U.S. Department of 

Transportation

Federal Railroad 

Administration

Driver Behavior Analysis at Highway-Rail Grade 

Crossings using Field Operational Test Data –

Heavy Trucks

Office of Research

and Development

Washington, DC 20590

DOT/FRA/ORD-12/22 Final Report

December 2012
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4. TITLE AND SUBTITLE

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

Data – Heavy Trucks

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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Federal Railroad Administration

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13. ABSTRACT (Maximum 200 words)

The United States 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 characteristics at or on 

approach to highway-rail grade crossings. Volpe Center reviewed and coded 3,171 grade crossing events involving heavy vehicle drivers 

collected during a recent field operational test of vehicle safety systems. The data collected for each grade crossing included data 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, an indicator of distraction, about 

21 percent of the time while traversing a highway-rail grade crossing. Additionally, results showed that drivers failed to look either left or right 

on approach to passive grade crossings about 41 percent of the time. The ultimate objective of the research is to provide the basic driver 

behavior research needed 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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51

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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

Illustrations .................................................................................................................................... vi

Tables..........................................................................................................vii

Acknowledgements......................................................................................................................viii

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

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

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

2 Overview of the IVBSS Heavy Truck FOT Data........................................................ 4

3 Grade Crossing Data Collection.................................................................................. 8

3.1 Grade Crossing Locator............................................................................................... 8

3.2 Video Data Viewer...................................................................................................... 9

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

3.4 Data Exporter ............................................................................................................ 12

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 Distraction ................................................................................................................. 19

4.5 Looking Behavior...................................................................................................... 22

4.6 Grade Crossing Activation ........................................................................................ 26

5 Conclusion................................................................................................................. 30

6 References ................................................................................................................. 32

Appendix A. 10 Hz Numerical Data Dictionary.......................................................................... 33

Appendix B. 5 Hz Numerical Data Dictionary............................................................................ 35

Appendix C. Summary Numerical Data Dictionary.................................................................... 36

Appendix D. Grade Crossing Video Data Coding Instructions................................................... 37

Abbreviations and Acronyms ....................................................................................................... 43
vi

Illustrations

Figure 1. Incident Rate per Billon VMT for Commercial Vehicles and All Other Vehicle Types 2

Figure 2. Geographical Range of Driving by P&D Drivers ........................................................... 6

Figure 3. Geographical Range of Driving by LH Drivers.............................................................. 7

Figure 4. Grade Crossing Locator Tool .......................................................................................... 8

Figure 5. Video Data Viewer.......................................................................................................... 9

Figure 6. Grade Crossing Coder ................................................................................................... 10

Figure 7. Data Exporter................................................................................................................. 12

Figure 8. Snapshot of an Event at a Highway-Rail Crossing Equipped with Gates..................... 15

Figure 9. Snapshot of an Event at a Highway-Rail Crossing Equipped with Flashing Lights..... 16

Figure 10. Snapshot of an Event at a Highway-Rail Crossing Equipped with Crossbucks Only..16

Figure 11. Distribution of Grade Crossing Events by Warning Device ....................................... 17

Figure 12. Distribution of Grade Crossing Events by Number of Tracks.................................... 18

Figure 13. Distribution of Grade Crossing Events by Number of Road Traffic Lanes................ 18

Figure 14. Snapshot of a Driver Talking on Phone on an Approach to a Highway-Rail Grade 

Crossing ............................................................................................................................ 19

Figure 15. Snapshot of a Driver Text Messaging on an Approach to a Highway-Rail Grade 

Crossing ............................................................................................................................ 20

Figure 16. Snapshot of a Driver Eating on an Approach to a Highway-Rail Grade Crossing ..... 20

Figure 17. Distribution of Grade Crossing Events by Looking Behavior .................................... 23

Figure 18. Distribution of Looking Behavior by Warning Devices (excluding unknown 

category) ........................................................................................................................... 25

Figure 19. Distribution of Looking Behavior by Type of Crossing (excluding unknown 

category) ........................................................................................................................... 26

Figure 20. Research Vehicle Committing a Violation at a Crossbucks-Equipped Crossing........ 28

Figure 21. Type I Violation .......................................................................................................... 28

Figure 22. Type II Violation ......................................................................................................... 29

Figure 23. Type III Violation........................................................................................................ 29
vii

Tables

Table 1. Demographic of the Field Test Participants ..................................................................... 5

Table 2. Data Dictionary for Grade Crossing Events................................................................... 11

Table 3. Participants Exposure by Driver Group.......................................................................... 13

Table 4. Grade Crossing Events by Driver Demographic ............................................................ 14

Table 5. Frequency of Secondary Tasks....................................................................................... 21

Table 6. Distraction Rate by Age Group ...................................................................................... 22

Table 7. Distraction Rate by Number of Years with CDL ........................................................... 22

Table 8. Looking Behavior Rate by Route Type .......................................................................... 23

Table 9. Looking Behavior Rate by Age Group........................................................................... 24

Table 10. Looking Behavior Rate by Number of Years with CDL.............................................. 24

Table 11. Distribution of Violations by Warning Devices........................................................... 27
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, FRA, and Tarek Omar, D.Sc., Program Manager, Signals, Train Control and 

Communications Division, FRA, for their guidance 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 

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

Division, U.S. DOT Research and Innovative Technology Administration’s John A. Volpe 

National Transportation Systems (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

In order to improve safe driving behavior at highway-rail grade crossings, it is important to 

understand driver actions at or on approach to highway-rail grade crossings. Thus, in order to 

gain better understating of the problem, the U.S. Department of Transportation’s (U.S. DOT) 

Federal Railroad Administration’s (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 recently obtained 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 analysis of driver 

behavior at or on approach to highway-rail grade crossings. This report presents the findings 

related to heavy-truck driver behavior at and on approach to highway-rail grade crossings as 

analyzed from the IVBSS Heavy Truck (HT) FOT data.

