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Travel-Time Prediction Methods: A Review

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Smart Computing and Communication (SmartCom 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11344))

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Abstract

Near-future Travel-time information is helpful to implement Intelligent Transportation Systems (ITS). Travel-time prediction refers to predicting future travel-time. Researchers have developed various methods to predict travel-time in the past decades. This paper conducts a review focusing on literatures, including techniques proposed recently. These methods are categorized as model-based and data-driven methods. We elaborate two common model-based methods, namely queuing theory and cell transmission model. Data-driven methods are categorized as parametric models (linear regression, autoregressive integrated moving average model and Kalman filter) and non-parametric models (neural network, support vector regression, nearest neighbors and ensemble learning). These methods are compared from data, prediction range and accuracy. In addition, we discuss several solutions to overcome shortcomings of existing methods, and highlight significant future research challenges.

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Acknowledgement

This work is supported in part by National Key R&D Program of China No. 2017YFB1200700 and National Natural Science Foundation of China No. 61701007.

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Authors and Affiliations

  1. School of Software and Microelectronics, Peking University, Beijing, China

    Mengting Bai, Yangxin Lin & Ping Wang

  2. National Engineering Research Center for Software Engineering, Peking University, Beijing, China

    Meng Ma & Ping Wang

  3. Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, China

    Ping Wang

Authors
  1. Mengting Bai
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  2. Yangxin Lin
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  3. Meng Ma
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  4. Ping Wang
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Corresponding authors

Correspondence to Meng Ma or Ping Wang .

Editor information

Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

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Bai, M., Lin, Y., Ma, M., Wang, P. (2018). Travel-Time Prediction Methods: A Review. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2018. Lecture Notes in Computer Science(), vol 11344. Springer, Cham. https://doi.org/10.1007/978-3-030-05755-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-05755-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05754-1

  • Online ISBN: 978-3-030-05755-8

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