U.S. Department of 

Transportation

Federal Railroad 

Administration

Driver Behavior Analysis at Highway-Rail Grade 

Crossings using Field Operational Test Data—

Light Vehicles

Office of Research

and Development

Washington, DC 20590

DOT/FRA/ORD-13/28 Final Report

May 2013
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Final Report

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4. TITLE AND SUBTITLE

Driver Behavior Analysis at Highway-Rail Grade Crossings using Field Operational Test 

Data—Light Vehicles

5. FUNDING NUMBERS

RR97A3/JMC48

RR97A4/KT333

6. AUTHOR(S)

Tashi Ngamdung and Marco daSilva

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

U.S. Department of Transportation

Research and Innovative Technology Administration

John A. Volpe National Transportation Systems Center

55 Broadway

Cambridge, MA 02142

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REPORT NUMBER

DOT-VNTSC-FRA-12-02

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U.S. Department of Transportation 

Federal Railroad Administration

Office of Research and Development

Washington, DC 20590

10. SPONSORING/MONITORING

AGENCY REPORT NUMBER

DOT/FRA/ORD-13/28

11. SUPPLEMENTARY NOTES

Program Manager: Tarek Omar, D.Sc. 

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This document is available to the public through the FRA Web site at http://www.fra.dot.gov

or by calling (202) 493-1300.

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13. ABSTRACT (Maximum 200 words)

The U. S. Department of Transportation’s (U.S. DOT) Research and Innovative Technology Administration’s (RITA) John A. Volpe National 

Transportation Systems Center (Volpe Center), under the direction of the U.S. DOT Federal Railroad Administration (FRA) Office of Research 

and Development (R&D), conducted a research study focused on collecting and analyzing data related to driver behavior at or on approach to 

highway-rail grade crossings. Volpe Center reviewed and coded 4,215 grade crossing events involving light vehicle drivers collected during a 

recent field operational test of vehicle safety systems. The data collected for each grade crossing included information about drivers’ activities, 

driver and vehicle performance, driving environment, and vehicle location at or on approach to highway-rail grade crossings. 

One of the findings of the data analysis was that, on average, drivers were likely to engage in secondary tasks 46.7 percent of the time. 

Additionally, results showed that drivers failed to look either left or right on approach to passive grade crossings approximately 35 percent of the 

time. The ultimate objective of this research study is to assess basic driver behavior at highway-rail grade crossings so as to identify potential 

driver education/awareness strategies that would best mitigate risky driver behavior at grade crossings.

14. SUBJECT TERMS

Driver behavior, highway-rail intersection, grade crossing, accident reduction, video data, 

violation reduction

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52

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iv

METRIC/ENGLISH CONVERSION FACTORS

ENGLISH TO METRIC METRIC TO ENGLISH

LENGTH (APPROXIMATE) LENGTH (APPROXIMATE)

1 inch (in) = 2.5 centimeters (cm) 1 millimeter (mm) = 0.04 inch (in)

1 foot (ft) = 30 centimeters (cm) 1 centimeter (cm) = 0.4 inch (in)

1 yard (yd) = 0.9 meter (m) 1 meter (m) = 3.3 feet (ft)

1 mile (mi) = 1.6 kilometers (km) 1 meter (m) = 1.1 yards (yd)

1 kilometer (km) = 0.6 mile (mi)

AREA (APPROXIMATE) AREA (APPROXIMATE)

1 square inch (sq in, in2) = 6.5 square centimeters (cm2) 1 square centimeter (cm2) = 0.16 square inch (sq in, in2)

1 square foot (sq ft, ft2) = 0.09 square meter (m2) 1 square meter (m2) = 1.2 square yards (sq yd, yd2)

1 square yard (sq yd, yd2) = 0.8 square meter (m2) 1 square kilometer (km2) = 0.4 square mile (sq mi, mi2)

1 square mile (sq mi, mi2) = 2.6 square kilometers (km2) 10,000 square meters (m2) = 1 hectare (ha) = 2.5 acres

1 acre = 0.4 hectare (he) = 4,000 square meters (m2)

MASS - WEIGHT (APPROXIMATE) MASS - WEIGHT (APPROXIMATE)

1 ounce (oz) = 28 grams (gm) 1 gram (gm) = 0.036 ounce (oz)

1 pound (lb) = 0.45 kilogram (kg) 1 kilogram (kg) = 2.2 pounds (lb)

1 short ton = 2,000 pounds 

(lb)

= 0.9 tonne (t) 1 tonne (t) =

=

1,000 kilograms (kg)

1.1 short tons

VOLUME (APPROXIMATE) VOLUME (APPROXIMATE)

1 teaspoon (tsp) = 5 milliliters (ml) 1 milliliter (ml) = 0.03 fluid ounce (fl oz)

1 tablespoon (tbsp) = 15 milliliters (ml) 1 liter (l) = 2.1 pints (pt)

1 fluid ounce (fl oz) = 30 milliliters (ml) 1 liter (l) = 1.06 quarts (qt)

1 cup (c) = 0.24 liter (l) 1 liter (l) = 0.26 gallon (gal)

1 pint (pt) = 0.47 liter (l)

1 quart (qt) = 0.96 liter (l)

1 gallon (gal) = 3.8 liters (l)

1 cubic foot (cu ft, ft3) = 0.03 cubic meter (m3) 1 cubic meter (m3) = 36 cubic feet (cu ft, ft3)

1 cubic yard (cu yd, yd3) = 0.76 cubic meter (m3) 1 cubic meter (m3) = 1.3 cubic yards (cu yd, yd3)

TEMPERATURE (EXACT) TEMPERATURE (EXACT)

[(x-32)(5/9)] °F = y °C [(9/5) y + 32] °C = x °F

QUICK INCH - CENTIMETER LENGTH CONVERSION

0 1 2 3 4 5

Inches

Centimeters 0 1 2 3 4 5 6 7 8 9 10 11 12 13

QUICK FAHRENHEIT - CELSIUS TEMPERATURE CONVERSIO

 -40° -22° -4° 14° 32° 50° 68° 86° 104° 122° 140° 158° 176° 194° 212°

 

°F

 °C -40° -30° -20° -10° 0° 10° 20° 30° 40° 50° 60° 70° 80° 90° 100°

For more exact and or other conversion factors, see NIST Miscellaneous Publication 286, Units of Weights and 

Measures. Price $2.50 SD Catalog No. C13 10286 Updated 6/17/98
v

Contents

Executive Summary........................................................................................................................ 1

1. Introduction ................................................................................................................. 2

1.1 Project Objectives........................................................................................................ 3

2. Overview of the IVBSS Light Vehicle FOT Data....................................................... 4

3. Grade Crossing Data Collection.................................................................................. 7

3.1 Grade Crossing Locator............................................................................................... 7

3.2 Video Data Viewer...................................................................................................... 7

3.3 Grade Crossing Coder ................................................................................................. 9

3.4 Data Exporter ............................................................................................................ 11

4. Data Analysis............................................................................................................. 13

4.1 Vehicle Exposure ...................................................................................................... 13

4.2 Grade Crossing Events.............................................................................................. 14

4.3 Summary of Grade Crossing Event Data .................................................................. 15

4.4 Grade Crossing Activation ........................................................................................ 19

4.5 Distraction ................................................................................................................. 23

4.6 Looking Behavior...................................................................................................... 27

5. Conclusion................................................................................................................. 31

6. References ................................................................................................................. 33

Appendix A. 10 Hz Numerical Data Dictionary.......................................................................... 34

Appendix B. 5 Hz Numerical Data Dictionary............................................................................ 36

Appendix C. Summary Numerical Data Dictionary.................................................................... 37

Appendix D. Grade Crossing Video Data Coding Instructions................................................... 38

Abbreviations and Acronyms ....................................................................................................... 44
vi

Illustrations

Figure 1. Geographical Range of Travel by FOT Drivers............................................................. 6

Figure 2. Grade Crossing Locator Tool ......................................................................................... 8

Figure 3. Video Data Viewer......................................................................................................... 8

Figure 4. Grade Crossing Coder .................................................................................................... 9

Figure 5. Data Exporter................................................................................................................ 12

Figure 6. Average Distance Travelled by Age Group and Gender.............................................. 14

Figure 7. Snapshot of a Grade Crossing Event at a Highway-Rail Crossing Equipped with 

