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Fuzzy logic systems for transportation engineering: the state of the art

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  • Teodorovic, Dus[caron]an

Abstract

The paper presents a classification and analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes. Fuzzy logic is shown to be a very promising mathematical approach to modeling traffic and transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed to solve various traffic and transportation engineering problems. Emphasis is put on the importance of fuzzy logic systems as universal approximators in solving traffic and transportation problems. Possibilities are shown regarding the further application of fuzzy logic in this field.

Suggested Citation

  • Teodorovic, Dus[caron]an, 1999. "Fuzzy logic systems for transportation engineering: the state of the art," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(5), pages 337-364, June.
    • Handle: RePEc:eee:transa:v:33:y:1999:i:5:p:337-364
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    1. Tzeng, Gwo-Hshiung & Teodorovic, Dusan & Hwang, Ming-Jiu, 1996. "Fuzzy bicriteria multi-index transportation problems for coal allocation planning of Taipower," European Journal of Operational Research, Elsevier, vol. 95(1), pages 62-72, November.
    2. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
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    2. Navid Khademi & Mojtaba Rajabi & Afshin S. Mohaymany & Mahdi Samadzad, 2016. "Day-to-day travel time perception modeling using an adaptive-network-based fuzzy inference system (ANFIS)," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 25-52, March.
    3. Ceder, Avishai & Chowdhury, Subeh & Taghipouran, Nima & Olsen, Jared, 2013. "Modelling public-transport users’ behaviour at connection point," Transport Policy, Elsevier, vol. 27(C), pages 112-122.
    4. Bera, Sharminda & Rao, K. V. Krishna, 2011. "Estimation of origin-destination matrix from traffic counts: the state of the art," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 49, pages 2-23.
    5. Sabounchi, Nasim S. & Triantis, Konstantinos P. & Sarangi, Sudipta & Liu, Shiyong, 2014. "Dynamic simulation modeling and policy analysis of an area-based congestion pricing scheme for a transportation socioeconomic system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 357-383.
    6. Kingsley Adjenughwure & Basil Papadopoulos, 2019. "Towards a Fair and More Transparent Rule-Based Valuation of Travel Time Savings," Sustainability, MDPI, vol. 11(4), pages 1-19, February.
    7. Chowdhury, Subeh & Ceder, Avishai (Avi), 2016. "Users’ willingness to ride an integrated public-transport service: A literature review," Transport Policy, Elsevier, vol. 48(C), pages 183-195.
    8. Chang, Yu-Hern & Yeh, Chung-Hsing, 2004. "A new airline safety index," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 369-383, May.
    9. Fatmi, Mahmudur Rahman & Chowdhury, Subeh & Habib, Muhammad Ahsanul, 2017. "Life history-oriented residential location choice model: A stress-based two-tier panel modeling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 293-307.
    10. Liu, Shiyong & Triantis, Konstantinos P. & Sarangi, Sudipta, 2010. "A framework for evaluating the dynamic impacts of a congestion pricing policy for a transportation socioeconomic system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 596-608, October.
    11. Arslan, Turan & Khisty, Jotin, 2006. "A rational approach to handling fuzzy perceptions in route choice," European Journal of Operational Research, Elsevier, vol. 168(2), pages 571-583, January.

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