research >>rail crossings >>Proposal outline

completed

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.

Rail crossings provide different levels of warnings, from descending barriers or four-quadrant gates down to mere stop signs at private crossings. Subsequently, crashes are either caused by people violating the signs, signals, and gates or people not perceiving an approaching train. Gating systems that cannot be violated are difficult and expensive (as there are 12,784 crossings in California, 7,847 public and 4,777 private) and it is imperative that we conduct research to determine the reason for violations and misjudgment. A considerable amount of research has already been conducted, identifying some of the following factors that may lead to violation or misjudgment.

Perception of speed/distance of the train (i.e., time of arrival of the train to the crossing)
Perception of waiting time (i.e., waiting time until the train arrives plus waiting time for the train to pass [related to speed and length of the train])
Perceived probably of injury given a crash
Value placed on different outcomes (e.g., avoiding a crash, having to wait for a train to pass, etc.).
Perceived frequency of trains at the crossing
Environmental factors (e.g., weather, lighting, traffic conditions, etc.)
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:

Develop and demonstrate a video/radar observation model for driver behavior at rail crossings. Driver behavior (e.g., approach behavior, violation, crossing speed) will be studied as a function of objectively measured variables (e.g., speed/distance of train, frequency of trains, length of train, etc.)
Develop and demonstrate 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.
This comprehensive model of rail crossing violations will be tailored for use in California, and can be used by Caltrans to (i) conduct a comprehensive evaluation of rail crossings throughout the state and (ii) prepare a cost-effective plan for continued reduction of rail crossing incidents. The project will benefit greatly by leveraging already existing assets at PATH, the Institute of Transportation Studies, the School of Public Health, and the School of Optometry, including extensive prior experience in rail crossing projects, expertise in SDT concepts, expertise in studies of perception, expertise in video monitoring, availability of, and expertise in use of, an instrumented vehicle, and access to bibliographic sources and relevant data bases.

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.