An AHP-ME-Based Vehicle Crash Prediction Model considering Driver Intention and Real-Time Traffic/Road Condition

Abstract

Although numerous studies have attempted to use vehicle motion data for real-time vehicle crash prediction, many driver behavior and road/environment factors (e.g., driving intention and pavement condition) have not been considered. In order to cope with increased complexity and extent crash risk assessment with the consideration of factors like driving intention and pavement condition, this paper (a) combines driver intention, vehicle motion, and dynamic traffic environment into the assessment of the conflict risk in real time, (b) establishes a hierarchical analysis model for quantitatively describing driving safety based on an Analytic Hierarchy Process (AHP), and (c) applies a Matter Element (ME) Model to take multiple factors, which are heterogeneous in terms of nature of analysis (quantitative or qualitative) and measure units, into account, and provide a comprehensive evaluation of vehicle crash risk. Finally, a set of simulation cases are used to compare the detection efficiency of the proposed method with ANN and SVM for vehicle collision. The example analysis shows that the proposed AHP-ME model can more accurately predict the collision risk of vehicles. Moreover, the proposed AHP-ME model provides an effective solution to unify multi-factors (driver intention, vehicle motion, and dynamic traffic environment) into an integrated decision-making framework.