TY  - JOUR
PY  - 2005//
TI  - Real-time estimation of accident likelihood for safety enhancement
JO  - Journal of transportation engineering
A1  - Oh, JS
A1  - Oh, Cheol
A1  - Ritchie, Stephen G.
A1  - Chang, Mingway
SP  - 358
EP  - 363
VL  - 131
IS  - 5
N2  - 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>
LA  - en
SN  - 0733-947X
UR  - http://dx.doi.org/
ID  - ref1
ER  -