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A data analytics framework for reliable bus arrival time prediction using artificial neural networks

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Abstract

The analysis of extensive vehicle location data in an urban bus system requires an efficient data-driven method so that its output can be used to improve both the information and operational reliabilities of the bus transit services. This research aims to design and implement an integrated model for forecasting bus arrival times using artificial intelligence techniques. The novelty of the research lies in develo** an integrated predictive model based on a neural network computing framework to improve the results of fleet arrival forecasting models based on the estimation of travel time reliability. To validate the prediction model, extensive experiments are conducted to compare the performance of the proposed algorithms with existing methods. The developed data-driven model based on a neural network shows high accuracy in predicting bus arrival time as compared with the SVM and ARIMA. The results of the research show that improvement in reliability significantly reduces the prediction error and thus increases the perceived quality of the services.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Industrial Engineering Department, Sharif University of Technology, Tehran, Iran

    Erfan Hassannayebi

  2. Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Ali Farjad

  3. Department of Civil Engineering, Sharif University of Technology, Tehran, Iran

    Alireza Azadnia

  4. Industrial Management-Operations Research, Department of Industrial Management, Management and Accounting Faculty, Allameh Tabataba’i University, Tehran, Iran

    Mehrdad Javidi

  5. VIT University, Vellore, India

    Raghavendra Chunduri

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  1. Erfan Hassannayebi
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  2. Ali Farjad
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  3. Alireza Azadnia
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Hassannayebi, E., Farjad, A., Azadnia, A. et al. A data analytics framework for reliable bus arrival time prediction using artificial neural networks. Int J Data Sci Anal (2023). https://doi.org/10.1007/s41060-023-00391-y

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