Real-time estimation of accident likelihood for safety enhancement
- Authors
- Oh, un-Seok; Oh, Cheol; Ritchie, Stephen G.; Chang, Myungsoon
- Issue Date
- May-2005
- Publisher
- ASCE-AMER SOC CIVIL ENGINEERS
- Keywords
- Accident prevention; Bayesian analysis; Probability density functions; Traffic accidents; Traffic management; Traffic safety
- Citation
- JOURNAL OF TRANSPORTATION ENGINEERING, v.131, no.5, pp.358 - 363
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF TRANSPORTATION ENGINEERING
- Volume
- 131
- Number
- 5
- Start Page
- 358
- End Page
- 363
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/45993
- DOI
- 10.1061/(ASCE)0733-947X(2005)131:5(358)
- ISSN
- 0733-947X
- 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.
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Collections - COLLEGE OF ENGINEERING SCIENCES > DEPARTMENT OF TRANSPORTATION AND LOGISTICS ENGINEERING > 1. Journal Articles
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