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Prediction of rear-end conflict frequency using multiple-location traffic parameters

journal contribution
posted on 01.04.2021, 09:15 by Christos Katrakazas, Athanasios TheofilatosAthanasios Theofilatos, Md Ashraful Islam, Eleonora Papadimitriou, Loukas Dimitriou, Constantinos Antoniou
Traffic conflicts are heavily correlated with traffic collisions and may provide insightful information on the failure mechanism and factors that contribute more towards a collision. Although proactive traffic management systems have been supported heavily in the research community, and autonomous vehicles (AVs) are soon to become a reality, analyses are concentrated on very specific environments using aggregated data. This study aims at investigating –for the first time- rear-end conflict frequency in an urban network level using vehicle-to-vehicle interactions and at correlating frequency with the corresponding network traffic state. The Time-To-Collision (TTC) and Deceleration Rate to Avoid Crash (DRAC) metrics are utilized to estimate conflict frequency on the current network situation, as well as on scenarios including AV characteristics. Three critical conflict points are defined, according to TTC and DRAC thresholds. After extracting conflicts, data are fitted into Zero-inflated and also traditional Negative Binomial models, as well as quasi-Poisson models, while controlling for endogeneity, in order to investigate contributory factors of conflict frequency. Results demonstrate that conflict counts are significantly higher in congested traffic and that high variations in speed increase conflicts. Nevertheless, a comparison with simulated AV traffic and the use of more surrogate safety indicators could provide more insight into the relationship between traffic state and traffic conflicts in the near future.

History

School

  • Architecture, Building and Civil Engineering

Published in

Accident Analysis & Prevention

Volume

152

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Accident Analysis and Prevention and the definitive published version is available at https://doi.org/10.1016/j.aap.2021.106007.

Acceptance date

17/01/2021

Publication date

2021-02-05

Copyright date

2021

ISSN

0001-4575

eISSN

1879-2057

Language

en

Depositor

Dr Akis Theofilatos. Deposit date: 29 March 2021

Article number

106007