Analysis of the IVBSS HT FOT data yielded a subset of 3,171 grade crossing events, or 

instances, where the IVBSS HT research vehicle traversed a grade crossing. The research team 

then reviewed and coded the 3,171 grade crossing events. The data collected for each grade 

crossing event included data 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, commercial drivers in the study were 

likely to engage in secondary tasks, an indicator of driver distraction, about 20.8 percent of the 

time. The data also indicated that pickup and delivery drivers, as well as drivers with a 

Commercial Driver’s License (CDL) for less than 22 years, were more likely to be engaged in 

secondary tasks compared to line-haul drivers and drivers who had a CDL for 22 years or more. 

The average length of CDL experience was 22 years for the study sample.

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 about 60.5 percent of the time. When 

analyzed by age group, the data revealed that younger drivers (less than the study average of 47 

years old) were more likely to look at least one way at or on approach to highway-rail grade 

crossings than older drivers (47 years old or older). Roughly 64.1 percent of younger drivers 

looked at least one way compared to 51.1 percent for older drivers. The data also indicated that 

at or on approach to highway-rail grade crossings, drivers with a CDL for 22 years or more 

looked at least one way 69.2 percent compared to 56.8 percent for drivers with a CDL for less 

than 22 years.

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 

research needed to identify and guide potential driver education/awareness strategies that would 

best mitigate risky driver behavior at grade crossings.
2

1 Introduction

In 1994, the U.S. Department of Transportation (U.S. DOT) Rail-Highway Crossing Safety 

Action Plan [1] 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 [2]. In 2006, the 

U.S. DOT Research and Innovative Technology Administration’s John A. Volpe National 

Transportation Systems Center (Volpe Center) completed a study to identify factors that aided in 

the successful reduction of these incidents. The study identified Commercial Driver Safety as 

the factor responsible for the largest reductions in incidents from 1994 to 2003 [3]. One of the 

major commercial driver safety components was the creation in 1999 of the Federal Motor 

Carrier Safety Administration (FMCSA), whose major purpose is to reduce the number and 

severity of large-truck crashes by emphasizing commercial vehicle safety. One of the FMCSA’s 

most significant accomplishments to date has been the passage of the Commercial Driver 

Disqualification law (CFR 383.51) in October 1999, which states that commercial drivers 

convicted of violating the highway-rail grade crossing warning devices will have their CDL 

suspended.

Figure 1 shows the incident rate per one billion Vehicle Miles Travelled (VMT) for commercial 

vehicles (Truck and Truck-trailer), and all other vehicle types over the past decade. As can be 

seen from the chart, commercial vehicles have the highest rate of incident per VMT, but they 

also have the largest reduction over that period. The incident rate decreased by 60.8 percent for 

commercial vehicles compared to 36.9 percent for all other vehicle types from 2001 to 2010.

However, in 2010, the commercial vehicle incident rate was still almost three times greater than 

the incident rate for all other vehicle types.

Figure 1. Incident Rate per Billon VMT for Commercial Vehicles and All Other Vehicle 

Types

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Incident Rate per Billion VMT

Year

All Other Motor Vehicles Commercial Vehicles
3

The need to conduct research on driver behavior issues at grade crossings was highlighted in the 

2009 US DOT Federal Railroad Administration’s Third Research Needs Workshop on HighwayRail Grade Crossing and Trespass Prevention sponsored by the Federal Railroad Administration 

(FRA) [4]. That workshop, held as a forum to exchange ideas, concepts, and strategic planning, 

resulted in the identification of high-priority research needs. Evaluating the effectiveness of 

motorist and pedestrian signs and treatment, as well as researching driver behavior, were

classified as top priorities.

In order to improve driver behavior at highway-rail grade crossings, it is important to understand 

driver actions at or on approach to grade crossings. Thus, to gain a better understating of the 

problem, the U.S. DOT FRA Office of Research and Development (R&D) funded a project to 

review and analyze driver activity at or on approach to highway-rail grade crossings. Volpe 

Center used data obtained in 2010 from the Integrated Vehicle Based Safety System (IVBSS) 

Heavy Truck Field Operational Test (FOT) sponsored by the U.S. DOT National Highway 

Traffic Safety Administration (NHTSA) for this effort focused on studying heavy-truck driver 

behavior at and on approach to highway-rail grade crossings.

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 was a cooperative research 

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

Transportation Research Institute (UMTRI) to assess the potential safety benefits and drivers’ 

acceptance of an integrated crash warning system [5]. 

The Volpe Center’s Advanced Vehicle Technology Division had performed an independent 

evaluation of the IVBSS program (in support of the NHTSA) which included analysis of video 

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

Crossing Safety and Trespass Prevention Research Program leveraged the NHTSA-sponsored 

evaluation program to perform research into driver behavior at or on approach to highway-rail 

grade crossings.

