@article{ref1,
title="Real-time estimation of accident likelihood for safety enhancement",
journal="Journal of transportation engineering",
year="2005",
author="Oh, JS and Oh, Cheol and Ritchie, Stephen G. and Chang, Mingway",
volume="131",
number="5",
pages="358-363",
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.   <p>Language: en</p>",
language="en",
issn="0733-947X",
doi="",
url="http://dx.doi.org/"
}