval loop
data, these aggregated data based tests can detect only certain types of
loop detector errors. For better error detections, high-resolution loop
event data are necessary. Loop event data contain important individual
vehicle data including actuation counts, arrival time, and departure
time. Such individual vehicle information can be used to pinpoint loop
detector malfunctions. Examples of category *(2)* studies are Coifman et
al. (2004) and Cheevarunothai et al. (2005).

Since dual-loop detector data are typically aggregated into 20- or
30-second intervals to save data storage space and communication
bandwidth, most traffic agencies currently use interval-aggregated loop
data for checking whether loop malfunctions exist. The current WSDOT
loop detection system aggregates loop measurements into 20-second
intervals for archival and analysis. Based on the WSDOT 20-second
interval data, a procedure to detect loop malfunctions was proposed by
Nihan et al. (1990). The procedure compares collected traffic counts and
volume-to-occupancy ratios with certain constant thresholds to determine
the reliability of loop data. Error suspected data are marked with
flags. The procedure is capable of filtering out serious malfunctions
such as those caused by short pulses and chattering, but is limited to
those visible from the aggregated data. Obviously, the loss of
individual vehicle information in the aggregated data makes in-depth
analysis of loop malfunctions more complicated and at times impossible.

High-resolution loop event data became available due to new developments
in computer software and hardware technologies (Chen and May 1987;
Coifman 1999; Coifman et al. 2000; Zhang et al. 2003; and Cheevarunothai
et al. 2005). Such loop event data preserve information on individual
vehicles and allow in-depth analyses of loop malfunctions. Nonetheless,
little has been accomplished in using event data to identify and correct
loop sensitivity problems.

Loop event data have been employed by the Berkeley Highway Laboratory
(BHL) for loop data quality evaluations. Chen and May (1987) developed a
procedure for verifying loop detector data using event data. Coifman and
Dhoorjaty (2004) made another step forward by developing detector
validation tests using individual vehicle information extracted from
event data (e.g., on-time, speed, length, and headway). The extracted
vehicle information was compared with corresponding pre-set constant
thresholds to identify erroneous loop data. These studies demonstrated
the values of using high-resolution event data for loop data error
identifications and corrections.

Since a standard loop detector station employs a Model 170 controller in
California, the California Department of Transportation (Caltrans) uses
the processing capability of the Model 170 controller to collect and
store event data from the field for the I-880 Field Experiment (Coifman
et al. 2000). Because of the limited computing power of a Model 170
controller, outputting 60 Hz event data obstructs the normal operation
of the controller. Thus, when a Model 170 controller is used for event
data collection by the BHL, the normal operation of the controller will
be interrupted.

To facilitate the event data collection process, a Detector Event DAta
Collection (DEDAC) system was developed at the University of Washington
(UW) in 2002 (Zhang et al., 2003). The system is capable of collecting
event data from the Input File of a control cabinet. Since the DEDAC
system taps loop events before they flow into the controller and relies
on its own computing power for data processing and storing, the
controller's normal operation will not be interrupted. The DEDAC system
makes loop event data collection cheap and easy. Using the event data
collected by the DEDAC system, Zhang et al. (2003) found that the major
cause of inaccurate dual-loop data was the incorrect sensitivity levels
of a dual-loop detector. An extended analysis confirmed this result. A
dual-loop sensitivity problem can result from any of the following two
scenarios: (1) the sensitivity level discrepancy between the two single
loops, and (2) unsuitable loop sensitivity levels on both single loops.

The accuracy of a loop detector's measurements is significantly
influenced by its sensitivity. For example, the higher the loop
sensitivity, the longer a vehicle's loop on-time. Consequently, both
types of validity tests may give erroneous conclusions on loop
malfunctions when sensitivity-influenced loop data are applied.
Therefore, a loop's sensitivity problems must be fixed before using its
data for loop malfunction detections. High-resolution loop event data
are a great information resource for identifying and correcting loop
sensitivity problems.

**CHAPTER 3 Principles of Loop Detection**

**3.1 LOOP OCCUPANCY**

Loop occupancy is defined as the percentage of time that a loop is
occupied by vehicles over a time period. It can be calculated by
dividing the sum of vehicle on-times by the time period length. The
measurement of an on-time starts when the front bumper of a vehicle
arrives at the leading edge of a single loop and ends when the rear end
of the vehicle passes the loop's lagging edge.

Each loop detector is a tuned electrical circuit where the loop wire is
the inductive element. Its inductance is represented by *L*. When a
vehicle drives over the loop wire, eddy currents are induced around the
peripheral metal of the vehicle. Although the iron mass of the vehicle's
engine, transmission, or differential will increase the loop inductance
due to the ferromagnetic effect, the decrease in inductance from the
eddy currents more than offsets the increase from the ferrous mass, and
the net effect of the vehicle's presence is an overall reduction in loop
inductance denoted by *∆L*. Therefore, when a vehicle is on top of a
loop detector, it decreases the inductance of the loop. This decrease in
inductance then triggers the Detector Electronic Unit (DEU)'s output
relay or solid state circuit which, in turn, switches the output voltage
to the controller to a low level (close to zero Volt Direct Current (V
DC)) signifying that a vehicle's presence has been detected (ITE 1997).

Traffic controllers located in the roadside control cabinets typically
scan loop detectors at a rate of 60 times per second (Coifman et al.
2000; Zhang et al. 2003)*.* Each scan results in a "loop occupied" or
"loop not occupied" response. A scan counter in the controller is
incremented once for each "loop occupied" response. A vehicle's on-time
in seconds can be converted from its Scan Counts (*SCs*):

$\text{Ontime} = \frac{\text{SCs}}{\text{60}}$ (3-1)

In the current WSDOT loop detection system, loop occupancy is calculated
as:

$\text{Occupancy} = \frac{\text{SCs}\ \text{in}\ a\text{20} - \text{second interval}}{\text{1200}} \ast \text{100}$
(3-2)

In this research, we use *SCs* of individual vehicles to calculate the
on-times and then use the calculated on-times for identifying and
correcting dual-loop sensitivity problems.

**3.2 LOOP SENSITIVITY LEVELS**

A loop detector identifies the presence of a vehicle by comparing the
relative change of loop inductance (*∆L/L*) caused by a vehicle
traversing over a loop with a threshold value which is often referred to
as loop sensitivity level. The minimum percentage change of inductance
(*min* *∆L/L*) for a DEU to respond is adjustable. A typical DEU used on
freeways has eight or sixteen levels of sensitivity settings. The
threshold values for these sensitivity levels are shown in Tables 3-1
and 3-2. The normal sensitivity levels for the eight- and
sixteen-sensitivity-level DEU to set are 4 and 10, respectively.
According to Tables 3-1 and 3-2, the higher the sensitivity level, the
easier the detection of a vehicle. However, an over sensitive loop may
have other detection problems, such as detecting vehicles on adjacent
lanes. The main consideration for having several sensitivity levels
available with a DEU is to allow the optimal sensitivity setting over a
variety of loop configurations.

**Table 3-1. Threshold Values for Percentage Change of Inductance (Eight
Level System)**

  ----------------------------------- -----------------------------------
  Sensitivity Level                   *min ∆L/L*

  **7**                               0.01%

  **6**                               0.02%

  **5**                               0.04%

  **4**                               0.08%

  **3**                               0.16%

  **2**                               0.32%

  **1**                               0.64%

  **0**                               1.28%
  ----------------------------------- -----------------------------------

**Table 3-2.** **Threshold Values for Percentage Change of Inductance
(Sixteen Level System)**

  ----------------- ----------------- ----------------- -----------------
  Sensitivity Level *min ∆L/L*        Sensitivity Level *min ∆L/L*

  **15**            0.010%            7                 0.160%

  **14**            0.014%            6                 0.226%

  **13**            0.020%            5                 0.320%

  **12**            0.028%            4                 0.453%

  **11**            0.040%            3                 0.640%

  **10**            0.057%            2                 0.905%

  **9**             0.080%            1                 1.280%

  **8**             0.113%            0                 OFF
  ----------------- ----------------- ----------------- -----------------

**3.3 SPEED MEASUREMENTS**

A dual-loop detector system consists of two single loops separated by
several feet. According to the WSDOT convention, the upstream single
loop is called the M loop and the downstream single loop is called the S
loop. Since the leading edge to leading edge distance between the M loop
and the S loop (*Dist~MS~*) is predetermined and the traversal time
between the two loops can be directly measured, a dual loop detector can
output vehicle speed. In Washington State, *Dist~MS~* is normally 16
feet (4.8 meters). If a vehicle arrives the M loop at *t~m-on~* and the
S loop at *t~s-on~*, then its speed can be calculated as