Gates ..................................................................................................................................... 16

Figure 8. Snapshot of a Grade Crossing Event at a Highway-Rail Crossing Equipped with 

Flashing Lights...................................................................................................................... 16

Figure 9. Snapshot of a Grade Crossing Event at a Highway-Rail Crossing Equipped with 

Crossbucks Only ................................................................................................................... 17

Figure 10. Distribution of Grade Crossing Events by Warning Device ...................................... 18

Figure 11. Distribution of Grade Crossing Events by Number of Tracks................................... 18

Figure 12. Distribution of Grade Crossing Events by Number of Road Traffic Lanes............... 19

Figure 13. Type I Violation ......................................................................................................... 21

Figure 14. Type II Violation ........................................................................................................ 22

Figure 15. Type III Violation....................................................................................................... 22

Figure 16. Snapshot of a Driver Talking on Phone on an Approach to a Highway-Rail Grade 

Crossing ................................................................................................................................ 23

Figure 17. Snapshot of a Driver Text Messaging on an Approach to a Highway-Rail Grade 

Crossing ................................................................................................................................ 24

Figure 18. Snapshot of a Driver Reading on an Approach to a Highway-Rail Grade Crossing . 24

Figure 19. Comparison of Secondary Tasks by Gender .............................................................. 26

Figure 20. Distribution of Grade Crossing Events by Looking Behavior ................................... 28

Figure 21. Distribution of Looking Behavior by Age Group ...................................................... 29

Figure 22. Distribution of Looking Behavior by Warning Devices ............................................ 30

Figure 23. Distribution of Looking Behavior by Type of Crossing ............................................ 30
vii

Tables

Table 1. Number of Participants by Gender and Age Group......................................................... 5

Table 2. Data Dictionary for Grade Crossing Events.................................................................. 10

Table 3. Participants’ Exposure by Gender and Age Group........................................................ 13

Table 4. Grade Crossing Events by Age Group and Gender....................................................... 15

Table 5. Breakdown of Grade Crossing Events at Activated Crossings where Research Vehicles 

Were the Lead Vehicle.......................................................................................................... 21

Table 6. Frequency of Secondary Tasks...................................................................................... 25

Table 7. Distraction Rate by Age Group ..................................................................................... 27

Table 8. Looking Behavior Rate by Gender................................................................................ 28
viii

Acknowledgements

The U.S. Department of Transportation (U.S. DOT) Federal Railroad Administration (FRA) 

Office of Research and Development (R&D) sponsored the work leading to this report. The 

authors would like to thank Sam Alibrahim, Chief of the Train Control and Communications 

Division, and Tarek Omar, Program Manager, Train Control and Communications Division, for 

their guidance and direction during the project and in developing this report. 

The authors would also like to acknowledge Bernard Kennedy, Ryan Brown, Frank Foderaro, 

Jaclyn Buonopane, all of the Systems Safety and Engineering Division; Stephanie Chase, Scott 

Gabree, Danielle Eon, Alison Busch, Michelle Yeh (formerly), all of the Surface Transportation 

and Human Factors Division; and Adam Bailin, formerly of the Advanced Vehicle Technology 

Division, U.S. DOT Research and Innovative Technology Administration’s (RITA) John A. 

Volpe National Transportation Systems Center (Volpe Center), for their support in software 

development, data collection, and analysis. Additional thanks to Chris Cicchitelli of Computer 

Science Corporation, Volpe Center onsite contractor, for his technical support in software 

development.

Special thanks to Wassim Najm, Emily Nodine, and Andy Lam, all of Volpe Center’s Advanced 

Vehicle Technology Division, who graciously satisfied all of our data requests.
1

Executive Summary

To improve safe driving behavior at highway-rail grade crossings, it is important to understand 

driver actions at or on approach to those crossings. Accordingly, the Federal Railroad 

Administration (FRA) Office of Research and Development (R&D) funded a project to review 

and analyze driver’s activities at or on approach to highway-rail grade crossings. This effort was 

conducted under the auspices of the Highway-Rail Grade Crossing Safety and Trespass 

Prevention Research Program at the John A. Volpe National Transportation Systems Center 

(Volpe Center). The research team used data obtained in 2010 from the Integrated VehicleBased Safety System (IVBSS) Field Operational Test (FOT) sponsored by the U.S. DOT 

National Highway Traffic Safety Administration (NHTSA) for analysis of driver behavior at or 

on approach to highway-rail grade crossings.

Analysis of the IVBSS FOT data yielded a subset of 4,215 grade crossing events, or instances,

where the IVBSS research vehicle traversed a grade crossing. The research team then reviewed

and coded the 4,215 grade crossing events. The data collected for each grade crossing event

included information about drivers’ activities, driver and vehicle performance, driving 

environment, and vehicle location at or on approach to highway-rail grade crossings.

The results of the data analysis revealed that, on average, drivers were likely to engage in 

secondary tasks, an indicator of driver distraction, approximately 46.7 percent of the time. The 

data also indicated that younger and middle-aged drivers are more likely to be engaged in 

secondary tasks compared with older drivers.

Analysis of looking behavior on approach to grade crossings showed that drivers looked at least 

one way at or on approach to highway-rail grade crossings approximately 33.7 percent of the 

time. When analyzed by age group, the results showed that older drivers were much more likely 

to look at least one way at or on approach to highway-rail grade crossings than either middleaged or younger drivers. The analysis of looking behavior by warning devices indicated that 

drivers were most likely to look at least one way at crossings equipped with STOP signs and 

least likely to look at least one way at crossings equipped with flashing lights. The data also 

indicated that drivers looked at least one way in 65 percent of the events at passive crossings, but 

failed to look either way (remained looking straight) in the other 35 percent of passive crossing

events. 

Evaluating the effectiveness of motorist and pedestrian signs and treatment is a top research 

priority. The authors hope the results presented in this report provide the basic driver behavior 

information needed to identify and guide potential driver education and awareness strategies that 

would best mitigate risky driver behavior at grade crossings.
2

1. Introduction

The U. S. Department of Transportation’s (U.S. DOT) Research and Innovative Technology 

Administration (RITA) John A. Volpe National Transportation Systems Center (Volpe Center) 

provides technical support to the U.S. DOT Federal Railroad Administration (FRA) in all aspects 

of highway-rail grade crossing safety and trespass prevention research. Notable progress has 

been made over the past 10 years in improving safety at highway-rail grade crossings. Collisions 

at grade crossings have declined 38 percent, and fatalities at grade crossings have also declined 

approximately 38 percent between 2001 and 2010 [1]. 

In 1994, the U.S. DOT’s Rail-Highway Crossing Safety Action Plan [2] set a goal to reduce 

grade crossing collisions and fatalities nationwide by 50 percent over 10 years. The U.S. DOT 

came close to meeting its goal. From 1994 to 2003, incidents between trains and highway-users 

were reduced by 40.4 percent—from 4,999 to 2,977. Over that same period, fatalities were 

reduced by 45.9 percent—from 617 to 334 [1]. Despite significant reductions in grade crossing 

incidents, they still represent a significant portion of overall accident risk for the railroad 

industry. 

According to a 2004 report by the U.S. DOT Office of Inspector General [3], “Risky driver 

behavior or poor judgment accounted for 31,035 or 94 percent of public grade crossing 

accidents” from 1994 to 2003. The need to conduct research on driver behavior issues at grade 

crossings was highlighted in the FRA-sponsored U.S. DOT Federal Railroad Administration’s 

Third Research Needs Workshop on Highway-Rail Grade Crossing and Trespass Prevention [4]. 

That workshop, held in 2009 as a forum to exchange ideas, concepts, and strategic planning,

resulted in the identification of high-priority research needs. Researching driver behavior and 

evaluating the effectiveness of motorist and pedestrian signs and treatment were top priorities. 

To improve safe driving behavior at highway-rail grade crossings, it is important to understand 

driver actions at or on approach to grade crossings. Thus, the FRA Office of Research and 

Development (R&D) funded a project to review and analyze drivers’ activities at or on approach 

to highway-rail grade crossings. Volpe Center used data obtained in 2010 from the Integrated 

Vehicle-Based Safety System (IVBSS) Field Operational Test (FOT) sponsored by the U.S.

