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Urban Bus Arrival Time Prediction Using Linear Regression and Kalman Filter—A Comparison

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Soft Computing and Signal Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 898))

Abstract

The study aims to use two statistical processes to predict the arrival time of a bus in the highly dynamic traffic conditions of Mumbai. The paper provides a comparison of regression and Kalman filter as an attempt to model the travel time for a bus. GPS data collected from the bus during field study was used for training and validation of both the models. The Kalman filter model is leveraged to provide real-time information and is used to exploit the correlation between the test bus and previous buses plying along the same route and is shown to perform better among the two for forecasting travel time.

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Author information

Authors and Affiliations

  1. Electronics and Telecommunication Department, Dwarkadas J. Sanghvi College of Engineering, Mumbai, 400056, India

    Neeraj Ramkumar & Archana Chaudhari

Authors
  1. Neeraj Ramkumar
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  2. Archana Chaudhari
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Corresponding author

Correspondence to Neeraj Ramkumar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ, USA

    Jiacun Wang

  2. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangaluru, Karnataka, India

    G. Ram Mohana Reddy

  3. Department of Computer Science and Engineering, JNTUH College of Engineering Hyderabad, Hyderabad, Telangana, India

    V. Kamakshi Prasad

  4. Department of Electronics and Communication Engineering, Malla Reddy College of Engineering & Technology, Secunderabad, Telangana, India

    V. Sivakumar Reddy

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Ramkumar, N., Chaudhari, A. (2019). Urban Bus Arrival Time Prediction Using Linear Regression and Kalman Filter—A Comparison. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 898. Springer, Singapore. https://doi.org/10.1007/978-981-13-3393-4_29

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