Task Order 5208
Transportation Safety Research
Investigation of Driver Behavior at Rail Crossings
David R. Ragland
Rail crossing crashes have declined in the past 30 years, both nationally and in California. This is largely because of deployment of a wide range of countermeasures at rail crossings, including signal systems, gating and grade separation programs. However, the number of crashes and subsequent injuries and deaths is still unacceptably high. In the three years 2000-2002 there were 572 highway-rail incidents at California at-grade crossings, resulting in 111 deaths. The problem could increase because of numerous recently constructed or planned light rail or commuter rail systems that cross busy urban streets in many cities (California cities include Los Angeles, San Francisco, Sacramento, San Diego, and San Jose) and also the increased automobile travel demand statewide and subsequent increased exposure.
The goal of this study is to develop a comprehensive model of rail crossing violations, based on Signal Detection Theory (SDT) concepts and incorporating previous research, that can (i) predict violations under different conditions (listed above) and (ii) predict driver response to different countermeasure configurations (e.g., variation in design, timing, etc.). The development of the model will begin with a thorough review of the available literature, and will be an iterative process, providing hypotheses for, and being modified based on, the results of the following two tasks:
The project will consist of five tasks: (i) Conduct literature/data review, (ii) Develop a comprehensive SDT model; (iii) Develop video/radar observation model; (iv) Develop naturalistic data collection model; (v) Prepare final report.
Task 1. Literature/Data Review
Task 2. Develop a Comprehensive Signal Detection Theory (SDT)-based ModelThis task is initially based on the literature review, and will result in a comprehensive model for predicting violations and misjudgments under different conditions and for predicting driver response to different signal configurations (e.g., variation in design, timing, etc.) or other countermeasures. The development of the model will be an iterative process, providing hypotheses for, and being modified based on the results, of the following two activities.
The first iteration of the model will generate hypotheses, requirements, and specifications for video observation of rail crossings to observe driver behavior under different conditions (Task 3) and direct observation of drivers as they perceive speed and arrival time of trains, and as they make decisions in an instrumented vehicle (Task 4).
Task 3. Develop and Test a Video/Radar Observation ModelIn this task we will develop and demonstrate an observation model for driver behavior at rail crossings. Driver behavior (e.g., approach behavior, violation, crossing speed) will be viewed as a function of objectively measured variables (e.g., speed/distance of train, frequency of trains, length of train, etc.). The system will be composed of cameras and radar sensors. The cameras will be mounted to view the grade crossing in each direction of travel of the highway and railroad as well as crossing signal status. The radar sensors will be mounted to record highway traffic in each direction of the highway traffic and one for the railroad traffic. The radar will be used to record the velocity profile, direction, and distance of the vehicles and trains
Task 4. Develop and Pilot a Naturalistic Data Collection ModelThis task will consist of two subtasks. The goal of this data collection is to describe nominal driver behavior at a crossing in terms of vehicle control (speed, acceleration) and scanning patterns (data collected via an eye tracker). In the first subtask we will collect data on perception of speed (of a train) by human observers. We will ask our observers to carefully study an approaching train and then to make a judgment as to whether or not a closely following (~ 2 minutes delay) rail-equipped pick-up truck traveling the same direction on the same tracks is going faster or slower (it will have been going the identical speed). We will test the important hypothesis that people underestimate the speed of large objects (e.g., trains). Speed perception can then be factored into our model.
In the second subtask we will study the perception and understanding of rail crossings by developing and demonstrating a model using an instrumented vehicle to study the behavior of individual drivers at rail crossings. Individual differences in behavior as a function of driver characteristics (e.g., age, gender) and driver perception (e.g., perception of speed, distance, frequency, length) will be observed.
Task 5. Final ReportThe report will contain (i) summary of literature review, analysis of existing data, and description of existing and newly developed signs, signals, and gates, (ii) description of the model for predicting driver violations and response to countermeasures, (iii) results from the video observation of drivers, (iv) results of observation using the instrumented vehicle and of speed perception survey, and (v) recommendations for a comprehensive plan for reducing rail crossing incidents in California.