DOT National Highway Traffic Safety Administration (NHTSA) for this effort.

The IVBSS program was established in November of 2005 to develop and test an integrated, 

vehicle-based, crash warning system that would help reduce rear-end, lane change, and roadway 

departure crashes for light vehicles and heavy commercial trucks. It is a cooperative research 

agreement between the U.S. DOT and an industry team led by University of Michigan 

Transportation Research Institute (UMTRI) to conduct field operational test and collect data to 

objectively assess the potential safety benefits and driver acceptance of prototype integrated 

crash warning systems [5]. 

The Volpe Center, in support of the NHTSA, is currently using Advanced Vehicle Technology to 

perform an independent evaluation of the IVBSS program; the evaluation includes analysis of 

video and numerical data collected during the IVBSS FOT. The Volpe Center’s Highway-Rail 
3

Grade Crossing Safety and Trespass Prevention Research Program leveraged the Center’s 

current NHTSA-sponsored evaluation program to perform research on driver behavior at or on 

approach to highway-rail grade crossings.

1.1 Project Objectives

The main objectives of this project were as follows:

• To conduct a feasibility assessment of using the IVBSS Light Vehicle FOT data to 

perform highway-rail grade crossing driver behavior analysis. 

• To collect and analyze drivers’ activities at or on approach to highway-rail grade 

crossings.

These objectives were achieved through the analysis of video and numerical data gathered from 

the IVBSS FOT. The data collection and analysis focused on events where the test vehicles were 

on approach and traveled over grade crossings. The ultimate objective of the research is to 

provide the basic driver behavior information needed to identify potential driver education and 

awareness strategies that would best mitigate risky driver behavior at grade crossings.
4

2. Overview of the IVBSS Light Vehicle FOT Data

The report titled “Integrated Vehicle-Based Safety Systems Field Operational Test Plan” [5] 

contains test information and the plan for the field operational test. The majority of the 

background information presented in this section was obtained from that report.

The IVBSS Light Vehicle (LV) FOT spanned more than a year from April 16, 2009 to May 13, 

2010. The FOT included 108 participants and 16 research vehicles. Each participant drove a 

research vehicle for a period of approximately six weeks. The data collected for FOT amount to 

approximately 648 weeks or 12 years’ worth of driving data. 

2.1.1 Characterization of the IVBSS LV FOT Fleet

The research vehicles included 16 late-model Honda Accords (4 2006 and 12 2007 models), with 

1 2006 model serving as a backup unit. The light vehicle platform provided the following crash 

warning functions:

• Forward crash warning (FCW)—warns drivers of the potential for a rear-end crash with 

another vehicle;

• Lateral drift warning (LDW)—warns drivers that they may be drifting inadvertently from 

their lane or departing from the roadway;

• Lane-change/merge warning (LCM)—warns drivers of possible unsafe lateral maneuvers 

based on adjacent vehicles, or vehicles approaching in adjacent lanes, and includes fulltime side-object-presence indicators. LCM included a blind-spot detection (BSD) 

component that provided drivers with information about approaching vehicles, as well as 

vehicles in their blind spot; and

• Curve speed warning (CSW)—warns drivers when they are traveling at a rate of speed 

too high to safely negotiate an upcoming curve. [6]

2.1.2 Participants

The 108 participants consisted of 54 male and 54 female randomly sampled drivers ranging in 

age from 21 to 69 years old. The participants, each of whom drove a research vehicle for a 

period of six weeks, were classified into six groups based on gender and age group for the 

IVBSS LV FOT. The age groups consisted of younger (between 20 and 30 years old), middleaged (between 40 and 50 years old), and older (between 60 and 70 years old) individuals. Table 

1 shows the breakdown of the participants in the IVBSS LV FOT by gender and age group.

Participants were recruited with the assistance of the Michigan Secretary of State (Michigan’s 

drivers licensing bureau). A random sample of several thousand drivers from the eight counties 

surrounding Ann Arbor (all within a 1.5-hour drive of UMTRI) was contacted to solicit the

individuals’ participation in the FOT. It was proposed that the sample drivers be screened to 

exclude anyone matching the following criteria: (1) having one crash resulting in a fatality 

within the past 36 months, and (2) having been convicted of either driving while intoxicated or

driving under the influence of alcohol or a controlled substance within the past 36 months. The 

respondents to the solicitation were then screened by an UMTRI research assistant to ensure that 
5

selected participants met the predetermined qualification criteria (i.e., age, gender, and miles 

driven in the past year) to satisfy the proposed experimental design. [5]

Table 1. Number of Participants by Gender and Age Group

Age Male Female Total

Younger 20-30 18 18 36

Middle-aged 40-50 18 18 36

Older 60-70 18 18 36

Total 54 54 108

2.1.3 IVBSS LV FOT Data

A wide range of video and numerical data was collected during the IVBSS LV FOT. The data 

were collected and stored in a Data Acquisition System (DAS) that was installed in each research 

vehicle. Both video and numerical data were collected continuously throughout a trip. A trip 

was defined by the vehicle ignition cycle (i.e., from the time the vehicle ignition was turned on 

until it was turned off) [6]. Data was retrieved from the DAS at the end of the testing period 

when a participant returned the research vehicle to UMTRI. 

The numerical data were collected using the integrated system installed in each research vehicle. 

The system collected data related to vehicle performance, driver performance, vehicle location,

and driving environment. The raw numerical data were stored in a Structured Query Language 

(SQL) database format. The complete list of numerical data collected for the analysis of grade 

crossing events is provided in Appendices A, B, and C. 

The video data were collected from five cameras that were installed inside each research vehicle. 

The cameras were placed strategically to capture the forward view, driver’s face, instrument 

panel, exterior left side of the vehicle, and exterior right side of the vehicle. 

For the IVBSS Light Vehicle FOT, the data set consisted of 22,656 trips covering a total of 

213,395 miles. Figure 1 shows the geographical range of FOT travel based on destination points

[6]. The majority of travel was within the Lower Peninsula of Michigan, with the greatest 

concentration in the metropolitan areas of Detroit and Ann Arbor. Travel ranged as far north as the 

Upper Peninsula of Michigan, west to south central Missouri, and east to eastern Pennsylvania, 

Washington, DC, and eastern North Carolina. Based on drivers’ end destinations, the study area 

selected for this research consisted of highway-rail grade crossings in Michigan, Indiana, and Ohio. 

Grade crossings in these three States, where most of the travel took place, were cross-referenced with

the routes traveled by each research vehicle to determine research vehicle presence at highwayrail grade crossings.
6

Figure 1. Geographical Range of Travel by FOT Drivers
7

3. Grade Crossing Data Collection

The first step to analyzing driver behavior at highway-rail grade crossings was to collect driver 

activities and obtain numerical data at or on approach to such grade crossings. To perform this 

data collection, the Volpe Center research team developed the following four customized data 

collection tools to interface with and query the IVBSS FOT data:

• Grade Crossing Locator

• Video Data Viewer

• Grade Crossing Coder

• Data Exporter

3.1 Grade Crossing Locator

The Grade Crossing Locator tool cross-referenced each grade crossing’s geolocation with the 

research vehicle geolocation to calculate if and when a vehicle was present at a crossing. 

Samples of drivers’ end destinations were mapped to determine the participants’ travel 

destinations. Based on this result, grade crossings in Michigan, Indiana, and Ohio were selected 

and cross-referenced with routes traveled by each research vehicle to determine vehicle presence 

at highway-rail grade crossings. The latitude (lat) and longitude (long) coordinates of each 

highway-rail grade crossing within those three States were obtained from the Bureau of 

Transportation Statistics National Transportation Atlas Database 2008. The geolocation of each 

research vehicle was obtained from the lat/long coordinates recorded by an on-board Global 

Positioning System (GPS). Due to accuracy errors for both crossing and research vehicle 

geolocation, a radius of 100 feet around each grade crossing was used in querying the vehicle 

data to capture the events in which a research vehicle traveled over a highway-rail grade 

crossing. The tool generated a list of possible trips with crossing ID and the estimated time that 

a research vehicle was present at a crossing. Figure 2 shows the snapshot of the Grade Crossing 

Locator tool. 