$\text{Speed} = \frac{\text{Dist}_{\text{MS}}}{(t_{s - \text{on}} - t_{m - \text{on}})}$
(3-3)

**3.4 VEHICLE LENGTH MEASUREMENTS**

A dual-loop detector classifies vehicles into bins according to their
lengths. A vehicle's length can be estimated from its speed and on-times
measured by the M and S loops. The on-times for the M and S loops
(*Ontime~M~ and Ontime~S~, respectively*) can be expressed as

$\text{Ontime}_{M} = t_{m - \text{off}} - t_{m - \text{on}}$ (3-4)

$\text{Ontime}_{S} = t_{s - \text{off}} - t_{s - \text{on}}$ (3-5)

The WSDOT's dual loop algorithm uses Equation (3-6) for vehicle length
calculation:

$\text{Length} = \left\lbrack \text{Speed}\text{ * }\left( \frac{\text{Ontime}_{M} + \text{Ontime}_{S}}{2} \right) \right\rbrack - \text{Loop Length}$
(3-6)

Since *Ontime~M~* and *Ontime~S~* may be different due to possible speed
variations over *Dist~MS~*, the mean on-time value is used for
calculating vehicle length to minimize the estimation error. The loop
length term is included in Equation (3-6) because the on-time of a
vehicle is measured from the moment the vehicle's front bumper reaches
the leading edge of a single loop to the time its rear end leaves the
lagging edge of the loop. Hence, the loop length is subtracted from the
loop detector's effective vehicle lengths to give the actual vehicle
length in Equation (3-6).

**CHAPTER 4 RESEARCH APPROACH**

Two major types of dual-loop sensitivity problems considered in this
study are: 1) sensitivity discrepancy between the M and S loops; and 2)
incorrect sensitivity levels for both M and S loops, even though no
discrepancy is observed. These are the two main causes of imprecise
speed and vehicle length measurements in the existing dual-loop
detection systems. Remedial solutions to the first and second
sensitivity problems are described in Sections 4.1 and 4.2,
respectively. An algorithm developed for identifying and fixing the two
sensitivity problems based on the remedial solutions is presented in
Section 4.3.

**4.1 IDENTIFICATION OF DUAL-LOOP SENSITIVITY DISCREPANCIES**

To achieve accurate measurements of vehicle speed, the sensitivity
levels of the M and S loops must be approximately the same. However, the
same sensitivity level settings on the DEUs of the M and S loops may not
assure the same sensitivity level between the two single loops because a
loop's inductance is also affected by its environmental conditions (such
as temperature, humidity, road pavement structure and conditions, etc.)
which may be different from location to location. Variation in
environmental conditions can cause sensitivity inconsistencies between
the M and S loops, and therefore imprecise calculations of speed. The
greater the difference in sensitivity between the M and S loops, the
greater the inaccuracy of the speed measurements.

Since *Dist~MS~* (the leading edge to leading edge distance between the
M and S loops) is small (about 16 ft or 4.88 meters), speed is
considered to be constant when a vehicle traverses over the M and S
loops. With a constant speed, the M and S loops should have identical
on-time measurements, i.e., their on-time differences should be zero if
their sensitivities agree. Therefore, an on-time difference can be an
indicator of a sensitivity discrepancy between the M and S loops.

The percent of on-time difference can be calculated as

$\text{On-Time Difference}(\text{\%}) = \frac{\left( \text{Ontime}_{M} - \text{Ontime}_{S} \right)}{\text{Ontime}_{M}} \ast \text{100}$
(4-1)

In accordance with Equation (4-1), if the M loop is more sensitive than
the S loop, the on-time difference will be positive, and vice versa.
Therefore, we can infer whether a sensitivity discrepancy problem exists
and its possible causes from the calculated on-time difference. In this
research, if only one of the loops has an incorrect sensitivity level,
the sensitivity discrepancy problems are attributed to four main cases:

1)  *Over-Sensitive M Loop*

> If the M loop's sensitivity is higher than the appropriate level, a
> vehicle can be detected before it reaches the leading edge of the M
> loop. This is illustrated in Figure 4-1. In this case, because the
> travel time measurement from the leading edge of the M loop to the
> leading edge of the S loop is longer than the measurement that would
> be made if the M and S loops had the same sensitivity levels, the
> measured speed will be lower than the actual speed.

2)  *Over-Sensitive S Loop*

> If the S loop's sensitivity is higher than the appropriate level, a
> vehicle can be detected before it reaches the leading edge of the S
> loop. This is illustrated in Figure 4-2. In this case, the travel time
> measurement from the leading edge of the M loop to the leading edge of
> the S loop is shorter than that the measurement that would be made if
> the M and S loops had the same sensitivity levels. Hence, the measured
> speed will be higher than the actual speed.

3)  *Under-Sensitive M Loop*

> Similarly, if the M loop's sensitivity is below the appropriate level,
> a vehicle cannot be detected until it passes the leading edge of the M
> loop. The travel time measurement used for the speed calculation will
> be shorter than the actual value and, therefore, speed will be
> over-estimated.

4)  *Under-Sensitive S Loop*

> Conversely, speed will be under-estimated if the S loop's sensitivity
> is below the appropriate level because the travel time measurement
> used for the speed calculation will be longer than the actual value in
> this case.

![](media/image1.wmf){width="5.072916666666667in"
height="2.3541666666666665in"}

**Figure 4-1. The M Loop Is Over-Sensitive and the S Loop Is at the
Correct Sensitivity**

**Level**

![](media/image2.wmf){width="4.90625in" height="2.5104166666666665in"}

**Figure 4-2.** **The M Loop Is at the Right Sensitivity Level and the S
Loop Is Over-Sensitive**

According to the WSDOT dual-loop algorithm, if a vehicle's on-time
difference is beyond ±10 percent, it is not classified (assigned to a
bin) although its speed is still recorded. This threshold was originally
set to screen out possible measurement errors from vehicles crossing the
M loop in one lane and then the S loop in a different lane. However,
Zhang et al (2003) found that the majority of the vehicles screened out
by this ±10 percent criterion at dual-loop stations with serious under
count problems (where the total bin volume measurements were
significantly lower than the volumes counted by either the M loop or the
S loop) were actually not lane-changing vehicles. They also found that
sensitivity discrepancy was the main reason for the dual-loop
under-count problems at the observed loop stations. Consequently, if the
sensitivity discrepancy problem can be solved, dual-loop detectors will
provide better speed and bin volume measurements.

**4.2 DETECTION OF DUAL-LOOP INCORRECT SENSITIVITY LEVELS**

Measurements of vehicle speed from dual-loop detectors will be accurate
once the sensitivity discrepancy problem is corrected. However, even if
the sensitivities of the M and S loops are consistent,
[both]{.underline} may be at an incorrect level, i.e.,
[both]{.underline} loops may be over sensitive or under sensitive. As
stated previously, incorrect sensitivity levels for a dual loop can
result in inaccurate on-time measurements. As shown in Equation (3-5),
the calculation of vehicle length is based on these on-time
measurements. Therefore, imprecise on-times will lead to erroneous
vehicle length estimates. For instance, overly high sensitivity levels
on both the M and S loops will produce lengthened on-time measurements
for both loops. Vehicle lengths calculated from these lengthened
on-times will be longer than the actual vehicle lengths. Similarly, if
the sensitivity levels of the M and S loops are not high enough, vehicle
on-time measurements will be shorter than the actual values. These
shorter-than-normal on-times will result in under-estimated vehicle
lengths. Since the WSDOT vehicle classification algorithm classifies
vehicles based on vehicle lengths, incorrect sensitivity levels of a
dual-loop detection system will cause misclassification of vehicles,
i.e., will assign vehicles to incorrect vehicle-length bins.

To identify an appropriate sensitivity level for the M or the S loop,
information on individual vehicle lengths is needed. However, it is very
difficult to obtain ground-truth length data for vehicles traveling on
freeways at a specific time period. Therefore, a statistical approach is
applied here using the Short Vehicle (SV) length distribution observed
by Wang and Nihan (2004).

