Abstract
AbstractDuring heavy rains, traffic monitoring is limited, as the affected areas are monitored by reporting and patrolling. In this study, a method for detecting traffic anomalies during heavy rainfall events was established, and a model that uses probe vehicle data to detect traffic anomalies during a disaster (an event in which vehicles make U-turns in front of a damaged area) was proposed. In addition, a parameter calibration method was developed for the model using past disaster-related data. The generalizability of the calibrated model was evaluated by applying it to other disasters. According to the results, the proposed model exhibited good generalizability.
Funder
Japan Society for the Promotion of Science
Publisher
Springer Science and Business Media LLC
Subject
General Neuroscience,Computer Science Applications,Software,Automotive Engineering,Applied Mathematics,Control and Systems Engineering,Aerospace Engineering,Information Systems
Reference22 articles.
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