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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 920301, 7 pages
http://dx.doi.org/10.1155/2014/920301
Research Article

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

1Jiangsu Key Laboratory of Urban ITS, Southeast University, 2 Si-Pai Lou, Nanjing, Jiangsu 210096, China
2Transportation Engineering and Infrastructure Systems, Department of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA
3College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing, Jiangsu 210098, China

Received 24 September 2013; Accepted 12 December 2013; Published 29 January 2014

Academic Editor: Huimin Niu

Copyright © 2014 Yongfeng Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 and -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, Precision , Recall , and Indicator , 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.