Estimating intersections' near-crash conflicts with the drone-based image-recording data

Yen-Lin Huang; Yen-Hsiang Chen

doi:10.55329/snjp4746

Authors

Yen-Lin Huang University of Maryland, the United State of America https://orcid.org/0009-0005-6862-4260
Yen-Hsiang Chen National Taiwan University, Taiwan https://orcid.org/0000-0002-5316-7678

DOI:

https://doi.org/10.55329/snjp4746

Keywords:

drone-based image-recording data (DIRD), Extra Brake Required to Avoid a Crash (EBRAC), near-crash conflicts, proactive approach, surrogate measures of safety (SMoS)

Abstract

Recognizing the fact that the information from historical crash data cannot sufficiently reflect the risk level of target intersections under the current driving populations, some traffic safety researchers proposed to use various surrogate measures of safety (SMoS) from either roadside video records or simulation results for estimating near-crash conflicts and performing proactive safety assessment. Along the same line of research but taking advantage of the high-quality drone-based image-recording data (DIRD), this study presents a new effective surrogate variable, named ‘Extra Brake Required to Avoid a Crash’ (EBRAC), which offers a convenient, reliable, and direct tool for traffic professionals to perform the same analyses and risk rankings for resource allocation. The proposed new surrogate variable, featuring its direct relevance to road users’ maneuvers, e.g. braking from high-precision time-varying braking rate information uniquely available from the DIRD, has reflected crash-prone contributors attributed to driving behaviors and intersections’ overall environments. Hence, after proper field calibration, the proposed method based on EBRAC is directly applicable for estimating near-crash conflicts at other intersections in the same region without further adjustment or employing other advanced statistical techniques to integrate the information from multiple safety surrogates. The effectiveness of the proposed method with the new safety surrogate has been evaluated with field data from five intersections and 20 approaches; the results confirm that its performances, evaluated with two popular statistical tests, are either comparable to or better than the two state-of-the-art methods.

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Author Biographies

Yen-Lin Huang, University of Maryland, the United State of America

Yen-Lin Huang is a post-doctoral associate at the University of Maryland. He recently received the Ph.D. degree in Civil and Environmental Engineering from the University of Maryland, College Park. His research interests include freeway operations, transportation management and control, transportation safety, and traffic simulation.

CRediT contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing—original draft, Writing—review & editing.

Yen-Hsiang Chen, National Taiwan University, Taiwan

Yen-Hsiang Chen is an Assistant Professor affiliated with the National Taiwan University, Taiwan. His research interests include traffic control, traffic safety, and traffic engineering. He is particularly interested in topics that both have academic rigor and have the potential to be applied to the real world.

CRediT contribution: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Visualization, Writing—original draft, Writing—review & editing.

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