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Traffic volume prediction using intuitionistic fuzzy Grey-Markov model

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Abstract

Traffic congestion is a major problem in the last few decades. Though the Government is taking various measures like installing more traffic signals, constructing fly-overs, one-ways, etc., the problem could not be addressed effectively on the heavily congested areas. The increasing number of vehicles, the dynamic population of traffic, the narrowness of roads and the absence of alternative roads or other routes are the major obstacles to reduce traffic congestion. Researchers of various streams have been working on predicting traffic volume to solve this problem. In this paper, a new method named Intuitionistic Fuzzy Grey-Markov Model(IFGMM) is introduced, which is an extension of Grey-Markov to Intuitionistic fuzzy set. The model is distinctive when compared with other models as it has a high level of accuracy performance. The forecasting result of this model is almost in proximity with the actual result of the traffic volume. In addition, the relative errors shown in this method are lesser than that of other models, due to its prediction accuracy is higher. Since the data collected in this method are taken from a heavily congested road from a metropolitan city, the proposed approach will be suitable for any congested area of the world.

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Authors and Affiliations

  1. Department of Mathematics, Sri Venkateswaraa College of Technology, Sriperumbudur, Kanchipuram, India

    Kuppuswami Govindan

  2. Department of Mathematics, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Sujatha Ramalingam

  3. Laboratory of Information Processing, Faculty of Science Ben MSik, University Hassan II, Casablanca, Morocco

    Said Broumi

Authors
  1. Kuppuswami Govindan
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  2. Sujatha Ramalingam
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  3. Said Broumi
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Correspondence to Sujatha Ramalingam.

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Govindan, K., Ramalingam, S. & Broumi, S. Traffic volume prediction using intuitionistic fuzzy Grey-Markov model. Neural Comput & Applic 33, 12905–12920 (2021). https://doi.org/10.1007/s00521-021-05940-9

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  • DOI: https://doi.org/10.1007/s00521-021-05940-9

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