According to Wang and Nihan's vehicle length distribution, SV
(corresponds to Bin-1 vehicles) lengths follow a normal distribution
with a mean = 15.21 ft (4.64 m) and a Standard Deviation (*SD*) = 2.20
ft (0.67 m). The small standard deviation implies that SV lengths change
narrowly around the mean. Because of this attribute, the length
information for SVs can be employed to trace a correct sensitivity level
without significant errors. In this research, the SV-length distribution
reported by Wang and Nihan (2004) was used as the ground-truth vehicle
length distribution for SVs.

A calculated length from a dual-loop detector will be precise only when
the vehicle speed and the on-times are accurate. Accurate speed and
on-time measurements require the sensitivities of the M and S loops to
be consistent and at the appropriate sensitivity level. To identify
whether or not the sensitivity levels for the M and S loops are
appropriate, the calculated SV lengths were compared with the ground
truth SV-length distribution. The comparison was based on the histogram
of calculated SV lengths and the histogram generated from the ground
truth SV-length distribution. When the histogram of SV lengths measured
by dual-loop detectors is significantly different from the ground truth
SV-length histogram, we can conclude that the sensitivity levels for the
M and S loops are incorrect.

When both loops have consistent but incorrect sensitivity levels, there
are two extreme cases for this dual-loop sensitivity problem. In the
first case, both the M and S loops have overly high sensitivity levels.
The histogram of measured SV lengths shifts to the right side of the
ground truth histogram as shown in Figure 4-3 because of unrealistically
large on-times on both the M and S loops. (In Figures 4-3 and 4-4, the
green bars stand for the ground truth SV length histogram, and the blue
lines represent the histogram of measured SV lengths.) Conversely, if
the sensitivity levels of both the M and S loops are too low, the
histogram of measured SV lengths shifts to the left side of the ground
truth histogram as shown in Figure 4-4 because of unrealistically short
on-times.

![](media/image3.wmf){width="5.001388888888889in"
height="3.060416666666667in"}

**Figure 4-3. SV Length Histogram of Overly-High Dual-Loop Sensitivity**

(Note: 1 ft = 0.305 m)

![](media/image4.wmf){width="4.709027777777778in"
height="3.1659722222222224in"}

**Figure 4-4. SV Length Histogram of Overly-Low Dual-Loop Sensitivity**

(Note: 1 ft = 0.305 m)

**4.3 DEVELOPMENT OF AN ALGORITHM FOR CORRECTING DUAL-LOOP SENSITIVITY
PROBLEMS**

As mentioned earlier, sensitivity discrepancies in a dual-loop detection
system can be detected by calculating on-time differences between the M
and S loops. If the on-time differences are close to zero, we can
conclude that a dual-loop detector does not have sensitivity discrepancy
problems. Thus, the dual-loop sensitivity discrepancy problem can be
solved by adjusting the sensitivity levels at the DEUs until the
on-times measured by both the M and S loops are the same or their
on-time differences are zero. For example, if the on-time differences
are positive, then the M loop is more sensitive than the S loop. We can
remove the sensitivity discrepancy by increasing the sensitivity of the
S loop and/or decreasing the sensitivity of the M loop. Similarly, if
the on-time differences are negative, then the M loop is less sensitive
than the S loop. To close the sensitivity gap, we can increase the
sensitivity of the M loop and/or decrease the sensitivity of the S loop.
Thus, adjusting loop sensitivity settings at the DEUs until the on-time
differences of a dual-loop detector are close or equal to zero can
eliminate the sensitivity discrepancy problem.

As explained in the previous section, we use a statistical approach
based on the SV-length distribution observed by Wang and Nihan (2004) to
check whether consistent sensitivity levels of the M and S loops are
appropriate. A comparison between the histogram of calculated SV-length
distribution and the histogram generated from the ground truth SV-length
data was employed to identify whether or not a dual-loop detector was at
an appropriate sensitivity level. When a dual-loop detector is at an
inappropriate sensitivity level, the modes of the two histograms will be
significantly different. To achieve an appropriate sensitivity level,
the sensitivity settings at the DEUs should be adjusted until the
histogram of SV-length measurements extracted from dual-loop event data
is similar to the histogram of the ground truth SV-length data.

Statistically, the larger the SV sample size, the more accurate the
comparison with the ground truth SV-length histogram. However, a large
sample size takes a long time to accumulate. Since the sensitivity
tuning process can take several iterations requiring multiple field
attempts, correction of sensitivity problems may take too long if large
samples of SV lengths are used. Therefore, a tradeoff between accuracy
and efficiency is necessary. Efficiency and accuracy can be balanced by
selecting an appropriate sample size for comparison with the
ground-truth SV length histogram. Our experiments and analyses indicated
that a sample of one hundred SV-length measurements was adequate for our
test locations. Analyses based on this sample size provided acceptable
accuracy, but further decreases in the sample size resulted in instable
results due to the random impacts from vehicle arrivals. Daytime traffic
volumes at our test locations were above 580 veh/hr/ln with
approximately 10 percent trucks or buses. With similar traffic streams,
it should not take more than thirteen minutes to collect one-hundred SV
lengths for any dual-loop detector. The time duration for a sample data
collection is still significant but tolerable.

For the current study, the histogram of this sample of SV lengths was
then compared to the histogram generated from the ground truth SV-length
data. At suitable sensitivity levels, the two histograms should match
closely. The goodness of fit between the measured SV length distribution
and the ground truth SV length distribution was determined by the
calculated sum of squared errors. Since SV lengths range from 9 ft (2.74
m) to 25 ft (7.62 m), the measured SV lengths can be placed into
seventeen categories with an increment of 1 ft (0.305m) between
consecutive categories. The error for each length category is defined as
the observed number of vehicles subtracted from the expected number of
vehicles. If the sum of squared errors over all 17 categories is smaller
than a specified threshold, we conclude that a particular dual-loop
system is at the correct sensitivity level. (Our experience showed that
400 is a reasonable threshold value.) Otherwise, we recommend
sensitivity adjustments to the two single loops. The sensitivity setting
adjustments for the M and S loops may create a new sensitivity
discrepancy problem. Before collecting another one hundred SV lengths
for a new test, the M and S loop sensitivity discrepancy must be
examined and corrected to make sure there is no sensitivity discrepancy
problem. To ensure that a dual-loop detector is at a correct sensitivity
level, the identification and correction steps for sensitivity
discrepancies and incorrect sensitivity levels should be applied
alternatively and iteratively until both sensitivity problems are
eliminated.

**CHAPTER 5 Development of the Advanced Loop Event Data Analyzer (ALEDA)
System**

High-resolution loop event data are not widely collected by existing
freeway data collection systems. One of the reasons for this is the need
to save disk space for data storage and bandwidth for data transmission
to Traffic System Management Centers (TSMCs). The WSDOT loop detection
system has event data available in the controllers but they are
discarded after loop measurements are aggregated into 20-second
intervals for archiving at the TSMC. Therefore, loop event data are not
logged or stored in the current WSDOT loop detection systems. To
accomplish event data collection, a complementary system that can be
applied at loop detector stations was required. The Detector Event DAta
Collection (DEDAC) system was developed to fulfill the requirement of
event data collection by the TransNow ITS Group at UW (Zhang et al.
2003). The DEDAC system was designed to execute on a desktop computer
with a Peripheral Component Interconnect (PCI) compliant data
acquisition card. However, because of the size of a desktop computer and
the available space in a controller cabinet, it was difficult to apply
the DEDAC system in the field. A portable laptop version of DEDAC was
developed to circumvent this problem.

A further improvement in event data collection and analysis is the newly
developed ALEDA system. The ALEDA system developed in this study is a
user-friendly, portable, and practical tool for event data collection,
analysis, and dual-loop detector tuning (Cheevarunothai et al. 2005). It
implements the algorithm described in Chapter 4 for sensitivity tuning
of dual-loop detectors. ALEDA utilizes the latest digital Input/Output
(I/O) technologies and is executable on a laptop computer running
Windows 2000 or XP operating systems. Implementation details of the
ALEDA system are presented in the follow sections of this chapter.

**5.1 LOOP EVENT DATA**

Loop event data are high-resolution loop measurements typically
collected at 60 Hz or higher (Coifman, 2004). Unlike the WSDOT 20-second
aggregated data, loop event data contain complete information on
individual vehicles, such as vehicle arrival and departure times, and
vehicle on-times. Therefore, loop event data are great information
sources for in-depth analyses of loop malfunctions.

