[1] Yang Shunxin, Ni Fujian, Chen Fei,. Hierarchical decision-making systemfor real-time freeway incident response [J]. Journal of Southeast University (English Edition), 2009, 25 (4): 536-540. [doi:10.3969/j.issn.1003-7985.2009.04.025]

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Hierarchical decision-making systemfor real-time freeway incident response()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

25

Issue:

2009 4

Page:

536-540

Research Field:

Traffic and Transportation Engineering

Publishing date:

2009-12-30

Info

Title:

Hierarchical decision-making systemfor real-time freeway incident response

Author(s):

Yang Shunxin;  Ni Fujian;  Chen Fei

School of Transportation, Southeast University, Nanjing 210096, China

Keywords:

freeway incident;  decision-making;  rule-based reasoning;  case-based reasoning;  Bayesian networks

PACS:

U491

DOI:

10.3969/j.issn.1003-7985.2009.04.025

Abstract:

The artificial intelligence technique is used to generate a freeway incident response plan. The incident response framework based on rule-based reasoning, case-based reasoning and Bayesian networks reasoning is presented. First, a freeway incident management system(RK-IMS)based on rule-based reasoning is developed and applied for incident management in the northern section of the Nanjing-Lianyunguang Freeway. Then, field data from the two-year long operations of the RK-IMS are analyzed. Representations of incident case structures and Bayesian networks(BNs)structures related to incident responses are deduced. Finally, the k-nearest neighbor(k-NN)algorithm is applied to calculate the similarities of the cases. The preplan generation and the control strategy by integrating the k-NN algorithm are also developed. The model is validated by using incident data of the year 2006 from the RK-IMS. The comparison results indicate that the proposed algorithm is accurate and reliable.

References:

[1] Jacob C, Abdulhai B. Automated adaptive traffic corridor control using reinforcement learning: approach and case studies [J].Transportation Research Record, 2006, 1959: 1-8.
[2] Chowdhury M, Sadek A, Ma Y, et al. Applications of artificial intelligence paradigms to decision support in real-time traffic management [J].Transportation Research Record, 2006, 1968: 92-98.
[3] Adel W S. Case-based reasoning for real-time traffic flow management [D]. Charlottesville, VA, USA: School of Engineering and Applied Science of University of Virginia, 1998.
[4] Madanat S, Cassidy M J, Teng H, et al. Decision-making system for freeway incident response using sequential hypothesis testing methods [J].Transportation Research Record, 1996, 1554: 228-235.
[5] Arti G. Application of knowledge-based expert systems to incident management on freeways [D]. Amherst, MA, USA: Department of Civil Engineering of University of Massachusetts, 1992.
[6] Stephen R, Neil A. A real-time expert system approach to freeway incident management [R]. Berkeley, CA, USA: University of California at Berkeley, 1991.
[7] Stephen R. A knowledge-based decision support architecture for advanced traffic management [J]. Transportation Research Part A, 1990, 24(1): 27-37.
[8] Papageorgiou M, Messmer A, Azema J, et al. A neural network approach to freeway network traffic control [J]. Control Eng Practice, 1995, 3(12): 1719-1726.
[9] Filippo L, Stephen R. Development and evaluation of a knowledge-based system for traffic congestion management and control [J]. Transportation Research Part C, 2001, 9(6): 433-459.
[10] Rust P, Abde-Rahim A, Hassan A. Evaluation freeway diversion route plans in integrated incident management systems under uncertainties [C]//Proceedings of the Fourth International Symposium on Uncertainty Modeling and Analysis. College Park, MD, USA, 2003: 46-60.
[11] Lee D, Jeng S. Integrated freeway incident management using data mining and expert systems [R]. Irvine, CA, USA:University of California at Irvine, 2004.
[12] Ghada M H. Risk-based decision support model for planning emergency response for hazardous materials road accidents [D]. Waterloo, Ontario, Canada:University of Waterloo, 2004.
[13] Nilsson N J. Artificial intelligence: a new synthesis[M]. San Fransisco, CA, USA: Morgan Kaufmann Publishers Inc, 1998.

Memo

Memo:

Biography: Yang Shunxin(1971—), male, doctor, lecturer, shunxin.yang@gmail.com.
Foundation item: The Natural Science Foundation of Jiangsu Province(No.BK2008308).
Citation: Yang Shunxin, Ni Fujian, Chen Fei. Hierarchical decision-making system for real-time freeway incident response[J]. Journal of Southeast University(English Edition), 2009, 25(4): 536-540.

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