1.1 Project Objectives

The main objectives of this project were:

• To conduct a feasibility assessment of using the IVBSS Heavy Truck FOT data to 

perform highway-rail grade crossing driver behavior analyses 

• To collect and analyze driver activity at or on approach to highway-rail grade crossing

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

the IVBSS HT 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 was

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

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

2 Overview of the IVBSS Heavy Truck FOT Data

The majority of the background information presented in this section was obtained from the 

reports titled “Integrated Vehicle-Based Safety Systems Heavy-Truck Field Operational Test 

Independent Evaluation” [6] and “Integrated Vehicle-Based Safety Systems Heavy-Truck Field 

Operational Test Key Finding Report” [7].

The IVBSS HT FOT was conducted over a period of 10 months (mo) from February 2009 to 

December 2009. It included 20 commercial truck drivers from Con-way Freight, Inc. and 10

research vehicles. Each research vehicle was assigned to one pickup and delivery (P&D) and 

one line-haul (LH) driver. P&D drivers worked during day time, and LH drivers worked the 

night shift. Each driver drove a research vehicle for a period of 10 months. 

2.1.1 Characterization of the IVBSS HT FOT Fleet

The research vehicles were comprised of 10 single International ProStar 8600-series tractors.

The heavy truck 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 the roadway; and 

• 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 also includes a blind-spot detection (BSD) 

component that provides drivers with information about vehicles in their blind spot [7].

2.1.2 Participants

The 20 participants from Con-way Freight, Inc. were comprised of 10 P&D drivers and 10 LH

drivers. These driver categories were defined as follows:

Pickup and delivery drivers worked during the day, making many short trips throughout 

the metropolitan Detroit area to pick up and deliver goods. Line-haul drivers worked the 

night shifts, generally making one long round-trip delivery [6]. 

Although 20 drivers were recruited for the FOT, data from only 18 drivers was included in the 

analysis. As noted in the IVBSS report, two P&D drivers did not accumulate sufficient mileage 

during the test and therefore were not included for final analysis [6]. The 18 participants ranged 

in age from 32 to 63 years old, with an average age of 47 years. On average, LH drivers tended

to be older and had held CDLs for longer than P&D drivers, as shown in Table 1. Data for 

number of years with CDL was not available for driver number 29 and therefore that driver was 

not included in any analysis related to years with CDL.
5

Table 1. Demographic of the Field Test Participants

Driver 

Number Age

Years with 

CDL

Years 

Employed by 

Con-way Education Level

Pickup and delivery

1 46 22 15 High School

2 46 13 10 High School

4 32 10 10 High School

5 43 21 10 High School

6 44 12 9 High School

7 52 33 8 High School

8 38 14 11 High School

10 63 15 14 High School

Line-haul

21 51 30 25 High School

22 48 27 24 High School

23 54 35 21 High School

24 48 25 20 High School

25 45 21 15 Some College

26 52 23 18 High School

27 49 25 19 High School

28 46 25 12 Some College

29 53 ? 9 11th Grade

30 40 18 10 High School

Average P&D 45.5 17.5 10.9

Average LH 48.6 25.4 17.3

Average All 47.2 21.7 14.4

2.1.3 IVBSS HT FOT Data

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

were collected and stored in a Data Acquisition System (DAS) 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) [7]. 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 that was installed in each research 

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

location, and driving environment. The raw numerical data was 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 was collected from five cameras that were installed inside each research vehicle.

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

cabin/instrument panel, exterior left side of the vehicle, and exterior right side of the vehicle. 
6

For the IVBSS Heavy Truck FOT, the overall data set consisted of 21,289 trips covering a total 

of 497,385 miles (mi). Figure 2 and Figure 3 show the geographical range of FOT travel by the 

P&D and LH drivers, respectively, based on destination points [6]. Most driving, relative to 

mileage, took place in the lower peninsula of Michigan (63 percent) and in Ohio (33 percent), 

with a small portion taking place in northern Indiana (4 percent) [6]. Travel ranged as far north 

as Gaylord, Michigan, south to Cincinnati, Ohio, east to Lordstown, Ohio, and west to Gary,

Indiana. Based on drivers’ end destination, the study area selected for this research consisted of 

highway-rail grade crossings in Michigan, Ohio, and Indiana. Grade crossings in these three 

States, where most of the travel took place, were selected to cross-reference with routes traveled 

by each research vehicle to determine vehicle presence at highway-rail grade crossings.

Figure 2. Geographical Range of Driving by P&D Drivers
7

Figure 3. Geographical Range of Driving by LH Drivers
8

3 Grade Crossing Data Collection

The first step to analyzing driver behavior at highway-rail grade crossings was collecting data on 

driver activity at or on approach to such grade crossings. To perform this data collection, the 

Volpe Center research team developed four customized data collection tools to interface with 

and query the IVBSS FOT data. These tools are:

• 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 geo-location with the 

research vehicle geo-location to calculate if and when a vehicle was present at a crossing. 

Samples of driver’s end destinations were mapped to determine the participant travel destination. 

