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

49 Downloads (Pure)

Abstract

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
PublisherSpringer
Chapter22
Pages213-222
Number of pages10
ISBN (Electronic)978-3-030-91608-4
ISBN (Print)978-3-030-91607-7
DOIs
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
https://ideal-conf.com/ideal2021

Publication series

Name Lecture Notes in Computer Science (LNCS)
PublisherSpringer
Volume13113
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameInformation Systems and Applications, incl. Internet/Web, and HCI (LNISA)
Volume13113

Conference

Conference22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021
Abbreviated titleIDEAL 2021
Country/TerritoryUnited Kingdom
CityManchester
Period25/11/2127/11/21
Internet address

Keywords

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

Fingerprint

Dive into the research topics of 'A Deep Learning-Based Approach for Train Arrival Time Prediction'. Together they form a unique fingerprint.

Cite this