Analysis of stationary and dynamic factors affecting highway accident occurrence: A dynamic correlated grouped random parameters binary logit approach

Fountas, Grigorios; Sarwar, Md Tawfiq; Anastasopoulos, Panagiotis Ch.; Blatt, Alan; Majka, Kevin

Abstract

Traditional accident analysis typically explores non-time-varying (stationary) factors that affect accident occurrence on roadway segments. However, the impact of time-varying (dynamic) factors is not thoroughly investigated. This paper seeks to simultaneously identify pre-crash stationary and dynamic factors of accident occurrence, while accounting for unobserved heterogeneity. Using highly disaggregate information for the potential dynamic factors, and aggregate data for the traditional stationary elements, a dynamic binary random parameters (mixed) logit framework is employed. With this approach, the dynamic nature of weather-related, and driving- and pavement-condition information is jointly investigated with traditional roadway geometric and traffic characteristics. To additionally account for the combined effect of the dynamic and stationary factors on the accident occurrence, the developed random parameters logit framework allows for possible correlations among the random parameters. The analysis is based on crash and non-crash observations between 2011 and 2013, drawn from urban and rural highway segments in the state of Washington. The findings show that the proposed methodological framework can account for both stationary and dynamic factors affecting accident occurrence probabilities, for panel effects, for unobserved heterogeneity through the use of random parameters, and for possible correlation among the latter. The comparative evaluation among the correlated grouped random parameters, the uncorrelated random parameters logit models, and their fixed parameters logit counterpart, demonstrate the potential of the random parameters modeling, in general, and the benefits of the correlated grouped random parameters approach, specifically, in terms of statistical fit and explanatory power.

• Journal Article Type: Article
• Acceptance Date: May 21, 2017
• Online Publication Date: Mar 7, 2018
• Publication Date: 2018-04
• Deposit Date: Sep 20, 2018
• Journal: Accident Analysis & Prevention
• Print ISSN: 0001-4575
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 113
• Pages: 330-340
• DOI: https://doi.org/10.1016/j.aap.2017.05.018
• Keywords: Public Health, Environmental and Occupational Health; Law; Safety, Risk, Reliability and Quality; Human Factors and Ergonomics; General Medicine
• Public URL: http://researchrepository.napier.ac.uk/Output/1302959