Real-time Train Arrival Time Prediction at Multiple Stations and Arbitrary Times

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Sammanfattning

Real-time prediction of train arrivals is important for proactive traffic control and information provision in passenger rails. Despite many studies in predicting arrival times or delays at stations, they are essentially the next-step time series prediction problem which may limit their applications in practice. For example, passengers on the trains or waiting on platforms may have different destinations and need the predicted train arrival times for any downstream stations rather than only the next station. The paper aims to formulate a real-time train arrival times prediction problem at multiple stations and arbitrary times. We develop multi-output machine learning models and systematically evaluate their performance using train operation data in Sweden. The direct multi-output regression models with different regression functions are tested, including LightGBM, linear regression, random forest regression, and gradient boosting regression models. The hyperparameters are optimized using random grid search and five-fold cross-validation methods. The results show that the Direct Multi-Output LightGBM significantly outper-formed other models in terms of accuracy. The predictions at downstream stations improve as the train moves along given more real-time information is observed.

Originalspråkengelska
Titel på värdpublikation2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
FörlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Sidor793-798
Antal sidor6
ISBN (elektroniskt)9781665468800
ISBN (tryckt)9781665468817
DOI
StatusPublished - 2022
Evenemang25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022 - Macau, Kina
Varaktighet: 2022 okt. 82022 okt. 12

Publikationsserier

NamnIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volym2022-October

Konferens

Konferens25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Land/TerritoriumKina
OrtMacau
Period2022/10/082022/10/12

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© 2022 IEEE.

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