Citation

Chen F, Chen S, Ma X. J. Saf. Res. 2018; 65: 153-159.

Affiliation

College of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China. Electronic address: xiaoxiang.ma@tongji.edu.cn.

Copyright

(Copyright © 2018, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2018.02.010

PMID

29776524

Abstract

INTRODUCTION: Driving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction.

METHOD: Crash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models.

RESULTS: Model estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood.

CONCLUSIONS: The study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated.

Copyright © 2018 National Safety Council and Elsevier Ltd. All rights reserved.

Language: en

Keywords

Big data; Mixed logit model; Random parameter; Real-time driving environment; Refined temporal scale