International Conference on Transportation and Development 2020
Hybrid Data-Fusion Model for Short-Term Road Hazardous Segments Identification Based on the Acceleration and Deceleration Information
Publication: International Conference on Transportation and Development 2020
ABSTRACT
Road hazardous segments (HS) identification is valid and useful to improve traffic safety and save life. Traditionally, researchers always use long-term historical crash data with the simulation model to find the HS, which is always a way of being wise after the tragedy. However, this paper promotes a scheme of short-term real-time HS identification scheme based on the traffic inconstancy features. Using accumulative historical floating vehicle data to show and capture the road-segment level of spatial-temporal inconstancy features, then combined with the real-time loop detector data along the same road segments, his paper uses an innovative hybrid model called the standard deviation accident model (SDAM) to identify the three risky levels of HS, suspected, hazardous, and dangerous. A day was broken into 83-hour short-term periods, and the model is fitted by two weeks crash data and validated by 3 days, and achieve the accuracy over 73% for identifying the short-term hazard on each road segment (average length 0.826 km) of total 177 road segments. The success of the SDAM model is mainly due to the well-use of the inconstancy information captured from the long-term historical trajectory data. It was found that the inconstancy of speed and deceleration are close related to the HS identification (with a p-value less than 0.001) significantly. The features of HS road segments are summarized, and the safety improvement suggestions are provided at the end of the paper.
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Published In
International Conference on Transportation and Development 2020
Pages: 313 - 326
Editor: Guohui Zhang, Ph.D., University of Hawaii
ISBN (Online): 978-0-7844-8314-5
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© 2020 American Society of Civil Engineers.
History
Published online: Aug 31, 2020
Published in print: Aug 31, 2020
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