3.2 Video Data Viewer

The drivers’ activities at or on approach to a highway-rail grade crossing were collected from 

analysis of the video data recorded with the five video cameras installed on each research 

vehicle. A tool called “Video Data Viewer” was developed to combine all five camera views 

and play them simultaneously so that the driver’s activity and the surrounding scene from 

different angles could be viewed concurrently. 

Figure 3 shows a snapshot of the Video Data Viewer as the research vehicle approached a 

crossing that is occupied by a train. A drop-down menu on the top left corner of the screen 

provided an option to select driver and trip for possible grade crossing events. All grade crossing 

events for the selected driver and trip were displayed on the bottom right corner of the screen 

with Crossing ID and the time the research vehicle was present at that crossing. 
8

 Figure 2. Grade Crossing Locator Tool

Figure 3. Video Data Viewer
9

3.3 Grade Crossing Coder

Figure 4 shows a snapshot of the Grade Crossing Coder. This tool was developed and used to 

record drivers’ activities observed through the video data viewer. The data collection of driver 

activity for a grade crossing event started at the moment a research vehicle arrived at the grade 

crossing pavement marking (this is designated in the Grade Crossing Coder as t1) and ended 

when it cleared the crossing (this is designated in the Grade Crossing Coder as t3). For any 

grade crossing event in which a research vehicle did not encounter the pavement marking, the 

data collection started eight seconds before the research vehicle arrived at the crossing (this is 

designated in the Grade Crossing Coder as t2). This 8-second value was calculated based on the 

average time it took a research vehicle to cover the distance from a pavement marking to a 

crossing, as observed during the study. The data collected from the video scene included

information about crossing inventory, driving conditions, driver’s activities, and crossing 

violations. 

Figure 4. Grade Crossing Coder
10

The left side of the screen in Figure 4 shows a schematic of a typical single track highway-rail 

grade crossing with the location of t1, t2, and t3. The menus on the right were used to enter 

drivers’ characteristics and surrounding scene for a grade crossing event. The first step in 

entering the data was to input the driver and the trip ID and then select the crossing ID for which 

the data was collected. Once the information was entered, the save button on the bottom right 

corner of the screen was used to save the coded information for the event as a data table. 

Table 2 provides a list of the data gathered from analysis of the video data for each grade 

crossing event through the Grade Crossing Coder. Appendix D provides the data dictionary and 

instruction on how the video was coded.

Table 2. Data Dictionary for Grade Crossing Events

Driver ID Driver ID

Trip ID Trip ID

crossing_event_id Crossing ID for which the data is collected

t1_time

Time a research vehicle arrives at the crossing 

pavement marking

t2_time

Time a research vehicle arrives at the crossing 

(used the stop line at the crossing as a reference for 

arriving at the crossing)

t3_time Time a research vehicle exits the crossing

warning devices Lists highest warning device at a crossing

adv_warning_devices

Identifies whether advanced warning is present for 

a crossing

num_tracks Number of tracks

num_traffic_lanes

Number of traffic lanes that intersect with a 

crossing

driver_distracted Secondary task that driver was involved in

eyes_off_road Identifies whether driver's eyes were off road

obstruction identifies whether a crossing was obstructed

weather Provides weather condition

visibility Provides visibility

nearby_intersecting_road

Identifies whether intersecting road is present 10 

sec within a crossing

nearby_intersecting_road_time

Time research vehicle arrives at an intersecting 

road

nearby_intersecting_road_warning_dev

ices Warning devices at the intersecting road
11

cars_in_front

Identifies whether cars are present in front of the 

research vehicle

cars_stop_on_crossing Identifies whether any car is stopped on a crossing

construction

Identifies whether construction work is performed 

at or on approach to the crossing

signal_activation

Identifies whether crossing was activated for the 

grade crossing event

veh_commit_violation

Identifies whether research vehicle committed 

violation

lead_veh_commit_violation Identifies whether lead vehicle committed violation

veh_opposite_approach_commit_violat

ion

Identifies whether vehicle in opposite direction 

committed violation

veh_same_approach_commit_violation

Identifies whether vehicle on same approach 

committed violation

violation_veh_type Research vehicle violation type

violation_veh_when Violation before or after a train for research vehicle

violation_leadveh_type Lead vehicle violation type

violation_leadveh_when Violation before or after a train for lead vehicle

violation_oppoapproachveh_type Opposite approach vehicle violation type

violation_oppoapproachveh_when

Violation before or after a train for vehicle from 

opposite approach

violation_sameapproachveh_type Same approach vehicle violation type

violation_sameapproachveh_when

Violation before or after a train for vehicle from 

same approach

looking_behavior Looking behavior as driver approached the crossing

passengers_in_car Number of passengers in the car

total_trip_time Total trip time

traffic Traffic conditions on approach to a crossing

comments Comments

3.4 Data Exporter

Figure 5 shows a screenshot of the Grade Crossing Exporter tool. This tool was used to export 

both video and numerical data that was collected for each grade crossing event. The top section 

titled “Available Grade Crossings” exported a list of possible grade crossing events per driver 

that were identified with the Grade Crossing Locator tool. The file was exported in a 
12

spreadsheet format with a list of possible trips, crossing ID, and the time a research vehicle was 

present at a crossing for that trip.

The middle section titled “Grade Crossing Responses” exported all the user generated grade 

crossing responses for the selected driver as inputted in the Grade Crossing Coder. This data 

was also exported in a spreadsheet format. 

The last section titled “Numerical Data” was used to export both video and numerical data for a 

single grade crossing event. The data was exported in a spreadsheet format with three different 

tabs. The first tab included numerical data collected at 10 Hz during the IVBSS FOT; the second 

tab included data collected at 5 Hz; and the final tab included summary data of the trip and the 

video data that was collected for that grade crossing event. Refer to Appendices A, B, and C for 

the complete list of both numerical and video data that were collected for each grade crossing 

event.

Figure 5. Data Exporter
13

4. Data Analysis

Analysis of the driver behavior data at grade crossings focused on distribution of crossing events 

by warning devices, vehicle exposure, grade crossing violation, and identification of patterns of 

driver behavior. Examples of driver behavior characteristics include looking behavior (looked 

one way, looked both ways, or looked neither way) and the presence of distractions (phone, 

eating, talking to passenger, etc.). The analysis presented in this section focuses on the lightvehicle part of the IVBSS FOT.

4.1 Vehicle Exposure

The IVBSS LV FOT spanned a little more than a year from April 16, 2009, to May 1, 2010. 

During this period, the 108 participants drove research vehicles a total of 213,395 miles and 

made a total of 22,656 trips. On average, each participant drove approximately 1,976 miles 

during the test period. Figure 6 shows the average distance in miles travelled by age group and 

gender. As can be seen from the chart, male drivers drove more miles on average than female 

drivers across all age groups. In fact, male drivers drove 56.1 percent more miles than female 

drivers (2,409 versus 1,543 miles). Table 3 shows the participants’ exposure by Vehicle Miles 

Traveled across gender and age group. [6]

Table 3. Participants’ Exposure by Gender and Age Group

Younger Middle–aged Older All Drivers

Miles Percent Miles Percent Miles Percent Miles Percent

Male 39,623 19 51,147 24 39,312 18 130,932 61

Female 29,274 14 30,617 14 23,423 11 82,462 39

Total 68,897 32 81,763 38 62,734 29 213,395 100
14

Figure 6. Average Distance Travelled by Age Group and Gender

4.2 Grade Crossing Events

For the IVBSS Light Vehicle FOT, the 108 participants made a total of 22,656 trips in the 

research vehicles. Of those trips, the Grade Crossing Locator tool identified 4,898 trips within 

which there was a possible grade crossing event. As previously discussed in Section 3.1, the 

Grade Crossing Locator tool cross-referenced each grade crossing’s geolocation with the 

research vehicle’s geolocation to calculate if and when a vehicle was present at a crossing. Due 

to coordinate accuracy errors for both crossing and research vehicle location, a radius of 100 feet 

around each grade crossing was used in querying the vehicle data to capture the events in which 

a research vehicle traveled over a highway-rail grade crossing. The tool generated a list of 

possible grade crossing events, which amounted to 9,736, by using the 100-foot zone around the 

crossing GPS coordinates. The research team reviewed the video from all of these potential

grade crossing events and identified 3,137 trips containing a total of 4,215 grade crossing events. 