![](media/image5.png){width="5.416666666666667in"
height="3.002083333333333in"}

**Figure 5-1. Sample File of Event Data**

By default, ALEDA collects loop event data at 60 Hz, the frequency
typically used by a controller to scan loop detectors. Loop event data
are stored in a comma delimited data file as shown in Figure 5-1. This
file contains sixteen columns of event data that correspond to the
readings of M and S loops of eight dual-loop detectors. For example, the
first and second columns from the left hand side of the file are for the
M and S loops, respectively, of the dual-loop detector in the first
traffic lane (the rightmost lane on freeways by the WSDOT convention).
Following the event data columns are four columns of time data for
recording the hour, minute, second, and millisecond of each poll.

**5.2 System Design and Components**

According to the Traffic Detector Handbook (ITE, 1997), a controller
collects loop detector data by reading the voltage signals from the
Input File of its control cabinet: a high voltage level (24 V DC)
represents the situation when no car is on top of a loop detector ("OFF"
condition), and a low voltage level (0 V DC) denotes the situation when
a car occupies a loop detector ("ON" condition). The status of a loop
detector at a particular moment is regarded as an event. As a loop
detector event data collection and analysis system, ALEDA needs to
detect such events and record them at 60 Hz or higher. To realize these
functions, ALEDA is designed to have the following three hardware
components:

-   *Laptop computer with Universal Serial Bus (USB) ports.*

> Recommended configurations of a laptop computer include Windows 2000
> or Windows XP operating system, Pentium 4 processor, and 512 MB of
> Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM).

-   *Digital Input/Output (I/O) Adapter.*

> This adapter is required to connect a laptop computer with a USB port
> at one end and to connect the Input File housed in a control cabinet
> at the other end. In our design, we selected the SeaLINK ISO-16
> Isolated Inputs Digital Interface Adapter (please refer to
> <http://www.sealevel.com/uploads/manuals/8207.pdf> for details). It
> has two ports with 8 input channels for each port. The turn-on logic
> voltage is 3.8 V DC.

-   *Cable Connections.*

> Normal 24-guage cables are used to connect the laptop computer, the
> digital I/O adapter, and the Input File in a control cabinet.

To detect signal voltages and transform them into binary values, ALEDA
directly connects the digital I/O adapter to the Input File as shown in
Figure 5-2. By this connection, ALEDA taps loop event data from the
Input File without disturbing the normal operation of the controller.
The digital I/O adapter uses 3.8 V DC to classify voltage signals into
high or low levels. For instance, a 2 V DC signal is assigned to the low
level category because it is lower than the 3.8 V DC threshold. ALEDA
can poll the data address of the digital I/O adapter at 60 Hz or higher.
The collected data are managed in an internal array. These data can
serve as inputs for real-time calculations and analyses of individual
vehicle on-times, speeds, lengths, and on-time differences or be
recorded to a user-specified text file for archiving. ALEDA's data flow
is illustrated in Figure 5-3.

![](media/image6.wmf){width="6.238194444444445in"
height="2.6430555555555557in"}

**Figure 5-2. ALEDA's Component Connections**

**Figure 5-3. ALEDA's Data Flow**

**5.3 System Implementation**

The ALEDA system was developed in the C# programming language with the
help of the Microsoft Visual C# .NET technologies and additional
Universal Library documents from the manufacturer of the digital I/O
adapter. It is a laptop based computer application to facilitate loop
event data collection, analysis, and dual-loop detector sensitivity
tune-ups.

Since a controller scans a loop detector at 60 Hz to provide inputs to
various control and monitoring algorithms, the ALEDA system should have
the capability of polling the data at the digital I/O adapter at 60 Hz
or higher to meet practical needs. A high-resolution timer that can
raise events at user-defined time intervals is needed to fulfill this
requirement. The multimedia timer optimized for use in Windows
applications in the Visual C#.NET technology was applied in ALEDA. The
multimedia timer provides the greatest degree of timing accuracy,
allowing applications to schedule timing events at a high resolution.
The timing resolution of the ALEDA system can be as high as 80 Hz.

**5.4 USER INTERFACE**

The user interface of ALEDA has two main control windows: 1) Traffic
Data Window, and 2) Sensitivity Data Window.

**[5.4.1 Traffic Data Window]{.smallcaps}**

The traffic data window (shown in Figure 5-4) displays, in real-time,
the status of each single-loop and a set of commonly desired traffic
data, such as vehicle count, lane occupancy, speed, and vehicle length.
A group of system controls are available for this window. Users can
record loop station name, register data collection date and time, and
specify a directory for saving the event data file.

![](media/image7.png){width="6.25in" height="4.704166666666667in"}

**Figure 5-4.** **ALEDA User Interface for Collecting Traffic Data**

**[5.4.2 Sensitivity Data Window]{.smallcaps}**

The sensitivity data window (Figure 5-5) shows real-time vehicle
information related to loop sensitivity check up and adjustment, such as
vehicle on-times, percentages of on-time differences between the M and S
loops, the curve of on-time difference changes, the histogram of
calculated SV lengths, and the ground-truth SV length histogram from
Wang and Nihan (2004). Such sensitivity data are indispensable for
detecting and correcting dual-loop sensitivity problems.

![](media/image8.wmf){width="6.877777777777778in"
height="4.876388888888889in"}

**Figure 5-5.** **ALEDA User Interface for Solving the Dual-Loop
Sensitivity Problems**

**5.5 MEASUREMENTS OF SPEED AND VEHICLE LENGTHS**

The flow chart of the algorithm for calculating speed and vehicle length
is shown in Figure 5-6. Speed is calculated from *Dist~MS~*, and the
time differences between the arrival time at the leading edge of the M
loop (*tm-on*) and the arrival time at the leading edge of the S loop
(*ts-on*), as shown in Equation (3-3). In the WSDOT's loop detection
system, the default value of the distance between the leading edge of
the M loop and the leading edge of the S loop is 16 feet (4.8 meters).
So Equation (3-3) can be rewritten as Equation (5-1):

$\text{Speed} = \frac{\text{16}}{(t_{s - \text{on}} - t_{m - \text{on}})}$
(5-1) Since lane-changing vehicles at the dual loop location may not
have the correct arrival time measured by the M and S loops, such
vehicles should be discarded from length calculations and length-based
classifications. To screen out lane-changing vehicles, ALEDA will match
*tm-on* with *ts-on* using a set of condition checks. There are three
possible conditions:

-   Condition one: a vehicle traverses over both the M and S loops.
    ALEDA will match its *tm-on* with the *ts-on* detected right after
    the *tm-on* is recorded.

-   Condition two: a vehicle traverses over only the M loop but not the
    S loop. In this case, no *ts-on* match can be found for the
    vehicle's *tm-on* within a reasonable time window. The *tm-on* will
    be dropped from speed and length calculations.

-   Condition three: a vehicle runs over only the S loop but not the M
    loop. ALEDA will reject the *ts-on* because of it cannot be used as
    a match for any *tm-on*.

The above condition checks ensure that only vehicles with paired *tm-on*
and *ts-on* will be used for speed calculations and length-based vehicle
classifications.

**Figure 5-6. ALEDA's Flow Chart for Speed and Length Calculations**

An individual vehicle's length is estimated immediately after its speed
is calculated. Equation (5-2) shows how the vehicle length can be
calculated for a 6 ft × 6 ft (1.8 m × 1.8 m) loop:

$\text{Length} = \left\lbrack \text{Speed}\text{ * }\left( \frac{\text{OntimeM} + \text{OntimeS}}{2} \right) \right\rbrack - 6$
(5-2)

Vehicle length is estimated from speed and averaged on-times on the M
and S loops. The on-times on the M loop are calculated from the arrival
time (*tm-on*) and the departure time (*tm-off*). Similarly, the S
loop's on-time is obtained from *ts-on* and *ts-off*. Since the length
is calculated from the average on-time and the on-time depends largely
on the loop's sensitivity level, the sensitivity setting plays a big
role in the accuracy of vehicle length estimation. To accommodate more
general situations, ALEDA provides a function for users to specify the
distance between the M loop and the S loop, and input the loop length.

