Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 54, Issue -, Pages 176-186Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2015.03.006
Keywords
Urban expressway; Real-time crash prediction; Dynamic Bayesian network; Traffic states; Speed conditions data
Categories
Funding
- Natural Science Foundation of China [51278362, 51422812]
- New Century Excellent Talents in University [NCET13-0425]
- Fundamental Research Funds for the Central Universities
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Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy. (C) 2015 Elsevier Ltd. All rights reserved.
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