Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian Theory
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 weal...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2014/920301 |
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| author | Yongfeng Ma Wenbo Zhang Jian Lu Li Yuan |
| author_facet | Yongfeng Ma Wenbo Zhang Jian Lu Li Yuan |
| author_sort | Yongfeng Ma |
| collection | DOAJ |
| description | 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 R5-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 P, Recall R, and Indicator F, 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. |
| format | Article |
| id | doaj-art-ad04b93517ce44f98c68a07e771ad557 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-ad04b93517ce44f98c68a07e771ad5572025-02-03T01:22:18ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2014-01-01201410.1155/2014/920301920301Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian TheoryYongfeng Ma0Wenbo Zhang1Jian Lu2Li Yuan3Jiangsu Key Laboratory of Urban ITS, Southeast University, 2 Si-Pai Lou, Nanjing, Jiangsu 210096, ChinaTransportation Engineering and Infrastructure Systems, Department of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USAJiangsu Key Laboratory of Urban ITS, Southeast University, 2 Si-Pai Lou, Nanjing, Jiangsu 210096, ChinaCollege of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing, Jiangsu 210098, ChinaTraffic 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 R5-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 P, Recall R, and Indicator F, 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.http://dx.doi.org/10.1155/2014/920301 |
| spellingShingle | Yongfeng Ma Wenbo Zhang Jian Lu Li Yuan Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian Theory Discrete Dynamics in Nature and Society |
| title | Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian Theory |
| title_full | Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian Theory |
| title_fullStr | Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian Theory |
| title_full_unstemmed | Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian Theory |
| title_short | Automated Generation of Traffic Incident Response Plan Based on Case-Based Reasoning and Bayesian Theory |
| title_sort | automated generation of traffic incident response plan based on case based reasoning and bayesian theory |
| url | http://dx.doi.org/10.1155/2014/920301 |
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