A Joint Framework For Static And Real-Time Crash Risk Analysis

Keywords

Case-control binary logit; Expressways; Joint econometric frameworks; Multinomial logit models; Real-time crash risk

Abstract

The current research effort bridges the gap between traditional crash risk and real-time crash risk models by developing a joint model that accommodates for both dimensions in developing crash risk analysis models. Specifically, we develop a joint reactive and proactive crash modeling framework by coupling the monthly crash risk and real-time crash risk in a unified econometric framework for a microscopic analysis unit. In the joint modeling approach, we propose and estimate an alternative to the case-control binary logit based real-time crash risk analysis by proposing a multinomial logit based approach where time periods serve as alternatives and the chosen alternative is the time period in which crash occurs. The joint model also allows us to accommodate for the common unobserved factors that increase the likelihood of a crash in microscopic unit to affect the real-time crash risk propensity. We demonstrate the application of the proposed approach by using data on roadway segments from three expressways in Central Florida (State Roads 408, 417, and 528) for 29 months. The monthly crash risk component is examined by using binary logit model employing different static roadway attributes (roadway geometry and operational attributes). The real-time crash risk component is examined by using a multinomial logit model employing different real-time traffic attributes (volume, speed, lane occupancy and environmental conditions). The outcome of the proposed approach allows us to predict both the monthly and real-time crash risk components simultaneously in a single econometric framework.

Publication Date

6-1-2018

Publication Title

Analytic Methods in Accident Research

Volume

18

Number of Pages

45-56

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.amar.2018.04.001

Socpus ID

85046709415 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85046709415

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