Browse > Article

Development of a Fuzzy-Genetic Algorithm-based Incident Detection Model with Self-adaptation Capability  

Lee, Si-Bok (영산대학교 교통물류시스템학과)
Kim, Young-Ho (영산대학교 교통물류시스템학과)
Publication Information
Journal of Korean Society of Transportation / v.22, no.4, 2004 , pp. 159-173 More about this Journal
Abstract
This study utilizes the fuzzy logic and genetic algorithm to improve the existing incident detection models by addressing the problems associated with "crisp" thresholds and model transferability (applicability). The model's major components were designed to be a set of the fuzzy inference engines, and for the self-adaptation capability the genetic algorithm was introduced in optimization(or training) of the fuzzy membership functions. This approach is often called "the hybrid of fuzzy-genetic algorithm" The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of performance measures such as detection rate, false alarm rate, and detection time. This study was not an effort for simple improvement of the model performance, but an experimental attempt to incorporate new characteristics essential for the incident detection model to be universally applicable for various roadway and traffic conditions. The study results prove that the initial objective of the study was satisfied, and suggest a direction that the future research work in this area must follow.
Keywords
ITS;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Ahmed, S.A. and A.R. Cook(1982). 'Discrete Dynamic Models for Freeway Incident Detection Systems'. Transportation Planning and Technology, Vol.7, pp.231-242   DOI   ScienceOn
2 Chen , C.-H., and G.-L. Chang(1993). 'A Dynamic Real-Time Incident Detection System For Urban Arterials: System Architecture and Preliminary Results'. Proceedings of Pacific Rim TransTech Conference , Vol. 1, Seattle, Washington. pp.98-104
3 Cheu, R.L., S.G. Ritchie, W.W. Recker, and B. Bavrian(1991). 'Investigation of Neural Network Model for Freeway Incident Detection' Program on Advanced Technology for the Highway, Institute of Transportation Studies, University of California, Berkeley, California
4 Han, L.D.. and A. D. May(1989). 'Artificial Intelligence Approaches for Urban Network Incident Detection And Control'. Institute of Transportation Studies. University of California, Berkeley, California
5 Hoose, N., M.-A. Vicencio, and X. Zhang (1992). 'Incident Detection in Urban Roads Using Computer Image Processing'. Traffic Engineering & Control. Vol.33, No.4, pp.236-244
6 Kevin N. Balke, P. E(1993), 'An Evaluation of Existing Incident Detection Algorithms', Research report 1232-20, Texas Transportation Institute
7 Khan, S.I., and S.G. Ritchie(May 1994). 'Incident Detection on Surface Streets Using Artificial Neural Networks'. Proceedings of the Intl Conference on Advanced Technologies in Transportation and Traffic Management, Singapore, pp.261-268
8 Sellam, S., A. Boulmakoul, and J.C. Pierrelee (1991). 'A Distributed Real Time Knowledge-Based System Using Video Image Processing for Junctions Automatic Incident Detection'. Proceedings of the Second International Conference on Applications of Advanced Technologies in Transportation Engineering, American Society of Civil Engineers, New York, New York, pp.351-356
9 장세봉(1997), '인공신경망을 이용한 고속도로 자동유고감지 모형의 개발', 박사학위논문, 서울대학교
10 Stephanedes, Y.J., and G. Vassilakis(1994). 'Intersection Incident Detection for IVHS' Paper submitted to 74th Annual Meeting of the Transportation Research Board, Washington, D.C.
11 데이터사이언스(주)홈페이지 (http://datascience.co.kr/mining_how_gen.htm)
12 Zbigniew Michalewicz (1996), 'Genetic Algorithms + Data Structures = Evolution Programs', Springer
13 강진기(1994), '검지기 간격에 따른 돌발상황검지 알고리즘의 돌발상황겸지시간 분석에 관한 연구', 석사학위논문, 서울대학교
14 Ivan, J.N., J.L. Schofer, C.R. Bhat, P.-C. Liu, F.S. Koppelman, and A. Rodriguez (1993). 'Arterial Street Incident Detection Using Multiple Data Sources: Plans for ADVANCE'. Proceedings of Pacific Rim TransTech Conference. Vol.1. Seattle, Washington, pp.429-435
15 Kaan Ozbay, and Pushkin Kachroo(1999), 'Incident Management in Intelligent Transportation Systems', Artech House
16 Elie Sanchez. Takanori Shibata, and Lotfi A. Zadeh (1997), 'Genetic Algorithms and Fuzzy Logic Systems', World Scientific
17 Levin, M. and G.M. Krause(1978). 'Incident Detection : A Bayesian Approach'. Transportation Research Record 682. TRB, National Research Council, Washington, D.C., pp.52-58
18 이영인 . 황준환 (2001), '간선도로 돌발상황 검지기법 개발연구', 대한교통학회지, 제19권 제2호, 대한교통학회, pp.73-87   과학기술학회마을
19 김영찬 . 엄성만(2004), '도시고속도로 비혼잡상황에서의 자동 돌발상황 감지 알고리즘 개발', 대한토목학회지, 제24권 제2D호, 대한토목학회, pp.167-173
20 Hall, F.L., Y. Shi, and G. Atala(1993). 'On-Line Testing of the McMaster Incident Detection Algorithm Under Recurrent Congestion'. Transportation Research Record 1394, TRB. National Research Council, Washington, D.C., pp.1-7
21 Sibok Lee (1995), Fuzzy Logic Based Incident Detection for Paired Intersections. A doctoral dissertation, Texas A&M University
22 Bell, M. G. H., and B 'Thancanamootoo(1988) Automatic Incident Detection Within Urban Traffic Control Systems. Proceedings of International Road and Traffic Conference Roads and Traffic 2000, Vol.4:2, pp.35-38
23 Stephanedes. Y.J., and A. P. Chassiakos (1993). 'Smoothing Algorithms for Incident Detection'. Transportation Research Record 1394, TRB, National Research Council, Washington, D.C., pp.8-16
24 채석 . 오영석(1997), '퍼지이론과 제어', 청문각