**CHAPTER 6 SYSTEM TESTING AND DISCUSSION**

**6.1 TEST DATA COLLECTION**

**6.1.1 Test Site Selection**

Data from dual-loop stations with sensitivity problems were needed to
verify the proposed algorithm. Based on a preliminary analysis using the
archived loop data and consultation with WSDOT technical supervisors,
two dual-loop stations were selected for this test: (1) ES-172R (located
at I-5 northbound & Metro Base); and (2) ES-137R (located at I-5
northbound & NE 45^th^ St.). Traffic counts for the duration from 6:00
am to 6:00 pm on Nov. 28, 2004, at these two sites were obtained from
the Traffic Data Acquisition and Distribution (TDAD) Website. Summary
statistics of these counts are shown in Tables 6-1 to 6-2. Each station
has three General Purpose (GP) lanes and one High Occupancy Vehicle
(HOV) lane (the left-most lane). Because of the unique characteristics
and low traffic volumes of the HOV lane, it was not included in this
test. The GP lanes were numbered 1 through 3 from right to left. Traffic
volumes recorded by the M loop, the S loop, and the dual-loop detector
(ST -- Speed Traps) are shown in the second, third, and fourth columns,
respectively, in Tables 6-1 and 6-2. The last column of the two tables
displays the percentage difference (DIFF%) between the volume counted by
the M loop and that counted by the ST. The DIFF% column indicates the
severity of the bin-volume undercount problem.

**Table 6-1. TDAD Volume Data at ES-172R Station (NB I-5 & Metro Base)
on November 28^th^, 2004**

  ------- -------- -------- -------- --------------------------------------
  Lane    M loop   S loop   ST       DIFF% = (M loop -- ST) / M loop \* 100

  1       15778    15872    14954    5.22

  2       14082    14686    12845    8.78

  3       10025    11186    567      **94.34**
  ------- -------- -------- -------- --------------------------------------

**Table 6-2. TDAD Volume Data at ES-137R Station (NB I-5 & NE 45^th^
St.) on November 28^th^, 2005**

  ------- -------- -------- -------- --------------------------------------
  Lane    M loop   S loop   ST       DIFF% = (M loop -- ST) / M loop \* 100

  1       8961     8991     8013     **10.58**

  2       14232    14283    13659    4.03

  3       15738    15613    14999    4.70
  ------- -------- -------- -------- --------------------------------------

As mentioned earlier, the current WSDOT dual-loop algorithm calculates
vehicle length only when the on-time difference between the M and S
loops is within ±10 percent. A dual-loop detector with severe
sensitivity problems should have its total classified vehicle volumes
significantly lower than that counted by either of its single loops.
Hence, traffic lanes with a DIFF% of 10 percent or higher were
considered to have serious sensitivity problems in this study. Traffic
lanes identified to have serious sensitivity problems were Lane 3 at
ES-172R and Lane 1 at ES-137R. For comparison purposes, all lanes at
these two stations, with or without sensitivity problems, were chosen
for this test.

The DEUs employed by the test loop stations were produced by either
Eberle Design Inc. or Peek Traffic Limited. These DEUs had either eight
levels of sensitivity (from level zero to seven) or sixteen levels of
sensitivity (from level zero to fifteen). For all
eight-sensitivity-level DEUs used by these selected stations, the
sensitivities were set at level two except for one single loop on Lane 3
at ES-172R where the sensitivity was set at level five. For other
single-loop detectors that use sixteen-sensitivity-level DEUs, the
sensitivities were all set at level ten.

As shown in Table 6-1, the DIFF% on Lane 3 of ES-172R was almost 95
percent. The WSDOT dual-loop algorithm allowed the dual-loop detector on
this lane to calculate vehicle lengths for only 5 percent of the total
lane traffic. The large sensitivity discrepancy between the M and S
loops may be caused by incorrect sensitivity level settings on one or
both of the two single loops: the sensitivity settings were level two
and level five for the M loop and the S loop, respectively. Whether or
not the S loop sensitivity was set too high must be investigated in this
test. Consistent sensitivity level settings on both the M and S loops do
not assure zero on-time differences between the two single loops because
a loop's inductance is affected by surrounding temperature, humidity,
road structure, etc. This implies that on-time differences may exceed 10
percent even when the M and S loop sensitivities are set at the same
level. A tool to identify and fix the dual-loop sensitivity problems is
needed. ALEDA can be used as such a tool.

**6.1.2 Data Collection**

Approximately 24 hours of event data were collected from the ES-172R
station to check the impact of different traffic conditions on the
sensitivity problems, and over two hours (76 minutes before the
sensitivity tune-up and 84 minutes after the tune-up of the dual-loop
detector) of event data were recorded at the ES-137R station to check
the performance and effectiveness of the proposed dual-loop tune-up
algorithm. The data collection at the ES-172R dual-loop station was
conducted from 10:25:42am on December 8^th^ to 9:41:17am on December
9^th^, 2004. At the ES-137R loop station, the before tune-up data were
collected from 10:36:56am to 11:52:09am on November 30, 2005 and the
after tune-up data were recorded from 12:24:58pm to 13:48:42am on the
same day. For verification purpose, the TSMC of the WSDOT recorded two
hours of video data (from 11:00am to 1:00pm on the data collection day)
at each selected station.

**6.2 data Extraction**

All the collected event data were analyzed to extract vehicle movement
data, such as volume count, loop occupancy, on-time difference, speed,
vehicle length, etc. The TSMC-recorded video data were manually
processed to extract bin-volume data for verification purposes. The
vehicle count data obtained from the video sets were used as ground
truth data for verifying the dual-loop tune-up algorithm.

**6.3 SYSTEM TESTING**

**[6.3.1 Connection Tests]{.smallcaps}**

Connection tests are required before ALEDA can be used to collect loop
event data and aid in tuning the sensitivity levels of dual loop
detectors. As the first step of ALEDA system testing, connection tests
were performed to ensure that ALEDA could work together with typical
traffic control devices, including the Model 332 cabinet and the Type
170 controller. ALEDA was connected to the Input File of a Model 332
cabinet at the Smart Transportation Applications and Research Laboratory
(STAR Lab) of the University of Washington. This connection follows the
design of ALEDA as specified in Figure 5-2. The connections for the
sixteen input channels of ALEDA were tested individually to ensure that
they were reliable. Then random combinations of the input channels were
tested to make sure that channels did not interfere with each other. The
test results showed that all sixteen input channels were successfully
connected to the cabinet and were able to work independently.

**[6.3.2 Performance Tests]{.smallcaps}**

Tests were also conducted to evaluate the performance of ALEDA.
Performance refers to whether loop event data can be collected and
analyzed at a desired frequency without significant delays. ALEDA was
used to collect and analyze event data for 24 hours at the ES-172R
station. Analyses of the collected event data showed that ALEDA had no
difficulties collecting and analyzing loop event data in real-time at a
frequency of 60 Hz. ALEDA may be able to work at an even higher
frequency, but this capability was not tested in this study.

**[6.3.3 Accuracy Test]{.smallcaps}**

An accuracy test was designed to check whether ALEDA can accurately
collect vehicle volumes based on loop event data. Traffic information
extracted from event data was compared with ground truth video data. The
30-minute test results (Table 6.3) indicated that vehicle volume data
collected by ALEDA are very reliable, with an accuracy of more than 99%
for all lanes at the ES-137R station.

**Table 6-3. Comparison of Vehicle Count Data at the ES-137R Station (NB
I-5 & NE 45^th^ NB) on November 30, 2005**

  ------------- ----------------- ----------------- ----------------------
  Lane          Video             Event Data        Accuracy%

  1             217               215               **99.08**

  2             419               423               **99.05**

  3             419               420               **99.76**
  ------------- ----------------- ----------------- ----------------------

**6.4 Evaluating the Effectiveness of the Algorithm in Identifying
Dual-Loop Sensitivity problems**

As mentioned earlier, the dual-loop sensitivity problems can be divided
into (1) the sensitivity discrepancy between the M and S loops, and (2)
the incorrect sensitivity levels of both the M and S loops.

**[6.4.1 Sensitivity Inconsistencies]{.smallcaps}**

Dual-loop sensitivity discrepancies are the primary cause of bin-volume
undercounts with WSDOT dual-loops. This is due to the fact that vehicles
with 10% or more on-time differences between the M and S loops are
dropped from the vehicle classification process. Since on-time
measurements depend on a loop's sensitivity level, the dual-loop on-time
differences identify the sensitivity discrepancies between the M and S
loops to a large extent. The on-time differences of all individual
vehicles detected by the dual loops at ES-172R in a 15-minute interval
are plotted in Figures 6-1 to 6-3 to illustrate the sensitivity
discrepancy problems at these dual loops.