Based on this result, grade crossings in Michigan, Indiana, and Ohio were selected and crossreferenced 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 was obtained from the Bureau of 

Transportation Statistics National Transportation Atlas Database 2008. The geo-location of each 

research vehicle was obtained from the lat./long. coordinates recorded by an onboard Global 

Positioning System (GPS). Due to geo-location accuracy limitations inherent in both data sets, a 

radius of 100 feet (ft) around each grade crossing was used in querying the vehicle data in order 

to capture the events accompanying a research vehicle’s movement 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 4 provides a snapshot of the Grade Crossing 

Locator tool. 

Figure 4. Grade Crossing Locator Tool
9

3.2 Video Data Viewer

Driver activity at or on approach to a highway-rail grade crossing was collected (from the five 

video cameras installed in each research vehicle) and subsequently analyzed. The Video Data 

Viewer tool 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 5 provides 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. 

Figure 5. Video Data Viewer

3.3 Grade Crossing Coder

Figure 6 provides a snapshot of the Grade Crossing Coder. This tool was developed and used to 

record driver activity 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 during which a research vehicle did not encounter the pavement marking, 

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 (s) value was calculated based on 
10

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 activities, and crossing 

violations. 

Figure 6. Grade Crossing Coder

The left side of the screen in Figure 6 shows a schematic of a typical single track highway-rail 

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

driver characteristics and surrounding scene information for a grade crossing event. The first 

step in entering the data was inputting the driver and trip ID, and then selecting 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 as a data table. Table 2

provides a list of the data gathered from analysis by the Grade Crossing Coder of the video data

for each grade crossing event. Appendix D provides the data dictionary and information about

how the video was coded.
11

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 measure?

nearby_intersecting_road

Identifies whether intersecting road is present within 10 s of a 

crossing

nearby_intersecting_road_time Time research vehicle arrives at an intersecting road

nearby_intersecting_road_warning

_devices Warning devices at the intersecting road

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_v

iolation

Identifies whether vehicle in opposite direction committed 

violation

veh_same_approach_commit_viol

ation 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
12

3.4 Data Exporter

Figure 7 shows a screenshot of the Grade Crossing Exporter tool. This tool was used to export 

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

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

identified with the Grade Crossing Locator tool. The file was exported in a spreadsheet format 

with a list of possible trips, crossing IDs, 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 its grade crossing events. Refer to Appendices A, B, and C for 

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

event.

Figure 7. Data Exporter
13

4 Data Analysis

The analysis presented in this section concentrates on the heavy-truck part of the IVBSS FOT. 

Analysis of the heavy-truck driver behavior data at grade crossings focused on identification of 

patterns of driver behavior, and on distribution of crossing events by warning devices, vehicle 

exposure, and grade crossing violation. Examples of driver behavior characteristics include 

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

(phone, eating, talking to passenger, etc.). 

4.1 Vehicle Exposure

The IVBSS HT FOT spanned approximately 10 months from February 2009 to December 2009. 

During this period, the 18 participants drove research vehicles a total of 497,385 miles during a 

total of 21,289 trips. On average, P&D drivers each drove about 8,955 miles and LH drivers 

each drove about 42,574 miles during the test period. Additionally, P&D drivers encountered an 

average of 347 grade crossings and LH drivers, who drove most of the time on highways, 

encountered a much lower average of 39 grade crossings during the test period. Table 3 presents

statistics on mileage for all 18 P&D and LH drivers. 

Table 3. Participant Exposure by Driver Group

Driver

Number of 

Trips VMT

Crossing 

Events

Pickup and Delivery

1 1,268 9,263 335

2 2,459 9,621 607

4 3,682 10,047 588

5 2,584 10,981 394

6 965 4,392 86

7 3,168 11,230 262

8 2,011 6,669 250

10 1,160 9,440 255

Line-Haul

21 297 22,522 54

22 511 68,964 81

23 427 57,017 110

24 372 52,126 16

25 264 20,443 8

26 662 72,473 8

27 606 57,475 66

28 312 28,100 27

29 275 25,809 13

30 266 20,813 11

Average P&D 2,162 8,955 347

Average LH 399 42,574 39

Average All 1,183 27,633 176
14

4.2 Grade Crossing Events

For the IVBSS HT FOT, the 18 participants made a total of 21,289 trips in the research vehicles.

Of those trips, the Grade Crossing Locator tool identified 5,744 trips during 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 geo-location with the research vehicle’s geolocation to calculate if and when a vehicle was present at a crossing. Due to accuracy limitations 

in both data sets, a radius of 100 ft around each grade crossing was used in querying the vehicle 

data in order to capture the events occurring while a research vehicle traveled over a highwayrail grade crossing. The tool generated a list of possible grade crossing events within the 

previously identified 5,744 trips. The number of possible grade crossing events amounted to 

10,911 when the 100-ft zone around the crossing GPS coordinates was used. The research team 

reviewed the video from all of these potential grade crossing events and identified 2,891 trips

with a total of 3,171 grade crossing events. A total of 2,575 of the 10,911 possible events (23.6 

percent) were missing video data, which was an issue related to the IVBSS FOT itself. 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 at which the vehicle’s direction of travel was 

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

classify it as a grade crossing event.

The 3,171 valid grade crossing events selected for analysis occurred at 158 unique grade 

crossings. The results of this report are based on the analysis of that data set. Table 4 shows the 

breakdown of the grade crossing events by route type, age group, and years with a CDL. 

Overall, about 88 percent of the events occurred on P&D routes; drivers were younger than 47 

years old in approximately 73 percent of the events, and drivers had less than 22 years with a 

CDL in approximately 69 percent of the events. 

