Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian Theory

Abstract

Traffic incident response plan, specifying response agencies and their responsibilities, can guide responders to take actions effectively and timely after traffic incidents. With a reasonable and feasible traffic incident response plan, related agencies will save many losses, such as humans and wealth. In this paper, how to generate traffic incident response plan automatically and specially was solved. Firstly, a well-known and approved method, Case-Based Reasoning (CBR), was introduced. Based on CBR, a detailed case representation andR5-cycle of CBR were developed. To enhance the efficiency of case retrieval, which was an important procedure, Bayesian Theory was introduced. To measure the performance of the proposed method, 23 traffic incidents caused by traffic crashes were selected and three indicators, PrecisionP, RecallR, and IndicatorF, were used. Results showed that 20 of 23 cases could be retrieved effectively and accurately. The method is practicable and accurate to generate traffic incident response plans. The method will promote the intelligent generation and management of traffic incident response plans and also make Traffic Incident Management more scientific and effective.