01631171

Component

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, the authors develop a joint reactive and proactive crash modeling framework by coupling the static monthly crash risk and dynamic real-time crash risk in a unified econometric framework for a microscopic analysis unit. In the joint modeling approach, the authors 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. The authors 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). While the real-time crash risk component is examined by using a random utility model employing different dynamic traffic attributes (volume, speed, lane occupancy and environmental conditions). The outcome of the proposed approach allows the authors to predict both the static and dynamic crash risk components simultaneously in a single econometric framework.

This paper was sponsored by TRB committee ABJ80 Standing Committee on Statistical Methods. Alternate title: Joint Framework for Static and Real-Time Crash Risk Analysis

01618707

17-05406

English

0000-0001-7901-3995

20p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Crash risk forecasting; Dynamic models; Econometric models; Multinomial logits; Real time information

Static models

Highways; Planning and Forecasting; Safety and Human Factors

Transportation Research Board Annual Meeting 2017 Paper #17-05406

TRIS, TRB, ATRI

Dec 8 2016 12:08PM