Table 4. Grade Crossing Events by Driver Demographic

Demographic Group Number of 

Grade Crossing 

Events

Route Type Pickup and delivery 2,777

Line-haul 394

Age Greater than 47 years old 865

Less than 47 years old 2,306

Years with CDL Greater than or equal to 22 959

Less than 22 2,199

Unknown (Driver # 29) 13
15

4.3 Summary of Grade Crossing Event Data

The identification of 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 devices present at the crossing. Figure 8, 

Figure 9, and Figure 10 show some examples of warning devices identified at selected grade 

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

devices 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

Figure 8. Snapshot of an Event at a Highway-Rail Crossing Equipped with Gates
16

Figure 9. Snapshot of an Event at a Highway-Rail Crossing Equipped with Flashing Lights

Figure 10. Snapshot of an Event at a Highway-Rail Crossing Equipped with only 

Crossbucks
17

Figure 11 shows the distribution of the 3,171 grade crossing events by warning device present at 

the crossing. As can be seen, a majority of the grade crossing events (91 percent or 2,891) 

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

data set included gates and flashing lights. Most of the remaining grade crossing events (9

percent or 275) occurred at passive crossings. Passive crossings in this data set consisted of 

those equipped with crossbucks. An additional five events occurred at crossings where the 

warning device was not identifiable from the video data due to poor video quality or 

environmental factors. Although three other warning device categories, “Four Quadrant Gate,” 

“STOP sign,” and “Other,” were listed in the study’s coding drop down menu, none of the 3,171

grade crossing events were coded under those categories.

Figure 11. Distribution of Grade Crossing Events by Warning Device

Figure 12 shows the distribution of grade crossing events by the number of tracks at the crossing.

As shown in Figure 12, 38.3 percent (1,216) of the grade crossing events occurred at double

track highway-rail grade crossings.

86.0%

5.2%

8.7%

0.2%

Gates

Lights

Crossbucks

Unknown
18

Figure 12. Distribution of Grade Crossing Events by Number of Tracks

Figure 13 shows the distribution of grade crossing events by number of traffic lanes in each 

direction traversing the crossing. As shown, about 55 percent (1,749) of the grade crossing 

events occurred at highway-rail grade crossings on single lane roadways. Approximately 44

percent of the events occurred on two-lane roadways.

Figure 13. Distribution of Grade Crossing Events by Number of Road Traffic Lanes

34.2%

38.3%

22.4%

5.0%

0.1%

1

2

3

4

>5

55.2%

43.6%

1.2% 0.0%

1

2

3

4
19

4.4 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 departing from the location of 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 and cabin shots captured by =

video data of two sides of grade crossing events. Out of a possible 3,171 grade crossing events, 

660 (or 20.8 percent) involved the driver performing a secondary task. The most frequently 

observed secondary task involved drivers talking on/listening to phone (205 or 6.5 percent), an 

example of which is shown in Figure 14. The next most common distraction was 

smoking/lighting cigarettes (156 or 4.9 percent). Figure 15 and Figure 16 show some other 

examples of drivers distracted on their approach to a highway-rail grade crossing. Table 5 lists 

all possible secondary tasks along with their frequency.

Figure 14. Snapshot of a Driver Talking on Phone on an Approach to a Highway-Rail 

Grade Crossing
20

Figure 15. Snapshot of a Driver Text Messaging on an Approach to a Highway-Rail Grade 

Crossing

Figure 16. Snapshot of a Driver Eating on an Approach to a Highway-Rail Grade Crossing
21

Data was analyzed to determine whether the distraction was any different based on route type, 

age group, or number of years with a CDL. Overall, P&D drivers were engaged in secondary 

tasks more often than LH drivers (P&D drivers were engaged in secondary tasks in 22.2 percent 

of grade crossing events compared to 11.2 percent for LH drivers). A paired t-test indicated that 

the overall change in rate of drivers involved in secondary tasks between P&D and LH drivers 

was statistically significant (p=0.000). Table 5 offers a comparison of secondary tasks between 

P&D and LH drivers.

Table 5. Frequency of Secondary Tasks

Secondary Tasks Number of Grade Crossing 

Events with Secondary 

Task

Total Frequency

P&D LH

None 2,161 350 2,511 79.2%

Talking on/listening to 

phone 192 13 205 6.5%

Smoking/lighting 

cigarettes 156 0 156 4.9%

Eating 76 1 77 2.4%

Reaching for object in 

vehicle 56 14 70 2.2%

Text messaging 46 1 47 1.5%

Drinking 41 2 43 1.4%

Looking to the 

side/outside car 14 2 16 0.5%

Adjusting controls 11 3 14 0.4%

Other 9 1 10 0.3%

Dialing phone 8 1 9 0.3%

Reading 5 2 7 0.2%

Singing/whistling 1 3 4 0.1%

Eyes closed > 1s 1 1 2 0.1%

Total 2,777 394 3,171

Secondary task frequency was also analyzed based on participants’ age and number of years with 

a CDL. Drivers were divided into two age groups (less than the study average of 47 years old 

and greater than that average). Data indicated that drivers in the younger group were engaged in 

secondary tasks in about 20.5 percent of grade crossing events compared to 21.6 percent for 

drivers in the older group. Based on paired t-test, the change in rate of secondary task frequency 