The remaining potential events identified by the tool turned out to be false positives. Many of 

those were grade-separated grade crossing events, which were identified since the grade crossing 

GPS data set used for the study contained GPS information for both at-grade and grade-separated 

grade crossings. Many others contained events in which the vehicle’s direction of travel was 

parallel to the railroad and any grade crossing at cross-streets within the 100-foot buffer zone 

would classify it as a grade crossing event.
15

The 4,215 valid grade crossing events occurred at 499 unique grade crossings over the three 

States (Michigan, Indiana, and Ohio) selected for analysis. The results of this report are based 

on the analysis of this data set. Table 4 shows the breakdown of the grade crossing events by age

group and gender. Overall, about two-thirds of the events involved male drivers, nearly half of 

which were in the younger (20–30 years old) age group. 

Table 4. Grade Crossing Events by Age Group and Gender

Male Female Total

Younger 1,292 276 1,568

Middle-aged 872 663 1,535

Older 580 532 1,112

Total 2,744 1,471 4,215

4.3 Summary of Grade Crossing Event Data

The identification of the warning devices present at each grade crossing was one of the data 

elements coded during the analysis of the video data for each grade crossing event. The analyst 

was instructed to select the highest level of warning device that was present at the crossing. 

Figure 7, Figure 8, and Figure 9 show some examples of warning devices identified at selected 

grade crossings. A drop-down menu, arranged in descending order with highest level of warning 

device at the top, was used for this data element. The drop-down menu contained the following 

devices:

• Four Quadrant Gate

• Gate

• Flashing Lights

• STOP sign

• Crossbucks

• Other

• Unknown
16

Figure 7. Snapshot of a Grade Crossing Event at a Highway-Rail Crossing Equipped with

Gates

Figure 8. Snapshot of a Grade Crossing Event at a Highway-Rail Crossing Equipped with

Flashing Lights
17

Figure 9. Snapshot of a Grade Crossing Event at a Highway-Rail Crossing Equipped with

Crossbucks Only

Figure 10 shows the distribution of the 4,215 grade crossing events by warning device present at 

the crossing. As can be seen, the majority of the grade crossing events (93 percent or 3,901) 

occurred at crossings equipped with active warning devices. The active warning devices for this 

data set include gates and flashing lights. The remaining grade crossing events (7 percent or 

314) occurred at passive crossings. The passive crossings in this data set included those 

equipped with STOP signs, crossbucks, and unknown. The unknown category indicates a 

crossing where the warning device was not identifiable from the video data due to poor video 

quality or environmental factors. Although two other warning device categories, “Four Quadrant 

Gate” and “Other,” are listed in the study’s coding drop-down menu, none of the 4,215 grade 

crossing events were coded under those categories.
18

Figure 10. Distribution of Grade Crossing Events by Warning Device

Figure 11 shows the distribution of grade crossing events by number of tracks at the crossing. 

As shown, 75.7 percent (3,189) of the grade crossing events occurred at single track highway-rail 

grade crossings.

Figure 11. Distribution of Grade Crossing Events by Number of Tracks
19

Figure 12 shows the distribution of grade crossing events by number of traffic lanes traversing 

the crossing. As shown, approximately two-thirds (2,799) of the grade crossing events occurred 

at highway-rail grade crossings on single lane roadways. Additionally, roughly 28 percent of the 

events occurred on two-lane roadways.

Figure 12. Distribution of Grade Crossing Events by Number of Road Traffic Lanes

4.4 Grade Crossing Activation

For an active crossing, grade crossing activation is defined as any time the crossing warnings are 

activated, either when a train is on approach or in the event of a false activation [7]. For a 

passive crossing, grade crossing activation is defined as when a highway-rail crossing is 

occupied by a train. Out of the possible 4,215 crossing events analyzed in this study, the 

research team identified 52 events (1.2 percent) involving grade crossing activation. The 52 

grade crossing activation events were comprised of 42 at crossings equipped with gates, 8 at 

crossings equipped with flashing lights, and 2 at crossings equipped with STOP signs. Eight out 

of the 52 grade crossing events were identified as false activation, meaning that the warning 

devices were activated without a train being present. 

Grade crossing violations were collected for the 52 grade crossing activation events. A grade 

crossing violation occurs when motorists disobey the warning devices at a highway-rail grade 

crossing. Grade crossing violations were collected for the research vehicle as well as for other 

vehicles in the vicinity, as captured by the research vehicle’s external cameras. The violations 

committed by other vehicles included other vehicles in front or parallel to the research vehicle 

traveling in the same direction and vehicles on opposite approaches to the grade crossing. For 

those 52 grade crossing activation events, the research team identified 73 violations. This is an 

average of 1.4 violations per grade crossing activation event during the study period.
20

Three types of vehicle violations were collected for the 42 grade crossing activation events that 

occurred at crossings equipped with gates. The definition of each type is provided below [7].

• A Type I violation occurs when a violator traverses the crossing while the lights are 

flashing, the bells are ringing, but before gate descent.

• A Type II violation occurs when a violator traverses the crossing during gate descent or 

ascent with audible devices sounding.

• A Type III violation occurs when a violator traverses the grade crossing after the gates

finish their descent and are fully deployed in a horizontal position.

The research team identified 63 violations for the 42 grade crossing activation events that 

occurred at crossings equipped with gates. The 63 violations were comprised of no Type I 

violations, 55 Type II violations (all after a train), and 9 Type III violations (8 before and 1 after 

a train). 

The research team identified 10 violations for the 8 grade crossing activation events that 

occurred at crossings equipped with flashing lights. These violations occurred when motorists

traversed the crossing while the lights were flashing either before or after a train event. The 10 

violations consisted of 6 before train arrival and 4 after a train had cleared the crossing but while 

lights were still flashing.

The two grade crossing activation events that occurred at crossings equipped with a STOP sign 

did not include any violations. For both train activation events, the crossing was occupied by a 

train when a research vehicle arrived at the crossing.

Research vehicles were the lead vehicle at an activated crossing event on 16 occasions. The 

drivers committed a violation in half of those events. As shown in Table 5, 9 crossing events

occurred at crossings equipped with gates, 5 at crossings equipped with flashing lights, and 2 at 

crossings equipped with STOP signs. For the nine grade crossing activation events at a gated 

crossings, research vehicles committed five violations. The violations included four Type II (all 

after a train) and one Type III (before a train). Figure 13–15 show examples of each type of 

violation.
21

Table 5. Breakdown of Grade Crossing Events at Activated Crossings where Research 

Vehicles Were the Lead Vehicle

Violations? Total

Yes No

Before a train After a train

Gates 1 4 4 9

Flashing Lights 1 2 2 5

Stop Sign 0 0 2 2

Total 2 6 8 16

Figure 13. Type I Violation 
22

Figure 14. Type II Violation

Figure 15. Type III Violation
23

4.5 Distraction

For the purpose of this analysis, drivers were identified as being distracted if they were engaged 

in any secondary tasks that may have prevented them from safely operating the vehicle any time 

between arriving at the pavement marking (t1) and exiting the crossing (t3). The secondary task 

categories for this project were developed based on the set used for the IVBSS project. The 

secondary tasks were identified by viewing face, cabin, and two sides’ video data of grade 

crossing events. Out of possible 4,215 grade crossing events, 1,968 (or 46.7 percent) of the 

grade crossing events involved the driver performing a secondary task. The most frequently 

observed secondary task involved drivers talking to or looking at a passenger (654 or 15.5

percent), followed by talking on or listening to the phone (280 or 6.6 percent), an example of 

which is shown in Figure 16. Figure 17 and Figure 18 show some other examples of drivers 

distracted on their approach to a highway-rail grade crossing. Table 6 lists all possible secondary 

tasks along with their frequency.

Figure 16. Snapshot of a Driver Talking on Phone on an Approach to a Highway-Rail

Grade Crossing
24

Figure 17. Snapshot of a Driver Text Messaging on an Approach to a Highway-Rail Grade 

Crossing

Figure 18. Snapshot of a Driver Reading on an Approach to a Highway-Rail Grade 

Crossing
25

Data was analyzed to determine whether the distraction was any different based on gender or age

group. Overall, male drivers were engaged in secondary tasks slightly more often than their 

female counterparts (47.3 percent of male drivers were engaged in secondary tasks compared 

with 45.5 percent of female drivers). A paired t-test indicated that the overall change in rate 

between male and female drivers involved in secondary tasks was not statistically significant (p

= 0.2762). Figure 19 shows the comparison of secondary task frequency as a percentage of the 

total for each gender.

