Real-time Estimation of Accident Likelihood for Safety Enhancement

Abstract

Unlike conventional traffic safety studies that focused on histrionic data analyses, this study attempts to identify traffic conditions that might lead to a traffic accident from real-time freeway traffic data. An innovative feature of the study is to apply the concept, real-time and preaccident, to accident studies by integrating real-time capabilities in advanced traffic management and information systems (ATMIS). In this study, the traffic conditions leading to more accidents are defined as real-time accident likelihood, and the accident likelihood is estimated by employing a nonparametric Bayesian model. The main goal of the study is to remove hazardous traffic condition prior to accident occurrence by incorporating the real-time accident likelihood into ATMIS. This study estimates real-time accident likelihood from empirical data on I-880 freeway in California, and shows its applicability as an accident precursor.