between the two age groups was not statistically significant (p=0.494). Table 6 shows the 

distraction rate by age group.
22

Table 6. Distraction Rate by Age Group

Age Group

Younger 

than 47 

years old

Older than 47 

years old

Grade Crossing Events with Driver 

Distraction 473 187

Total Grade Crossing Events

2,306 865

Distraction Rate 0.205 0.216

Analysis of secondary task frequency based on number of years with a CDL (less than the study 

average of 22 years, or 22 years and greater) indicated that drivers with a CDL for at least 22 

years were less likely to engage in secondary tasks during a grade crossing event than drivers 

with a CDL for less than 22 years. About 15.4 percent of drivers with a CDL for at least 22 

years were engaged in a secondary task during a grade crossing event compared to 23.2 percent 

for drivers with a CDL for less than 22 years. A paired t-test indicated that the overall difference 

in rate of secondary tasks between these two driver groups was statistically significant (p=0.000). 

Table 7 shows the distraction rate by number of years with CDL.

Table 7. Distraction Rate by Number of Years with CDL

Age Group

Less than 22 years 

with CDL

22 years or more with 

CDL

Grade Crossing Events 

with Distraction 511 148

Total Grade Crossing 

Events 2,199 959

Distraction Rate 0.232 0.154

4.5 Looking Behavior

Looking behavior was measured by 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 all grade crossing events to determine whether 

drivers 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 39.5 percent of the 

time as they approached a highway-rail grade crossing. About 29.7 percent of head movements 

were in both directions, and 30.8 percent of head movement was in one direction (looked left or 

looked right). Figure 17 shows the distribution of grade crossing events by looking behavior. 
23

Figure 17. Distribution of Grade Crossing Events by Looking Behavior

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

route type, age group, and number of years with a CDL. Looking behavior was further 

categorized into either “looked straight” or “looked at least one way.” P&D drivers looked at 

least one way 60.5 percent of the time and LH drivers looked at least one way 61.2 percent of the 

time at or on an approach to a grade crossing. This observed difference in driver looking 

behavior was not statistically significant based on a pair t-test (p=0.788). Table 8 shows looking 

behavior rate by route type.

Table 8. Looking Behavior Rate by Route Type

Route Type

P&D LH

Grade Crossing Events where 

drivers looked at least one way 1,679 241

Total Grade Crossing Events 2,777 394

Looking Behavior Rate 0.605 0.612

Grade Crossing events where drivers looked at least one way on their approach to grade 

crossings were analyzed per age group (younger than 47 years old and older than 47 years old). 

The data analysis of the looking behavior indicated that at or on an approach to a grade crossing

drivers younger than 47 years old were 64.1 percent likely to look at least one way compared to 

29.7%

30.8%

39.5%

Looked Both Ways

Looked One Way

Looked Straight
24

51.1 percent for drivers older than 47 years. Based on the paired t-test, the observed difference 

between drivers younger than 47 years old and drivers older than 47 years was statistically 

significant (p=0.000). It should be noted that seven out of the nine drivers older than 47 years in 

the study group were assigned to LH routes, where the frequency of encounters with grade 

crossings was much lower than those for the P&D routes. Table 9 shows looking behavior rate 

by age group.

Table 9. Looking Behavior Rate by Age Group

Age Group

Less than 47 years 

old

Greater than 47 years 

old

Grade Crossing Events where 

drivers looked at least one way 1,478 442

Total Grade Crossing Events 2,306 865

Looking Behavior Rate 0.641 0.511

Analysis of looking behavior based on number of years with a CDL (less than 22 years, and 22 

years or more) indicated that drivers with a CDL for at least 22 years were more likely to look at 

least one way at or on approach to a grade crossing than drivers with a CDL for less than 22 

years. About 69.2 percent of drivers with a CDL for 22 years or more looked at least one way at 

or on approach to a grade crossing compared to 56.8 percent for drivers with a CDL for less than 

22 years. A paired t-test indicated that the overall difference in rate of looking behavior between 

these two groups of drivers was statistically significant (p=0.000). It should be noted that seven 

out of the nine drivers with a CDL for at least 22 years in the study group were assigned to LH 

routes, where the frequency of encounters with grade crossings was much lower than for the 

P&D routes. Table 10 shows the looking behavior rate by number of years with CDL.

Table 10. Looking Behavior Rate by Number of Years with CDL

Age Group

Less than 22 years 

with CDL

22 years or more with 

CDL

Grade Crossing Events where 

drivers looked at least one way 1,250 664

Total Grade Crossing Events 2,199 959

Looking Behavior Rate 0.568 0.692

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 gates (this behavior was exhibited at 62.2 percent of those 2,727 events) and least 

likely to look at least one way at crossings equipped with lights (this behavior was exhibited at 

36.0 percent of those 164 events). For crossbuck-equipped crossings only, drivers looked at least 
25

one way approximately 59 percent of the time (163 out of 275). Figure 18 illustrates the 

distribution of looking behavior by warning device, excluding unknown category.

Figure 18. Distribution of Looking Behavior by Warning Devices (excluding Unknown 

category)

Looking behavior was also analyzed between active and passive crossings in general. For this 

analysis, active crossings included highway-rail grade crossings equipped with gates and flashing

lights, and passive crossings included highway-rail grade crossings equipped with crossbucks. 