![](media/image9.wmf){width="4.883333333333334in"
height="2.8118055555555554in"}

**Figure 6-1. Dual-Loop On-Time Differences for Lane 1 at ES-172R**

![](media/image10.wmf){width="4.883333333333334in"
height="2.720138888888889in"}

**Figure 6-2. Dual-Loop On-Time Differences for Lane 2 at ES-172R**

####  

![](media/image11.wmf){width="5.008333333333334in"
height="2.795138888888889in"}

**Figure 6-3.** **Dual-Loop On-Time Differences for Lane 3 at ES-172R**

In Figure 6-1, the plotted on-time differences on Lane 1 at the ES-172R
station are within ±10 percent for most detected vehicles. This conforms
to the small value of DIFF% (5.22 percent) in Table 6-1. The DIFF% for
Lane 2 (8.78 percent), however, is higher than that for Lane 1 (5.22
percent). This corresponds to a higher severity of sensitivity
discrepancy problems that can be perceived in Figure 6-2. However, the
average 15-minute on-time difference is still within the ±10 percent
range according to the trendline, in spite of the increased number of
on-time differences that exceed the ±10 percent range compared to
Lane 1. The lane with the worst sensitivity discrepancy problems at the
ES-172R station is Lane 3. The on-time differences are less than -10
percent in most cases and the average 15-minute on-time difference is
outside the ±10 percent range (Figure 6-3). This identified sensitivity
problem is clearly reflected by the high DIFF% value (94.34 percent) in
Table 6-1.

Based on the above analyses, we can conclude that the on-time
differences calculated by the proposed algorithm serve as a good
indicator of dual-loop sensitivity discrepancy problems. The graphical
displays of on-time differences on the user interface of ALEDA provide
important information to loop maintenance staffs on whether or not a
dual-loop detection system has sensitivity discrepancies.

**6.4.2 [Incorrect Sensitivity Levels]{.smallcaps}**

In addition to sensitivity discrepancies between the M and S loops, dual
loops may have consistent single loop sensitivity levels that are,
nevertheless, incorrect. This situation may also lead to vehicle
misclassifications. Incorrect sensitivity levels of both M and S loops
may result in on-time measurements that are too long or too short.
Lengthened on-times will result in overestimated vehicle lengths and
shortened on-times will produce underestimated vehicle lengths.

The algorithm developed in this study uses the SV length distribution
observed by Wang and Nihan (2004) to check whether or not the
sensitivity level of a consistent dual-loop detector is appropriate. As
mentioned earlier, the algorithm will perform the analysis once one
hundred SV length data are recorded.

To illustrate how the algorithm detects an incorrect sensitivity level
for a consistently sensitive dual-loop detector, the median lengths of
every one hundred SVs on each lane of the ES-172R station were plotted
in Figure 6-4. Although the sensitivity settings at all eight-level DEUs
were at level two (the sensitivity setting at the S loop's DEU of Lane 3
was first changed from level five to two to eliminate the sensitivity
discrepancy between the M and S loops), it was obvious that the
calculated median vehicle lengths for Lane 2 (16.2 ft or 4.94 meters)
and Lane 3 (16.0 ft or 4.88 meters) were longer than that for Lane 1
(15.5 ft or 4.73 meters). This illustrated the fact that the same
sensitivity level settings for different dual-loop detectors cannot
guarantee the same vehicle length measurements.

![](media/image12.wmf){width="6.215277777777778in"
height="3.120833333333333in"}

**Figure 6-4. Estimated SV Median Length at ES-172R (SB I-5 & Metro
Base)**

Because the median length of SVs for the ground truth data were about
15.5 ft or 4.73 meters and the median lengths of SVs for Lanes 2 and 3
were about 16.2 ft (4.94 meters) and 16.0 ft (4.88 meters),
respectively, measurements of vehicle lengths for Lanes 2 and 3 appeared
to be too long but still within a tolerable range (the sum of squared
errors of the 100-SV lengths was lower than 400 for both lanes). The
measurements of vehicle lengths for Lane 1 were also considered accurate
because its median lengths were 15.5 ft (4.73 meters). Therefore, the
sensitivity level settings of both the M and S loops in Lanes 1, 2 and 3
were considered appropriate. By comparing the median lengths of every
one hundred SVs with the ground-truth SV mean (since SV lengths are
normally distributed, its population mean and median should be the same)
and checking the sum of squared errors of the calculated SV lengths, the
algorithm can detect whether the sensitivity level of a dual-loop
detection system is appropriate.

**6.5 Evaluating the Effectiveness of the Algorithm in correcting
Dual-Loop Sensitivity problems**

**[6.5.1 Case One: the ES-172R Station]{.smallcaps}**

As mentioned earlier, the dual-loop on Lane 3 of ES-172R had serious
sensitivity discrepancy problems as indicated by its high DIFF% of
94.34%. The ALEDA system was applied to tune this dual-loop detector on
December 8, 2004. The tuned dual loop has the S loop sensitivity level
setting changed from five to two and the M loop sensitivity level stays
at two. To evaluate the tuning effect, the DIFF% values before and after
the tune up of this dual-loop detector were compared.

Fourteen days of data collection, including seven days before the tune
up (December 1, 2004 -- December 7, 2004) and seven days after the tune
up (December 9, 2004 -- December 15, 2004), were performed for the dual
loop on Lane 3 of station ES-172R and downloaded from the TDAD Website.
Differences between the M loop counts and the speed trap recorded total
bin volumes (DIFF%) were calculated and are presented in Table 6.4 and
Figure 6-5. The DIFF% values dropped from approximately 95% before the
sensitivity tune up to less than 2% after the tune up. This indicates
that almost all vehicles that drove over the dual-loop detector were
classified after the tune-up. Consequently, the performance of the dual
loop on Lane 3 of ES-172R improved significantly after the sensitivity
problems were solved.

![](media/image13.wmf){width="4.648611111111111in"
height="2.754166666666667in"}

**Figure 6-5. Variation of DIFF% in Lane 3 at ES-172R (SB I-5 & Metro
Base)**

**Table 6-4. The Variation of DIFF% for Lane 3 at the ES-172R Station
(NB I-5 & Metro Base)**

  ---------------------- ---------------- ---------------- ----------------
  Day                    DIFF%                             

                         Lane 1           Lane 2           Lane 3

  12/1/2004              5.06             7.31             94.90

  12/2/2004              6.49             9.84             94.66

  12/3/2004              6.70             8.28             95.15

  12/4/2004              3.57             7.58             95.66

  12/5/2004              3.88             7.27             95.02

  12/6/2004              6.49             9.80             94.73

  12/7/2004              7.30             10.52            95.12

  12/9/2004              9.54             15.18            1.08

  12/10/2004             8.71             17.29            1.22

  12/11/2004             4.90             14.06            0.65

  12/12/2004             4.47             13.23            -0.17

  12/13/2004             7.93             15.72            1.85

  12/14/2004             6.97             15.43            0.98

  12/15/2004             6.57             15.84            -0.13
  ---------------------- ---------------- ---------------- ----------------

At the ES-172R station, the sensitivity level settings of the Lane 1 and
Lane 2 dual loops were not changed in the tune up process on December 8,
2004 because neither of them was identified to have noticeable
sensitivity problems. However, after the Lane 3 dual-loop tune-up, the
DIFF% on Lane 1 and Lane 2 increased slightly as shown in Table 6-4.
These DIFF% changes might be caused by the changes in environmental
factors or by the change of sensitivity level of the Lane 3 dual loop.
Further research is needed to identify the impact of sensitivity
tune-ups to loop detectors in adjacent lanes.

**[6.5.2 Case Two: at the ES-137R Station]{.smallcaps}**

Similarly, the sensitivity problems on Lane 1 at the ES-137R station
were alleviated through a sensitivity tune-up using the ALEDA system.
The DIFF% of Lane 1 on November 28, 2005 was about 10.58% (Table 6-2),
even though the sensitivity level settings for the sixteen-level DEUs of
both the M and S loops were at level ten. The sensitivity tune-up
conducted on November 30, 2005 resulted in increasing the M loop
sensitivity setting to level eleven and keeping the S loop sensitivity
setting at level ten. The sensitivity tune-up was effective because the
DIFF% dropped to 6.31% (less than 10%) according to the dual-loop
measurements of December 2, 2005 (Table 6-5).