Table 6. Frequency of Secondary Tasks

ID Secondary Tasks Number of Grade 

Crossing Events with 

Secondary Task

Total

Male Female

0 None 1,446 801 2,247

1 Talking to/looking at passengers 446 208 654

2 Talking on/listening to phone 186 94 280

3 Looking to the side/outside car 150 107 257

4 Smoking/lighting cigarettes 127 32 159

5 Adjusting controls 81 46 127

6 Text messaging 76 13 89

7 Other 46 42 88

8 Eating 33 39 72

9 Reaching for object in vehicle 29 27 56

10 Singing/whistling 39 15 54

11 Drinking 40 8 48

12 Dialing phone 22 15 37

13 Grooming 14 16 30

14 Reading 7 6 13

15 Eyes closed > 1s 3 1 4

Total 2,745 1,470 4,215
26

Figure 19. Comparison of secondary tasks by gender
27

Analysis of secondary tasks based on age group (younger, middle-aged, and older) indicated that 

younger and middle-aged drivers were likelier than older drivers to engage in secondary tasks

during a grade crossing event. Approximately 42.8 percent of older drivers were engaged in 

secondary tasks during grade crossing events compared with 47.9 percent for middle-aged 

drivers and 48.3 percent for younger drivers. A paired t-test indicated that between older and 

middle-aged drivers, the overall change in rate of drivers involved in secondary tasks was 

statistically significant (p=0.0097), as was also the case between older and younger drivers (p =

0.0051). Table 7 shows the distraction rate by age group.

Table 7. Distraction Rate by Age Group

Age Group

Younger Middle-aged Older

Grade Crossing Events 

with Distraction

757 735 476

Total Grade Crossing 

Events

1,568 1,535 1,112

Distraction Rate 0.483 0.479 0.428

4.6 Looking Behavior

Looking behavior was measured by the amount of head movement as the driver approached the 

crossing from the pavement marking (t1) until the research vehicle cleared the crossing (t3). The 

research team viewed the face video data of a grade crossing event to observe whether the driver 

looked one way (to the left or to the right), both ways, or straight ahead. It is important to note 

that the focus was on head movement because no eye tracking data was collected. Analysis of 

the data collected suggests that drivers did not move their heads 66.3 percent of the time as they 

approached a highway-rail grade crossing. Only 15 percent of head movements were in both 

directions and 18.8 percent of head movement was in one direction (looked left or looked right). 

Figure 20 shows the distribution of grade crossing events by looking behavior. 
28

Figure 20. Distribution of Grade Crossing Events by Looking Behavior

The data was analyzed to determine whether the looking behavior was any different based on 

gender and age group. Looking behavior was further categorized into either looked straight or 

looked at least one way. At or on approach to a grade crossing, male drivers looked at least one 

way 33.9 percent of the time and female drivers looked at least one way 33.4 percent of the time. 

This observed difference in driver looking behavior was not statistically significant based on a 

pair t-test (p=0.789). Table 8 shows looking behavior rate by gender.

Table 8. Looking Behavior Rate by Gender

Gender 

Male Female

Grade Crossing Events where 

drivers looked at least one way

929 492

Total Grade Crossing Events 2,744 1,471

Looking Behavior Rate 0.339 0.334

Grade crossing events with drivers that looked at least one way on their approach to grade 

crossings were analyzed per age group (younger, middle-aged, and older). The data analysis of 

the looking behavior indicated that younger drivers were 25.3 percent likely to look at least one 

way compared with 35 percent for middle-aged drivers and 43.8 percent for older drivers at or on 

an approach to a grade crossing. Based on the paired t-test, the observed difference between 

both younger and middle-aged drivers and younger and older drivers was statistically significant 

(p<0.001). Figure 21 illustrates the distribution of looking behavior by age group.
29

Figure 21. Distribution of Looking Behavior by Age Group

Data was further analyzed to determine whether warning devices had any effect on drivers’ 

looking behavior at or on approach to highway-rail grade crossings. The data analysis of the

looking behavior indicated that drivers were most likely to look at least one way at crossings

equipped with STOP sign warning devices (97.3 percent exhibited this behavior) and least likely 

to look at least one way at crossings equipped with flashing lights or where warning devices 

were unknown (29.2 percent exhibited this behavior in each of these two categories). It should 

be noted that the sample data set related to these two warning device categories was very limited, 

as previously shown in Figure 10. Figure 22 illustrates the distribution of looking behavior by 

warning device.

Looking behavior was also analyzed between active and passive crossings. For this analysis, 

active crossings included highway-rail grade crossings equipped with gates and flashing lights;

passive crossings included highway-rail grade crossings equipped with stop sign, cross bucks, 

and unknown. The data analysis of the looking behavior indicated that drivers looked at least 

one way 64.6 percent of the time at passive crossings compared with 31.2 percent at active 

crossings. Based on the paired t-test, the observed difference in looking behavior at active and 

passive crossings was statistically significant (p=0.000). Figure 23 illustrates the distribution of 

looking behavior between active and passive crossings.
30

Figure 22. Distribution of Looking Behavior by Warning Devices

Figure 23. Distribution of Looking Behavior by Type of Crossing
31

5. Conclusion

Although previously studied and acknowledged to be a key factor in highway-rail grade crossing 

accidents, little is known about driver behavior and its contribution in such incidents. Thus, to 

gain a better understating of the problem, the FRA Office of R&D funded this project to review 

and analyze drivers’ activities at or on approach to highway-rail grade crossings. This research 

had two main goals. The first goal was to conduct a feasibility assessment of using the IVBSS 

FOT data to perform grade crossing driver behavior analyses. The second goal was to collect 

and analyze drivers’ activities at or on approach to highway-rail grade crossings. Both of these 

were achieved through the research documented herein. 

It should be noted that this report only covers driver behavior analysis using the IVBSS Light 

Vehicle FOT data set. The LV IVBSS FOT participants took 22,656 trips over the study period. 

Out of those trips, the research team identified 3,137 trips containing 4,215 grade crossing events

or instances where the IVBSS research vehicle traversed a grade crossing in the three States 

selected for this study. The research team then reviewed and coded the 4,215 grade crossing 

events. The data collected for each grade crossing event included information about drivers’ 

activities, driver and vehicle performance, driving environment, and vehicle location at or on 

approach to highway-rail grade crossings.

The results of the data analysis revealed that, on average, drivers were likely to engage in 

secondary tasks, an indicator of driver distraction, approximately 46.7 percent of the time and 

look at least one way at or on approach to highway-rail grade crossings approximately 33.7

percent of the time. 

The analysis of secondary task by gender and age group revealed that the distraction did not 

differ significantly between male and female drivers. Male drivers were engaged in secondary 

tasks 47.3 percent of the time compared with 45.5 percent for female drivers. The data further 

indicated that younger and middle-aged drivers were more likely to be engaged in secondary 

tasks compared with older drivers. Approximately 43 percent of older drivers were engaged in 

secondary tasks at or on approach to highway-rail grade crossings compared with approximately 

48 percent of both middle-aged and younger drivers.

Looking behavior also did not differ significantly between male and female drivers. Male 

drivers looked at least one way 33.9 percent of the time compared with 33.4 percent for female 

drivers. But when analyzed by age group, the data revealed that older drivers were much more 

likely to look at least one way at or on approach to highway-rail grade crossings than either 

middle-aged or younger drivers. Approximately 43.8 percent of older drivers looked at least one 

way at or on approach to highway-rail grade crossings compared with 35 percent for middleaged drivers and 25.3 percent for younger drivers.

The analysis of looking behavior by warning devices indicated that drivers were most likely to 

look at least one way at crossings equipped with STOP signs and least likely to look at least one 

way at crossings equipped with flashing lights or where the warning device type was unknown. 

When analyzed between active and passive crossings, the data indicated that drivers looked at 
32

least one way in 64.6 percent of the events at passive crossings compared with 31.2 percent of 

the events at active crossings.

Follow-on research already being conducted by the research team will focus on analysis of 

additional data elements, including vehicle speed profiles on approach to grade crossings; the 

research will also offer a deeper analysis of the driver behavior elements presented in this report. 