The data analysis of the looking behavior indicated that drivers looked at least one way 59.3 

percent of the time at passive crossings (163 of 275 events) compared to 60.7 percent of the time 

at active crossings (1,755 of 2,891 events). Figure 19 illustrates the distribution of looking 

behavior between active and passive crossings.

0%

10%

20%

30%

40%

50%

60%

70%

80%

Gates Lights Crossbucks

Looked at least one way Looked straight
26

Figure 19. Distribution of Looking Behavior by Type of Crossing (excluding Unknown 

category)

4.6 Grade Crossing Activation

For an active crossing, grade crossing activation was defined as any time the crossing warning 

devices were activated, either when a train was on approach or in the event of a false activation 

[8]. For a passive crossing, grade crossing activation was defined as when a highway-rail 

crossing was occupied by a train. Out of the possible 3,171 crossing events analyzed in this 

study, the research team identified 43 events (1.4 percent) involving grade crossing activation.

The 43 grade crossing activation events were comprised of 37 at crossings equipped with gates, 4 

at crossings equipped with flashing lights, and 2 at crossings equipped with crossbucks. Six out 

of the 41 activation events at active crossings were identified as false activation events, meaning 

that the warning devices were activated without a train being present. 

Grade crossing violations were collected for the 43 grade crossing activation events. A grade 

crossing violation occurs when motorists disobey the warning devices at the 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. These included 

other vehicles in front of or parallel to the research vehicle traveling in the same direction, and 

vehicles on opposite approach to the grade crossing. For those 43 grade crossing activation 

events, the research team identified 38 violations. There were an additional 10 instances for 

which the reviewer was not able to determine from the video data whether a nearby vehicle 

commited a violation. Table 11 provides distribution of violations by warning devices and type 

of vehicle. For the four violations that were commited by research vehicles, the drivers looked at 

least one way and were not involved in any secondary tasks in all four events. Two of the four 

violations occurred during false activation events.

0%

10%

20%

30%

40%

50%

60%

70%

80%

Active Passive

Looked at least one way Looked straight
27

Table 11. Distribution of Violations by Warning Devices

Warning 

Devices

Number of 

Activation 

Events

Number of Violations

by Research Vehicle

Number of 

Violations by Other 

Vehicles

Total

Violations

Gates 37 3 31 34

Lights 4 0 3 3

Crossbucks 2 1 0 1

Total 43 4 34 38

Three types of violations were collected for the 37 grade crossing activation events that occurred 

at crossings equipped with gates. The definition of each type is listed below [8].

• 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 34 violations for the 37 grade crossing activation events that 

occurred at crossings equipped with gates, as shown in Table 11. The 34 violations were 

comprised of 5 Type I violations (3 before and 2 after a train), 24 Type II violations (2 before 

and 22 after a train), and 5 Type III violations. All Type III violations occurred during three

false activation events. One out of the five type III violations was commited by a research 

vehicle. The driver in that event, which was also determined to be a false activation event, 

looked both ways before going around gates to traverse the crossing.

The research team identified three violations for the four 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. The three 

violations all occurred 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 crossbucks 

included one violation. That violation occurred when a research vehicle traversed the crossing 

before the train fully cleared the crossing as shown in Figure 20.

On thirteen occasions research vehicles were the lead vehicle at an activated crossing event. The 

drivers comitted a violation about 23 percent of the time (in 3 out of 13 events where the truck 

was the lead vehicle). The 13 grade crossing activation events consisted of 9 at crossings 

equipped with gates, 3 at crossings equipped with flashing lights, and 1 at crossings equipped 

with crossbucks. For the 9 grade crossing activation events at a gated crossing, research vehicles 

commited 2 Type I violations (all before a train). The research vehicle did not commit any 

violations for the three grade crossing activation events that occurred at crossings equipped with 

flashing lights. For the one grade crossing activation event at a crossing equipped with 

crossbucks, the research vehicle committed a violation by traversing the crossing before the train 
28

fully cleared the crossing, as shown in Figure 20. Figure 21, Figure 22, and Figure 23 show 

examples of each type of violation at crossings equipped with gates.

Figure 20. Research Vehicle Committing a Violation at a Crossbucks-equipped Crossing

Figure 21. Type I Violation
29

Figure 22. Type II Violation

Figure 23. Type III Violation
30

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, in 

order to gain a better understating of the problem, FRA’s Office of R&D funded this study to 

review and analyze driver activity 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 behavior analyses on heavy-truck vehicle drivers.

The second goal was to collect and analyze truck drivers’ activities at or on approach to

highway-rail grade crossings. Both of these goals were achieved through the research 

documented herein. 

This report reviews truck driver behavior using the IVBSS Heavy Truck FOT data set. The 18 

HT IVBSS FOT participants took 21,289 trips over the study period. Out of those trips, the 

research team identified 2,891 trips containing 3,171 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 3,171 grade crossing events. The data collected 

for each grade crossing event included data 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, about 20.8 percent of the time while traversing 

a grade crossing, and were likely to look at least one way at or on approach to grade crossings 

about 60.5 percent of the time. 