**Table 6-5. TDAD Volume Data at the ES-137R Station (NB I-5 & NE 45^th^
NB) on December 2, 2005**

  -------- -------- -------- -------- -------------------------------------
  Lane     M loop   S loop   ST       DIFF% = (M loop -- ST) / M loop \*
                                      100

  1        8575     8590     8034     **6.31**

  2        13643    13672    12803    6.16

  3        14678    14527    13987    4.71
  -------- -------- -------- -------- -------------------------------------

The test results in the above two cases demonstrated that the proposed
algorithm implemented in ALEDA is effective to identify and correct
sensitivity problems with the WSDOT dual-loop detectors.

**6.6 Truck Data**

The comparisons of vehicle counts for SVs (length ≤ 26ft) and trucks
(length \> 26ft) before and after the sensitivity tune-up of the Lane 1
dual loop at the ES-137R station (the only lane with a sensitivity
adjustment at ES-137R) are shown in Table 6.6. Traffic counts extracted
from the recorded videotape were used as the ground truth data for
comparisons with the TDAD data and the event data. Two data sets were
collected for the comparisons: one data set was from 2:00pm to 3:00pm on
May 16, 2002 (before the sensitivity tune-up) and the other was from
12:24pm to 13:24pm on November 30, 2005 (after the sensitivity tune-up).

**Table 6-6. Vehicle Count Data for Lane 1 at ES-137R (SB I-5 & NE
45^th^ St. NB) BEFORE and AFTER the Sensitivity Tune-Up**

+--------+-----------+--------+--------+----------+--------+---------+
|        | Vehicle   | Video  | TDAD   | Event    | VI-TD  | VI-EV   |
|        | Types     | (VI)   |        | Data     | Error  |         |
|        |           |        | (TD)   | (EV)     | (%)    | Error   |
|        |           |        |        |          |        | (%)     |
+--------+-----------+--------+--------+----------+--------+---------+
| **BE   | SV        | 446    | 447    | 447      | -0.22  | -0.22   |
| FORE** |           |        |        |          |        |         |
+--------+-----------+--------+--------+----------+--------+---------+
|        | Truck     | 18     | 13     | 17       | 27.78  | 5.56    |
+--------+-----------+--------+--------+----------+--------+---------+
|        | Total     | 464    | 460    | 464      | 0.86   | 0.00    |
+--------+-----------+--------+--------+----------+--------+---------+
| **A    | SV        | 653    | 651    | 653      | 0.31   | 0.00    |
| FTER** |           |        |        |          |        |         |
+--------+-----------+--------+--------+----------+--------+---------+
|        | Truck     | 30     | 24     | 29       | 20.00  | 3.33    |
+--------+-----------+--------+--------+----------+--------+---------+
|        | Total     | 683    | 675    | 682      | 1.17   | 0.15    |
+--------+-----------+--------+--------+----------+--------+---------+

Note: VI-TD Errors (%) = (Video Data -- TDAD Data) / Video Data

VI-EV Errors (%) = (Video Data -- Event Data) / Video Data

As shown in Table 6-6, trucks were seriously undercounted in the TDAD
data of Lane 1 at ES-137R before the sensitivity tune-up. The truck
counts on Lane 1 from the TDAD and ALEDA were smaller than the actual
truck counts (truck counts from the video data). The TDAD and ALEDA
recorded 13 and 17 trucks, respectively, instead of 18 trucks extracted
from the video data. The relative error of the TDAD truck counts was
about 28%, and that of the ALEDA truck counts was about 6%.

Comparisons of truck counts after the sensitivity tune-up showed that
the accuracy of the TDAD truck data had improved. The results in Table
6-6 show that the differences between the actual truck counts (from the
video data) and the truck counts from the TDAD data or the event data
were smaller than those before the sensitivity tune-up. The relative
error of the TDAD truck counts dropped from 28% to 20%. The relative
error of ALEDA collected truck counts also slightly decreased from 6% (1
missing vehicle out of 18 vehicles) to 3% (1 missing vehicle out of 30
vehicles).

In Table 6-6, it is obvious that the truck counts extracted from the
event data by ALEDA were much closer to the actual truck counts than
those from the TDAD data. We believe that the difference was due to the
way a vehicle with an on-time difference larger than 10% was treated in
the ALEDA algorithm. In the WSDOT dual-loop algorithm, such a vehicle
was discarded from classification. In the ALEDA application, the vehicle
was classified after the on-time difference was corrected.

A comparison of the TDAD truck data and the ground-truth (video) truck
data before and after the tune-up demonstrated the effectiveness of
using ALEDA for dual-loop sensitivity tune-ups. Once the sensitivity
problems are corrected, improved truck data can be collected from the
existing dual-loop detection systems. However, the 20% truck data error
after the tune up is still very significant. During the tune-up process,
we were not able to further lower this error through adjusting the
sensitivity levels. The fact that the ALEDA extracted truck volume data
that was very close to the ground-truth truck volume data indicates that
truck data can be further improved by employing a better dual-loop
algorithm that treats suspicious observations more effectively.

**6.7 analysis summary**

The results of the analysis can be summarized as follows:

1.  The algorithm developed in this study can efficiently detect and
    correct sensitivity discrepancies between the M and S loops.
    Consistent sensitivity between the M and S loops enhances the
    performance of the WSDOT's dual-loop detection system in measuring
    vehicle speed.

2.  Percentage differences between the volume counts on a single loop
    (the M or S loop) and a dual-loop system (the speed trap) were much
    smaller after the proposed algorithm was applied. Compared to the
    current WSDOT algorithm, this decreases the number of vehicles that
    will be discarded from classification based on the 10% threshold
    value of on-time difference.

3.  Incorrect sensitivity levels of two consistently sensitive single
    loops that form a dual-loop detector can be identified and
    eliminated using ALEDA. After the sensitivity tune-up of a dual-loop
    detector, the reliability and accuracy of the WSDOT dual-loop truck
    data can be significantly improved.

4.  The statistical approach that uses the features of SV-length
    distribution to determine whether the sensitivity level of a dual
    loop is appropriate has been demonstrated to be effective. The
    histogram of the calculated SV lengths should match closely with the
    histogram generated from the ground-truth SV-length distribution
    when a dual-loop detector is at the appropriate sensitivity
    level(s).

**CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS**

**7.1 CONCLUSIONS**

This study investigated dual-loop sensitivity problems and proposed a
new algorithm to detect and fix such sensitivity problems using loop
event data and the statistical features of SV-length distribution.
High-resolution event data contain more complete individual vehicle
information than interval-aggregated loop measurements and are more
useful for in-depth investigation of dual-loop sensitivity problems. The
analysis results showed that dual loops have two major sensitivity
problems: sensitivity discrepancies between the two single loops that
form a dual-loop detector, and incorrect levels of sensitivity on both
single loops when there are no sensitivity discrepancies. Dual-loop
sensitivity inconsistencies result in erroneous calculation of lane
occupancy, speed, and vehicle length. Incorrect sensitivity levels cause
imprecise measurements of vehicle lengths and hence misclassifications
of vehicles. The combination of both dual-loop sensitivity problems can
cause severely inaccurate measurements of vehicle speed and bin volumes.

A new algorithm for solving the two major types of dual-loop sensitivity
problems has been developed using loop event data and the
characteristics of SV-length distributions. This algorithm has been
implemented in a computer application named ALEDA. Tests of this system
showed that the two dual-loop sensitivity problems mentioned earlier can
be effectively corrected with the ALEDA application. Sensitivity
discrepancies can be eliminated by adjusting sensitivity levels at loop
DEUs until the on-time differences between the M and S loops are close
to zero. Similarly, the appropriate sensitivity level of a dual loop can
be identified based on a ground-truth SV-length distribution.

Dual-loop detectors are a major source of traffic data that are vital
for effective ATMS and ATIS. Dual-loop sensitivity problems must be
solved to increase the reliability of dual-loop data. In practice, these
sensitivity problems are typically detected and corrected manually by
traffic technicians based on their experience. The process is time
consuming and the result is often inaccurate. The proposed methodology
with the implemented ALEDA is expected to help solve dual-loop
sensitivity problems effectively.

**7.2 RECOMMENDATIONS FOR FURTHER STUDY**

The results from this project lead to several recommendations. First,
further testing of the proposed algorithm embedded in the ALEDA system
with DEUs from different manufacturers and under different weather and
road geometric conditions is recommended. So far the algorithm has been
tested at only two dual-loop stations on the I-5 corridor in the Seattle
area.

