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Categorization Of Urban Traffic Congestion Based On The Fuzzification Of Congestion Index Value And Influencing Parameters

Author

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  • Nilanchal PATEL

    (Birla Institute of Technology Mesra, Ranchi, Jharkhand – 835215, India)

  • Alok Bhushan MUKHERJEE

    (Birla Institute of Technology Mesra, Ranchi, Jharkhand – 835215, India)

Abstract

Traffic congestion is a dynamic phenomenon; it is not possible to determine the actual degree of congestion prevailing on the field using sharp boundaries of the influencing parameters. To overcome this, in this paper we have employed fuzzy concept to fuzzify the two influencing parameters viz. congestion index value and average speed that facilitated the categorization of the congestion status into five different classes i.e. highly congested, high-moderate congested, moderate congested, low congested, least congested as compared to the only two congestion classes determined through the traditionally used congestion index value of the influencing parameters. For each route, pre-defined membership values (between 0 and 1) were assigned to the congestion index value and average speed respectively based on the empirical observations made in the field. Using the same logic, knowledge-based weights were assigned to the five different classes of congestion. Subsequently, fuzzy OR operation was performed on the membership values of the two influencing parameters for each route separately. Finally, different routes of the study area were categorized as one of the five classes of congestion based on the resultant value of the fuzzy OR operation. The research demonstrated that application of the fuzzy concept and knowledge-based congestion weights can provide better realistic status of the congestion in the field as compared to traditionally used congestion index value of the influencing parameters.

Suggested Citation

  • Nilanchal PATEL & Alok Bhushan MUKHERJEE, 2014. "Categorization Of Urban Traffic Congestion Based On The Fuzzification Of Congestion Index Value And Influencing Parameters," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 9(4), pages 36-51, November.
    • Handle: RePEc:rom:terumm:v:9:y:2014:i:4:p:36-51
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    References listed on IDEAS

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    1. Barth, Matthew & Boriboonsomsin, Kanok, 2010. "Real-World Carbon Dioxide Impacts of Traffic Congestion," University of California Transportation Center, Working Papers qt07n946vd, University of California Transportation Center.
    2. C. Robin Lindsey & Erik T. Verhoef, 2000. "Traffic Congestion and Congestion Pricing," Tinbergen Institute Discussion Papers 00-101/3, Tinbergen Institute.
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    Cited by:

    1. Toan, Trinh Dinh & Wong, Y.D., 2021. "Fuzzy logic-based methodology for quantification of traffic congestion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    2. Kseniia ILCHENKO & Anastasiia LISOGOR, 2016. "Sustainable Development Modeling For Municipalities," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 11(1), pages 77-85, February.
    3. Armenia ANDRONICEANU, 2016. "The Quality Of The Urban Transport In Bucharest And How To Improve It In Accordance With The Expectations Of The Citizens," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 11(1), pages 5-18, February.

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