A joint framework for static and real-time crash risk analysis. (June 2018)

• Record Type: Journal Article
• Authors: Yasmin, Shamsunnahar; Eluru, Naveen; Wang, Ling; Abdel-Aty, Mohamed A.
• Is Part Of: Analytic methods in accident research. Volume 18(2018)
• Page Start: 45
• Page End: 56
• Publication Date: 2018-06
• Subjects: Real-time crash risk -- Case-control binary logit -- Multinomial logit models -- Joint econometric frameworks -- Expressways; Accidents -- Research -- Methodology -- Periodicals; Accidents -- Prevention -- Periodicals; 363.100721
• DOI: 10.1016/j.amar.2018.04.001
• Languages: English
• ISSNs: 2213-6657

Abstract

Highlights: We bridge the gap between traditional crash risk and real-time crash risk models. We develop a monthly crash risk model using a binary logit model. We consider a multinomial logit formulation to examine real-time crash risk. We demonstrate our model using data on roadway segments from three expressways. 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.