The analysis of secondary task by route type, driver’s age, and number of years with a CDL

revealed that the distraction did not differ significantly based on driver’s age. Younger drivers, 

those under the study average of 47 years old, were engaged in secondary tasks 20.5 percent of 

the time compared to 21.6 percent for older drivers (those over the study average of 47 years 

old). The data also indicated that P&D drivers were more likely to be engaged in secondary 

tasks than LH drivers. P&D drivers were engaged in secondary tasks at or on approach to grade 

crossings approximately 22 percent of the time, compared to 11 percent of the time for LH 

drivers. The data further indicated that drivers with a CDL for less than 22 years were more 

likely than drivers with a CDL for 22 years or more to be engaged in secondary tasks during 

grade crossing events. 

Looking behavior did not differ significantly between P&D and LH drivers. P&D drivers looked 

at least one way 60.5 percent of the time compared to 61.2 percent for LH drivers. But when 

analyzed by age group, the data revealed that younger drivers were more likely to look at least 

one way at or on approach to highway-rail grade crossings than older drivers. Younger drivers 

looked at least one way approximately 64.1 percent of the time compared to 51.1 percent for 

older drivers. The data also showed that drivers with a CDL for 22 years or more looked at least 

one way approximately 69.2 percent of the time compared to 56.8 percent for drivers with a CDL 

for less than 22 years. It should be noted that this analysis is based on a small number of drivers 

that participated in the IVBSS FOT. Out of the overall pool of 18 professional truck drivers in 
31

the study, 7 out of 9 in the older age group were assigned to LH routes and 7 out of 9 with a CDL 

for at least 22 years were assigned also to LH routes, where the frequency of encounters with 

grade crossings was much lower than those for the P&D routes.

Analysis of looking behavior by warning devices suggested that drivers were most likely to look 

at least one way at crossings equipped with gates, followed very closely by crossings equipped 

with crossbucks; they were least likely to look at least one way at crossings equipped with lights.

The data also indicated that looking behavior did not differ significantly between active and 

passive crossings. Drivers looked at least one way in 59.3 percent of the events at passive 

crossings, compared to 60.7 percent of the events at active crossings. 

Follow-on research already being conducted by the research team will analyze additional data 

elements such as vehicle speed profiles on approach to grade crossings; the research will also 

provide a deeper analysis of the driver behavior elements presented in this report. 

Evaluating the effectiveness of motorist and pedestrian signs and treatment—and researching 

driver behavior—is a top research priority, as identified 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, such as Operation Lifesaver’s Commercial Drivers e-Learning initiative [9], which

would best mitigate risky driver behavior at grade crossings.
32

6 References

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

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

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

[3] Horton, S., Carroll, A., Chaudhary, M., Ngamdung, T., Mozenter, J., and Skinner, D., 

Success Factors in the Reduction of Highway-rail Grade Crossing Incidents from 

1994 to 2003, Report No. DOT/FRA/ORD-09/05. Washington, DC: Federal Railroad 

Administration, April 2009. http://www.fra.dot.gov/downloads/research/ord0905.pdf

[4] Carroll, A., daSilva, M., and Ngamdung, T., US DOT 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/downloads/Research/ord1001.pdf

[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] Nodine, E., Lam, A., Najim, W., Wilson, B., and Brewer, J., Integrated Vehicle-Based 

Safety Systems Heavy-Truck Field Operational Test Independent Evaluation, 

prepared for National Highway Traffic Safety Administration, Cambridge, MA: U.S. 

DOT/NHTSA, May 2011.

[7] Sayer, J., Bogard, S., Funkhouser, D., LeBlanc, D., Bao, S., Blankespoor, A., Buonarosa, 

M., and Winkler, C., Integrated Vehicle-Based Safety Systems Heavy-Truck Field 

Operation Test Key Findings Report, prepared for Research and Innovative 

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

DOT/RITA, August 2010.

[8] 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.

[9] http://oli.org/e-learning-survey/, last accessed June 11, 2012.
33

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
34

LaneWidth Lane width

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
35

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
36

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 based on computer clock

Vehicle Vehicle number

WarmStart True if ignition happened with the DAS already running
37

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 (s) 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 it stops

vibrating from going over the tracks. 

7. Warning Devices: Select the highest level of warning devices 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
38

o 4

o >4

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

If the traffic lane merges or expands 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
39

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 s 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 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

o Unsure
40

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 the train is present at the crossing, the train triggers the 

track circuitry to activate the safety devices at the crossing. For 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 a 

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 at the 

crossing. For 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 following 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 the gate has not 

descended.

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 drop 

down 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
41

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

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

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’s 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’s 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’s 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 description that best fits the 

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

necessarily mean that the driver is distracted; it may mean that the driver is looking 

around to determine if a train is arriving or has left. If the driver only looked straight 

ahead, then 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.
42

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.

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 a 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’s activities or roadway 

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

trip. 
43

Abbreviations and Acronyms

BSD Blind Spot Detection

CDL Commercial Driver’s License

DAS Data Acquisition System

FCW Forward Crash Warning

FMCSA Federal Motor Carrier Safety Administration

FOT Field Operational Test

FRA Federal Railroad Administration

GPS Global Positioning System

HT

Hz

Heavy Truck

Hertz

IVBSS Integrated Vehicle-Based Safety Systems

lat. Latitude

LCM Lane-Change/Merge

LDW Lane-Departure Warning

LH

long.

Line haul

Longitude

NHTSA National Highway Traffic Safety Administration

P&D

R&D

Pickup and Delivery

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

Volpe Center

Vehicle Miles Travelled

John A. Volpe National Transportation Systems Center