A similar study analyzing professional truck driver behavior will be documented in a subsequent 

report and is also being conducted using the IVBSS Heavy Truck FOT data set.

Evaluating the effectiveness of motorist and pedestrian signs and treatments, and examining

driver behavior was identified as a top research priority in the 2009 FRA-sponsored Research 

Needs Workshop [4]. The authors hope the results presented in this report provide the basic 

driver behavior research needed to identify and guide potential driver education/awareness 

strategies that would best mitigate risky driver behavior at grade crossings.

 
33

6. References

[1] http://safetydata.fra.dot.gov/OfficeofSafety/default.aspx, last accessed June, 2011.

[2] U.S. Department of Transportation, Secretary of Transportation, Rail-Highway Crossing 

Safety Action Plan, Washington, DC: U.S. DOT, June 1994.

[3] U.S. Department of Transportation, Office of Inspector General, Audit of the HighwayRail Grade Crossing Safety Program, Washington, DC: U.S. DOT, June 2004.

[4] Carroll, A., daSilva, M., and Ngamdung, T., USDOT Federal Railroad 

Administration's Third Research Needs Workshop on Highway-Rail Grade 

Crossing Safety and Trespass Prevention: Volume I - Summary of Results. Report 

No. DOT/FRA/ORD-10/01.I. Washington, DC: Federal Railroad Administration, 

January 2010. http://www.fra.dot.gov/eLib/details/L01319

[5] Sayer, J., LeBlanc, D., Bogard, S., Hagan, M., Sardar, H., Buonarosa, M. L., and Barnes, 

M., Integrated Vehicle-Based Safety Systems Field Operational Test Plan, prepared for

Research and Innovative Technology Administration, ITS Joint Program Office, Ann 

Arbor, MI: U.S. DOT/RITA, December 2008.

[6] Sayer, J., Bogard, S., Busnarosa, M. L., LeBlanc, D., Funkhouser, D., Bao, S., 

Blankespoor, A., and Winkler, C., Integrated Vehicle-Based Safety Systems LightVehicle Field Operation Test Key Findings Report, prepared for Research and 

Innovative Technology Administration, ITS Joint Program Office, Ann Arbor, MI: U.S. 

DOT/RITA, January 2011.

[7] Sposato, S., Bien-Aime, P., and Chaudhary, M., Public Education and Enforcement 

Research Study, prepared for Federal Railroad Administration, Cambridge, MA: U.S. 

DOT/FRA, December 2006.
34

Appendix A. 

10 Hz Numerical Data Dictionary

Driver Driver ID

Trip Trip ID

Time Time in centiseconds since das started

ImuIndex

LcmIndex Lane change merge index

RadarFrontExtendedIndex Front Radar extended scan Index

AccelPedal Accelerator pedal

Brake Brake active

Distance Trip distance

Engaged Cruise control active

Speed Vehicle speed from transmission

Steer Steering wheel angle, cw is negative

TurnSignal Turn signal

Wipers Wiper switch state

GpsHeading Gps heading from Ublox Gps

GpsValid True if gps data is valid

Latitude Latitude from Ublox Gps

Longitude Longitude from Ublox Gps

CswAvailable Csw availability

CswWarning Csw warning generated from arbitration

FcwAccel Fcw target acceleration

FcwAvailable Fcw availability

FcwAzimuth Fcw azimuth

FcwRadarIndex ExtendedBosch radar scan index for fcw target

FcwRange Fcw range

FcwRangeRate Fcw range rate

FcwTargetId Fcw Target Id (handle)

FcwTargetType Fcw Target Type

FcwValidTarget Fcw Valid Target

FcwWarning Fcw warning generated from arbitration

LcmRearAvailable Lcm Rear Availability

LcmTTCAvailable Lcm TTC Availability

LcmWarning Lcm warning generated from arbitration

Leds Mirror Leds

BoundaryLeft Lane boundary type, left

BoundaryRight Lane boundary type, right

DistancePastEdge #N/A

LaneChange Lane change

LaneOffset Vehicle offset from lane center

LaneOffsetConf Lane offset confidence

LaneWidth Lane width
35

LdwAvailableLeft Ldw left side availability

LdwAvailableRight Ldw right side availability

LdwCaution Ldw caution generated from arbitration

LdwLateralSpeed Vehicle speed lateral to lane direction from ldw

LdwThreatLeft Ldw left threat type

LdwThreatRight Ldw right threat type

LdwWarning Ldw warning generated from arbitration

Time Time in centiseconds since das started

AcPressureSwitch Ac Pressure Switch

AtmPressure Atmospheric Pressure

BatteryVoltage Battery voltage

DasVoltage DAS input voltage

CoolantTemp Engine coolant temperature

EngineSpeed Engine speed

FuelUsed Fuel used

Gear Gear actual

GpsSpeed Speed from gps

IntakeTemp Intake Temperature

MCPressure Master cylinder pressure

Odometer Odometer

OutsideTemperature Outside temperature (uncalibrated)

PulseActivated True means brake pulse is activating

PulseRefused true means brake pulse command is rejected

ReferenceDistance Reference Distance Travelled

TargetThrottle Current target throttle

Throttle Current throttle

ArbitratedWarning Arbitrated warning enum

ArbReason Reason for arbitrated warning bitmap

DviAlert

Dvi alert response flags bit0 = audio , bit1 = haptic, bit2 = 

lvisual, bit3 = vsa

DviEnable

Dvi enable flags bit0 = audio & haptic, bit1 = led, bit2 = 

vsa

PulseRequest Brake pulse request

PulsePressure Brake pulse pressure request

PulseCalcPressure Brake pulse pressure calculation

SnoozeTime Time remaining until snooze expires

CswAlertRequest Csw imminent warning request

FcwAlertRequest Fcw alert request

LcmAlertRequest >0 if Lcm is requesting a warning

LdwAlertRequest Ldw alert request

AdhIndex Adh index

ArbIndex Arb transaction index

GwIndex Gateway index
36

Appendix B. 

5 Hz Numerical Data Dictionary

Driver Driver ID

Trip Trip ID

Time Time in centiseconds since das started

Ax Longitudinal acceleration from InertiaLink IMU

Ay Lateral acceleration from InertiaLink IMU

Az Vertical acceleration from InertiaLink IMU

PitchRate Pitch rate from InertiaLink IMU

RollRate Roll rate from InertiaLink IMU

YawRate Yaw rate from InertiaLink IMU

Pitch Pitch angle from InertiaLink IMU

Roll Roll angle from InertiaLink IMU

Yaw Yaw angle from InertiaLink IMU

ImuTime #N/A
37

Appendix C. 

Summary Numerical Data Dictionary

Driver Driver ID

Trip Trip ID

StartTime First time of test

EndTime Last time of test

IvbssEnable HMI (Ivbss functionality) enabled

Das Das number

BrakeCount Count of brake applications

Distance Trip distance

LdwDayDistance #N/A

WiperDistance #N/A

EmergencyShutDown

Shut down request from Blue Earth micro because of outof-range

Latitude Latitude from Ublox Gps

Longitude Longitude from Ublox Gps

Odometer Odometer

Prndl Prndl

Speed Vehicle speed from transmission

TurnSignalCount Count of turn signal application

LaneChangeCount Count of lane change

Vgt25Distance Distance above 25 mph

VgtRearMinDistance #N/A

VgtTTCMinDistance #N/A

CswRequestCount Count of Csw Alert requests

FcwRequestCount Count of Fcw Alert requests

LcmRequestCount Count of Lcm Alert requests

LdwRequestCount Count of Ldw Alert requests

CswWarningCount Count of Csw warnings

FcwWarningCount Count of Fcw warnings

LcmWarningCount Count of Lcm warnings

LdwCautionCount Count of Ldw cautions

LdwWarningCount Count of Ldw warnings

TODTripStart

Absolute date/time corresponding to test time = 0 in access 

date/time format based on computer clock

TripStart

Absolute date/time corresponding to test time = 0 in access 

date/time format

Vehicle Vehicle number

WarmStart True if ignition happened with the das already running
38

Appendix D. 

Grade Crossing Video Data Coding Instructions

1. Enter driver and trip ID for which you are collecting driver information and click “load” 

button.