Second, given the analysis results of the event data, an enhanced
analysis function for loop error identification and solution
recommendation should be developed. For example, intermittent
fluctuations of dual-loop on-time differences may arise from the cross
chattering of DEUs. Eliminating loop data errors and increasing the
effectiveness and performance of existing traffic data collection
systems will continue to be important for regional transportation
authorities.

Third, the ALEDA system needs further improvements in user interface
design and hardware selection to make it a standard tool for maintenance
staffs to tune-up dual loop detectors with sensitivity problems.

Fourth, the effectiveness of ALEDA depends on the physical conditions of
dual-loop detectors. For example, the sensitivity discrepancy problem
cannot be corrected if the M and S loop sensitivity difference is beyond
an adjustable range. Also, a tune-up is normally an approximate solution
rather than a perfect one due to the discrete values of sensitivity
levels available on a DEU.

Finally, the current dual-loop algorithm used by the WSDOT throws away
many potentially useful vehicle measurements. A new dual-loop algorithm
that is able to correct imperfect vehicle measurements and be less
affected by loop detector noises needs to be developed to further
improve truck and speed data.

**Acknowledgements**

The authors are grateful for the financial support to this project from
Transportation Northwest (TransNow), the USDOT University Transportation
Center for Federal Region 10, and the Washington State Department of
Transportation (WSDOT). The authors also wish to express sincere
appreciation to WSDOT's Traffic System Management Center personnel,
specifically, Mr. Mark Morse, Mr. Chitty David, and Ms. Lanping Xu for
their valuable suggestions and help with the collection of freeway data.
Special thanks also go to TransNow's staff, Andrea King and Christine
Fish for their compiling and editing of this final report.

**BIBLIOGRAPHY**

1.  Al-Deek, H. (1991). "The Role of Advanced Traveler Information
    Systems in Incident Management", *Dissertation Series Institute of
    Transportation*. Report Number: 1-5.

2.  Cheevarunothai, P., Y. Wang, and N. L. Nihan (2005). "Development of
    Advanced Loop Event Analyzer (ALEDA) for Investigations of Dual-Loop
    Detector Malfunctions", Presented at *The 12^th^ World Congress on
    Intelligent Transportation Systems,* San Francisco.

3.  Chen, C. H., and G. L. Chang (1993). "A Dynamic Real-Time Incident
    Detection System for Urban Arterials-System Architecture and
    Preliminary Results", In *Pacific Rim* *TransTech Conference: Volume
    I: Advance Technologies*, ASCE, 0-87262-916-3, pp. 98-104.

4.  Chen, C., K. Petty, A. Skabardonis, P.P. Varaiya, and Z. Jia (2001).
    "Freeway Performance Measurement System: Mining Loop Detector Data",
    In *Transportation Research* *Record: Journal of the Transportation
    Research Board, No. 1748*, TRB, National Research Council,
    Washington, D.C., 2001, pp. 96-102.

5.  Chen, L., and A. May (1987). "Traffic detector errors and
    diagnostics", In *Transportation Research* *Record: Journal of the
    Transportation Research Board, No. 1132*, TRB, National Research
    Council, Washington, D.C., pp. 82--93.

6.  Clark, D. C., W. T. Scherer, and B. L. Smith (2001).
    "Performance-Cost Evaluation Methodology for ITS Equipment
    Deployment", *Research Project Report for the* *National IIS
    Implementation Research Center, a USDOT University Transportation*
    *Center*. Report Number: UVA-CE-ITS-01-2.

7.  Cleghorn, D., F. L. Hall, and D. Garbuio (1991). "Improved Data
    Screening Techniques for Freeway Traffic Management Systems", In
    *Transportation Research Record: Journal of* *the Transportation
    Research Board, No. 1320*, TRB, National Research Council,
    Washington, D.C., pp. 17-23.

8.  Coifman, B. (1999). "Using Dual Loop Speed Traps to Identify
    Detector Errors", In *Transportation Research Record: Journal of the
    Transportation Research Board, No.* *1683*, TRB, National Research
    Council, Washington, D.C., pp. 47-58.

9.  Coifman, B., D. Lyddy, and A. Sabardonis (2000). "The Berkeley
    Highway Laboratory- Building on the I-880 Field Experiment", *Proc.
    IEEE ITS Council Annual Meeting*, IEEE, pp 5-10.

10. Coifman, B., and M. Cassidy (2002). "Vehicle Reidentification and
    Travel Time Measurement on Congested Freeways", *Transportation
    Research: Part A*. Vol. 36, No. 10, pp. 899-917.

11. Coifman, B., and S. Shoorjaty (2004), "Event Data Based Traffic
    Detector Validation tests", *Journal of Transportation Engineering*,
    Vol.130, No.3

12. ITE (Institute of Transportation Engineers) (1997). *Traffic
    Detector Handbook*. 2nd Edition, Washington D.C.

13. Jacobson, L. N., N. L. Nihan, and J. D. Bender. (1990). "Detecting
    Erroneous Loop Detector Data in a Freeway Traffic Management
    System", *Transportation Research Record:* *Journal of the
    Transportation Research Board, No. 1287*, TRB, National Research
    Council, Washington, D.C., pp. 151-166.

14. Jensen, M., M. Williamson, R. Sanchez, A. Newton, C. Mitchell,
    and M. Hallenbeck (2003). *WSDOT Intermodal Data Linkages Freight
    ITS Operational Test Evaluation: Final Report. Part 2, Freight ITS
    Traffic Data Evaluation,* *Final Report for Science* *Applications
    International Corporation*. Washington State, U.S. Department of
    Transportation. Report Number: EDL#13781.

15. Model SeaLINK ISO-16 Digital Input Adapter Technical Manual.
    <http://www.sealevel.com/uploads/manuals/8207.pdf>. Accessed on
    January 6, 2005

16. Nihan, L.N., Jacobson, L.N., Bender, J.D., and Davis, G.A. (1990).
    *Detector Data Validity*. Washington State Transportation Center --
    TRAC/WSDOT, TransNow/WSDOT Joint Final Research Report No. WA-RD
    208.1, 80 pages.

17. Nihan, L.N., X. Zhang, and Y. Wang (2002). *Evaluation of Dual-Loop
    Data Accuracy Using Video Ground Truth Data*. Washington State
    Transportation Center -- TRAC/WSDOT, TransNow/WSDOT Joint Final
    Research Report No. WA-RD 535.1, 35 pages.

18. Smith, K., and B. L. Smith (2001). *Forecasting the Clearance Time
    of Freeway Accidents.* Center for Transportation Studies, University
    of Virginia, Research Report No. UVACTS-15-0-35.

19. Traffic Data Acquisition and Distribution (TDAD) database.
    http://www.its.washington.edu/tdad/,Accessed on December 25, 2004

20. Turner, S., L. Albert, B. Gajewski, and W. Eisele (2000). "Archived
    Intelligent Transportation System Data Quality: Preliminary Analysis
    of San Antonio Transguide Data", *Transportation Research Record:
    Journal of the Transportation Research Board, No.* *1719*, TRB,
    National Research Council, Washington, D.C., pp. 77-84.

21. Wang, Y., and L.N. Nihan. (2003). "Can Single-Loop Detectors Do the
    Work of Dual-Loop Detectors?", *ASCE Journal of Transportation
    Engineering*, 129(2), pp. 169-176.

22. Wang, Y. and N. L. Nihan (2004). "Dynamic Estimation of Freeway
    Large-Truck Volumes Based on Single-Loop Measurements", *Journal of
    Intelligent Transportation Systems*, Vol. 8, No. 3, pp. 133-141.

23. Wang, Y., and N. L. Nihan (2004). "An Adaptive Algorithm for Freeway
    Speed Estimation with Single-Loop Measurements." *Proceedings of the
    8th World Multi-Conference on* *Systemics, Cybernetics and
    Informatics*, Vol. VII, 396-401.

24. Zhang, X., Y. Wang, N. L. Nihan, and M. E. Hallenbeck (2003).
    "Development of A System to Collect Loop Detector Event (Individual
    Vehicle) Data." *Transportation Research* *Record: Journal of the
    Transportation Research Board, No. 1855*, TRB, National Research
    Council, Washington, D.C., pp. 168-175.