A Deep Learning-Based Approach for Train Arrival Time Prediction

Bas Jacob Buijse, Vahideh Reshadat, Oscar Enzing

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
127 Downloads (Pure)


Level crossings have a function to let the traffic cross the railroad from one side to the other. In the Netherlands, 2300 level crossings are spread out over the country, playing a significant role in daily traffic. Currently, there isn’t an accurate estimation of the arrival time of trains at level crossings while it plays an important role in traffic flow management in intelligent transport systems. This paper presents a state-of-the-art deep learning model for predicting the arrival time of trains at level crossings using spatial and temporal aspects, external attributes, and multi-task learning. The spatial and temporal aspects incorporate geographical and historical travel data and the attributes provide specific information about a train route. Using multi-task learning all the information is combined and an arrival time prediction is made both for the entire route as for sub-parts of that route. Experimental results show that on average, the error is only 281 s with an average trip time of one hour. The model is able to accurately predict the arrival time at level crossings for various time steps in advance. The source code is available at https://github.com/basbuijse/train-arrival-time-estimator.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings
Subtitle of host publication22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings
EditorsDavid Camacho, Peter Tino, Richard Allmendinger, Hujun Yin, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
Place of PublicationCham
Number of pages10
ISBN (Electronic)978-3-030-91608-4
ISBN (Print)978-3-030-91607-7
Publication statusPublished - 2021
Event22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 - Manchester, United Kingdom
Duration: 25 Nov 202127 Nov 2021
Conference number: 22

Publication series

NameLecture Notes in Computer Science (LNCS)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameInformation Systems and Applications, incl. Internet/Web, and HCI (LNISA)


Conference22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021
Abbreviated titleIDEAL 2021
Country/TerritoryUnited Kingdom
Internet address


  • Deep learning
  • Multi-task learning
  • Spatial-temporal neural networks
  • Train arrival time prediction


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