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International Symposium on Neural Networks

ISNN 2019: Advances in Neural Networks – ISNN 2019 pp 11–18Cite as

Bus Arrival Time Prediction with LSTM Neural Network

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11554)

Abstract

Arrival time is a key aspect of passenger information systems. Provision of accurate bus arrival information is essential for delivering an attractive service and necessary to passengers for reducing their waiting time and bus stops and choosing alternative routes. Recently, the same information is used in smart-phone trip planners. In this paper, we explore an LSTM neural network model for bus arrival time prediction. We take into account heterogeneous information about the transport situation, directly or indirectly affecting the prediction travel time. We evaluate the proposed models with bus operation data from Samara, Russia. Evaluation results show that the proposed model outperforms some typical prediction algorithms.

Keywords

  • Arrival time prediction
  • Artificial neural network
  • Long short-term memory
  • Intelligent transportation systems

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Acknowledgments

The work was supported by the Ministry of Science and Higher Education of the Russian Federation (unique project identifier RFMEFI57518X0177).

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Authors and Affiliations

  1. Samara National Research University, 34, Moskovskoye shosse, Samara, 443086, Russia

    Anton Agafonov & Alexander Yumaganov

Authors
  1. Anton Agafonov
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  2. Alexander Yumaganov
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Corresponding author

Correspondence to Anton Agafonov .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. Sichuan University, Chengdu, China

    Huajin Tang

  3. Northeastern University, Shenyang, China

    Zhanshan Wang

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Agafonov, A., Yumaganov, A. (2019). Bus Arrival Time Prediction with LSTM Neural Network. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-22796-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22795-1

  • Online ISBN: 978-3-030-22796-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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