A fuzzy-based methodology for anticipating trend of incident traffic congestion on expressways
Email:
trinhdinhtoan@tlu.edu.vn
Từ khóa:
fuzzy logic, traffic control, multi-stage, incident management, fuzzy rule, decision support system.
Tóm tắt
Traffic control decisions for incident congestion management on expressways are often made in the face of uncertainty because it entails using many forms of both current and predicted traffic data and incident information to arrive at control decisions under critical-time pressure. For these reasons, an effective traffic control strategy during incidents often relies on techniques that deal efficiently with problems of uncertainty and imprecision. Motivated by this, the author has carried out a research project that develops a multi-stage Fuzzy Logic Controller (MS-FLC) as a tool to support traffic operator’s decision-making at the operational level. The research project aims at establishing a systematic procedure in deriving control actions for ramp control during incidents on expressways following fuzzy-logic approach. For proactive ramp control, the trend of traffic condition on expressways during incidents should be properly anticipated. This paper presents the first two stages of the MS-FLC: (1) evaluation of traffic condition upon incident occurrences, and (2) anticipation of traffic condition during incidents. The results show that the MS-FLC provides a systematic procedure in deriving control actions using fuzzy-based methodology, which possesses excellent capabilities in data-handling and knowledge representation to deliver linguistic expressions that is easy to understand by the operators for making decisions. With both current and anticipated types of information obtained from these two stages, the MS-FLC operates on both reactive and proactive control manners so as to enhance performance of the incident management on expressways.Tài liệu tham khảo
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[2]. S. R. Kukadapwar, D.K. Parbat, Modeling of traffic congestion on urban road network using fuzzy inference system, American Journal of Engineering Research, 4 (2015) 143-148.
[3]. T.D. Toan, Y.D. Wong, Fuzzy logic-based methodology for quantification of traffic congestion. Physica A: Statis. Mec. and its App. 570, 2021, 125784.
[4]. J. Mendel, Fuzzy logic systems for engineering: a tutorial, Proceedings of the IEEE, 83 (1995) 345-377.
[5]. D. Zhao, Y. Dai, Z. Zhang, Computational intelligence in urban traffic signal control: a survey. IEEE Transactions on Systems, Man, and Cybernetics, (2012) 485-494.
[6]. U. F. Eze, I. Emmanuel, E. Stephen, Fuzzy logic model for traffic congestion, IOSR Journal of Mobile Computing & Application, 1 (2014) 15-20.
[7]. A. John, Z. Yang, R. Riahi, Application of a collaborative modelling and strategic fuzzy decision support system for selecting appropriate resilience strategies for seaport operations, Journal of Traffic and Transportation Engineering (English Edition), 1 (2014) 159-179.
[8]. X. Li, Y. Liu, Y. Wang, Evaluating transit operator efficiency: an enhanced DEA model with constrained Fuzzy-AHP Cones, Journal of Traffic and Transportation Engineering (English Edition), 3 (2016) 215-225.
[9]. Q. Li, F. Qiao, L. Yu, Socio-demographic impacts on lane-changing response time and distance in work zone with drivers' smart advisory system, Journal of Traffic and Transportation Engineering (English Edition), 2 (2015) 313-326.
[10]. T. D. Toan, S. H. Lam, Development of a rule-based system for congestion management, in Transportation Research Board 84th Annual Meeting. Washington, D.C.: Transportation Research Board, 2005.
[11]. M.T. Tariq, A. Massahi, R. Saha, M. Hadi, Combining machine learning and fuzzy rule-based system in automating signal timing experts’ decisions during non-recurrent congestion, Transp. Res. Rec., 2674 (2020) 163-176
[12]. K. Hamad, S. Kikuchi, Developing a measure of traffic congestion - fuzzy inference approach, Transportation Research Record: Journal of the Transportation Research Board, 2770 (2002) 77-85.
[13]. L. Zhan, P. D. Prevedouro, User perceptions of signalized intersection level of service using fuzzy logic, Transportmetrica, 7 (2011) 279–296.
[14]. M. Collotta, L.L. Bello, G. Pau, A novel approach for dynamic traffic lights management based on Wireless Sensor Networks and multiple fuzzy logic controllers, Exp. Sys. with App, 42 (2015) 5403-5415. http://dx.doi.org/10.1016/j.eswa.2015.02.011
[15]. M. Kalinic, J.M. Krisp, Fuzzy inference approach in traffic congestion detection, Annals of GIS, 25 (2019) 329-336.
[16]. Y. Ge, A two-stage fuzzy logic control method of traffic signal based on traffic urgency degree, Mod. and Sim. in Eng., (2014) 694185. http://dx.doi.org/10.1155/2014/694185
[17]. Y.E. Hawas, M. Sherif, M.D. Alam, Optimized multistage fuzzy-based model for incident detection and management on urban streets, Fuz. Sets and Syst., 381 (2019) 78-104.
[18]. T.D. Toan, M. Meng, S.H. Lam, Y.D. Wong, Multi-stage fuzzy logic controller for expressway traffic control during incidents, Forthcoming paper, Journal of Transp. Eng. Part A: Systems (2022), in press (expected time: March 2022), https://doi.org/10.1061/JTEPBS.0000679
[19]. T.D. Toan, S.H. Lam, Y.D. Wong, M. Meng, Development and validation of a driving simulator for traffic control using field data, Physica A: Statis. Mec. and its App., 596 (2022)127201. https://doi.org/10.1016/j.physa.2022.127201
[20]. T. D. Toan, V. H. Truong, Support vector machine for short-term traffic flow prediction and improvement of its model training using nearest neighbor approach, Transp. Res. Rec., 2675 (2021) 362–373. https://doi.org/10.1177/0361198120980432
[21]. M. Meng, T.D. Toan, Y.D. Wong, S.H. Lam, Short-term travel-time prediction using support vector machine and nearest neighbor method, Transportation Research Record, (2022) 1–13. https://doi.org/10.1177/03611981221074371.
[22]. S. Peeta, H.S. Mahmassani, Multiple user classes real-time traffic assignment for online operations: A rolling horizon solution framework, Transp. Res. Part C: Emerging Technol., 3 (1995) 83-98.
[23]. T.D. Toan, Fuzzy based quantification of congestion for traffic control, Transport and Communications Science Journal, 72 (2021) 1-8.
[24]. J.G. Nicholas, L.A. Hoel, Traffic and Highway Engineering, fourth ed., University of Virginia, 2009.
[25]. Quadstone Paramics V5.1: Modeller Reference Manual. Quadstone Limited, Version No. 1.0. Quadstone Limited, Scotland, 2004.
[26]. MATLAB User Manual, R2016a, 2016.
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Nhận bài
22/01/2022
Nhận bài sửa
24/03/2022
Chấp nhận đăng
10/05/2022
Xuất bản
15/05/2022
Chuyên mục
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Kiểu trích dẫn
Trinh Dinh, T. (7600). A fuzzy-based methodology for anticipating trend of incident traffic congestion on expressways. Tạp Chí Khoa Học Giao Thông Vận Tải, 73(4), 381-396. https://doi.org/10.47869/tcsj.73.4.4
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