2. All the grade crossing associated with that driver and trip will load in the drop-down 

menu.

3. Select the crossing from drop-down menu.

4. T1: Enter the time (hh:mm:ss) when the test vehicle arrives at the crossing pavement 

marking. If no pavement markings are present, then subtract 8 seconds from the time 

found for T2. In this case you would figure out the time for T2 and then go back and 

enter the time for T1.

5. T2: Enter the time (hh:mm:ss) when the test vehicle arrives at the crossing. Use the stop 

line at the crossing as a reference for arriving at the crossing.

6. T3: Enter the time (hh:mm:ss) when the test vehicle exits the crossing. Since there is no 

rear camera, you can usually tell the test vehicle has exited the crossing when the vehicle 

has stopped vibrating from going over the tracks. 

7. Warning Devices: Select the highest level of warning devices that is present at the 

crossing. The drop-down menu is arranged in descending order with highest level of 

warning devices at the top. The drop-down menu contains the following devices:

o Four Quadrant Gate

o Gate

o Flashing Lights

o STOP sign

o Crossbucks

o Other

o Unknown

8. Advanced Warning Devices: Identify whether advanced warning devices are present at

the crossing. Advanced warning devices are usually located before or at the pavement 

marking. 

o Yes

o No

o Unsure

9. Number of Tracks: Select number of tracks at the crossing from the drop-down menu.

o 1

o 2

o 3
39

o 4

o >4

10. Number of Traffic Lanes: Select the number of traffic lanes approaching the crossing. 

If the traffic lanes merge or expand at the approach of the crossing, select the number of 

traffic lanes that intersect with the crossing.

o 1

o 2

o 3

o 4

o 5

11. Driver Distracted: From the drop-down menu, select the distraction that the driver 

experiences anytime between t1 and t3. If the driver was not distracted, then select 

“None.”

o None

o Dialing phone

o Talking on/listening to phone

o Text messaging

o Singing/whistling

o Talking to/looking at passenger

o Adjusting controls

o Eyes closed >1s

o Eating

o Drinking

o Grooming

o Smoking/lighting cigarettes

o Reading

o Reaching for object in vehicle

o Looking to the side/outside

o Other (please specify in the comment section)

12. Eyes off Road: Identify from the drop-down menu whether the driver’s eyes were off 

the road anytime between t1 and t3.

o Yes

o No

o Unsure

13. Obstruction: From the drop-down menu, select the category that best describes the 

obstruction of the crossing on the approach to the crossing. 

o None

o Trees/Bushes

o Other vehicles

o Building

o Other train
40

14. Weather: Select from the drop-down menu the weather category that best describes the 

weather condition. 

o Clear

o Foggy

o Rain 

o Snow

15. Visibility: Select from the drop-down menu the visibility category that best describes the 

visibility on approach to the crossing. 

o Dawn

o Day

o Dusk

o Dark

16. Nearby Intersecting Road Present: Identify whether there is a crossing intersection 

(not side street) 10 seconds after (t2) the test vehicle enters the crossing.

o Yes

o No

17. Nearby Intersecting Road Time: If there is a nearby intersection present, enter time 

(hh:mm:ss) the test vehicle arrives at the intersection.

18. Nearby Intersecting Road Warning Devices: If there is a nearby intersection present, 

select the warning device that is present at the intersection.

o Traffic Lights

o Stop Sign

o Yield Sign

o Rotary

19. Cars in front: Select from the drop-down menu whether there is a car present in front of 

the test vehicle in the same lane anytime between t1 and t3. 

o Yes

o No

o Unsure

20. Cars Stop on Crossing: Select from the drop-down menu whether the test vehicle or 

any other vehicle is stopped on the crossing.

o Yes

o No

o Unsure

21. Construction: Identify whether there is construction work being performed anytime 

between t1 and t3.

o Yes

o No
41

o Unsure

22. Was there signal activation: Identify from the drop-down menu whether there was any 

kind of activation at the crossing. The drop-down menu contains the following activation 

types:

o Train activation: When a train is present at the crossing, the train triggers the 

track circuitry to activate the safety devices at the crossing. If a passive crossing, 

if there is a train present at the crossing.

o False activation: When the safety devices at the crossing are activated without 

train’s presence at the crossing. This choice is only applicable for active 

crossings, not passive crossings.

o No activation: When the safety devices at the crossing are not activated; or, at a 

passive crossing, if there is no train present at the crossing.

23. Did the vehicle commit a violation: Identify from the drop-down menu whether the test 

vehicle commits any one of the three violations. 

o Type I: A type I violation occurs when a violator traverses the crossing

while the lights are flashing, the bells are ringing, but before gate descent.

o Type II: A type II violation occurs when a violator traverses the crossing 

during gate descent or ascent with audible devices sounding.

o Type III: A type III violation occurs when a violator traverses the grade 

crossing after the gates finish their descent and are fully deployed in a 

horizontal position.

24. Did the lead vehicle commit a violation: Identify from the drop-down menu whether 

the lead vehicle (vehicle in front of the test vehicle) committed any of the above three 

violations.

o Yes

o No

o Unsure

25. Did the vehicle on opposite approach commit a violation: Identify from the dropdown menu whether any of the vehicles from the opposite approach committed any of the 

above three violations.

o Yes

o No

o Unsure 

26. Did the vehicle on same approach commit a violation: Identify from the drop-down 

menu whether vehicle on same approach committed any of the above three violations.

o Yes

o No

o Unsure

27. Violation_Vehicle: Select the type of violation and identify whether the test vehicle 

caused a violation before or after the train arrival at the crossing.
42

o Type I o Before

o Type II o After

o Type III

28. Violation_Lead Vehicle: Select the type of violation and identify whether it was before 

or after the train arrival at the crossing. 

o Type I o Before

o Type II o After

o Type III

29. Violation_Vehicle on opposite approach: Select the type of violation and identify 

whether it was before or after the train arrival at the crossing.

o Type I o Before

o Type II o After

o Type III

30. Violation_Vehicle on same approach: Select the type of violation and identify whether 

it was before or after the train arrival at the crossing.

o Type I o Before

o Type II o After

o Type III

31. Looking Behavior: Select from the drop-down menu the option that best describes the 

driver’s looking behavior anytime between t1 and t3. Looking behavior does not mean

that the driver is distracted; he or she may be looking at the surroundings to assess if a 

train is arriving or has left. If the driver looked straight ahead, select the “None of the 

above” option.

o Looked Left

o Looked Right

o Looked Both Ways

o None of the Above 

32. Passengers in the car: Select from the drop-down menu the number of passengers in the 

car. You can identify whether there are passengers in the car from reviewing the cab and 

face view.

o 0

o 1

o 2

o 3

o N/A

33. Total Trip Time: Enter the total trip time in hh:mm:ss format. The total trip time can 

be found at the top right corner of the navigation control box.

34. Traffic: Identify the traffic condition (density) on approach side to crossing between t1 

and t3. Please do not consider traffic condition on the opposite approach.
43

o None: Select this category if there is no traffic (no vehicles) in front of the test 

vehicle.

o Light: Select this category if there are few vehicles (2–3 vehicles) in front and 

traffic is most likely moving at or near roadway speed limit.

o Moderate: Select this category if there are several vehicles in front (4–6 

vehicles) and traffic is most likely significantly slower than the normal roadway 

speed.

o Heavy: Select this category if the traffic (6+ vehicles) condition is stop and go.

35. Comment: Add any comments that describe driver activities or roadway characteristics 

that are not collected. This field does not have to be filled out for every trip. 
44

Abbreviations and Acronyms

BSD Blind Spot Detection

CSW Curve Speed Warning

DAS Data Acquisition System

FCW Forward Crash Warning

FOT Field Operational Test

FRA Federal Railroad Administration

GPS Global Positioning System

Hz Hertz

IVBSS Integrated Vehicle-Based Safety Systems

lat Latitude

LCM Lane-Change/Merge

LDW Lane Departure Warning

long Longitude

LV Light Vehicle

NHTSA National Highway Traffic Safety Administration

R&D Research and Development

RITA Research and Innovative Technology Administration

SQL Structured Query Language

UMTRI University of Michigan Transportation Research Institute

U.S. DOT United States Department of Transportation

VMT Vehicle Miles Traveled

Volpe Center John A. Volpe National Transportation Systems Center