Driver Behavior at Simulated Railroad Crossings

Document Type

Conference Proceeding

Publication Date

1-1-2018

Department

Department of Cognitive and Learning Sciences

Abstract

Highway-rail grade crossing collisions and fatalities have been in decline for several decades, but a recent ‘plateau’ has spurred additional interest in novel safety research methods. With the support of Federal Railroad Administration (FRA), Michigan Tech researchers have performed a large-scale study that utilizes the SHRP2 Naturalistic Driving Study (NDS) data to analyze how various crossing warning devices affect driver behavior and to validate the driving simulation data. To this end, representative crossings from the NDS dataset were recreated in a driving simulator. This paper describes driver behavior at simulated rail crossings modeled after real world crossings included in the NDS dataset. Results suggest that drivers may not react properly to crossbucks and active warnings in the off position. Participants performed the safest behaviors in reaction to STOP signs. The majority of participants also reported an increase in vigilance and compliant behaviors after repeated exposure to RR crossings, which was supported by the results of a linear regression analysis. Participants used the presence of active RR warnings (in the off position) as a cue that there is no oncoming train and it is safe to cross without preparing to yield (operationalized as visually scanning for a train and active speed reduction). Drivers react the most appropriately to STOP signs, but it is unclear whether or not these behaviors would lead to a decrease in train-vehicle collisions.

Publisher's Statement

© 2018, Springer International Publishing AG, part of Springer Nature. Publisher’s version of record: https://doi.org/10.1007/978-3-319-91397-1_49

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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