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Arrival Time Predictions for Buses using Recurrent Neural Networks
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Ankomsttidsprediktioner för bussar med rekurrenta neurala nätverk (Swedish)
Abstract [en]

In this thesis, two different types of bus passengers are identified. These two types, namely current passengers and passengers-to-be have different needs in terms of arrival time predictions. A set of machine learning models based on recurrent neural networks and long short-term memory units were developed to meet these needs. Furthermore, bus data from the public transport in Östergötland county, Sweden, were collected and used for training new machine learning models. These new models are compared with the current prediction system that is used today to provide passengers with arrival time information.

The models proposed in this thesis uses a sequence of time steps as input and the observed arrival time as output. Each input time step contains information about the current state such as the time of arrival, the departure time from thevery first stop and the current position in Cartesian coordinates. The targeted value for each input is the arrival time at the next time step. To predict the rest of the trip, the prediction for the next step is simply used as input in the next time step.

The result shows that the proposed models can improve the mean absolute error per stop between 7.2% to 40.9% compared to the system used today on all eight routes tested. Furthermore, the choice of loss function introduces models thatcan meet the identified passengers need by trading average prediction accuracy for a certainty that predictions do not overestimate or underestimate the target time in approximately 95% of the cases.

Place, publisher, year, edition, pages
2019. , p. 65
Keywords [en]
Machine Learning, Recurrent Neural Networks, RNN, Long short-term memory, LSTM, Regression, GTFS
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-165133ISRN: LIU-IDA/LITH-EX-A--19/098--SEOAI: oai:DiVA.org:liu-165133DiVA, id: diva2:1424184
External cooperation
Attentec AB; Östgötatrafiken AB
Subject / course
Computer science
Presentation
2019-12-10, Alan Turing, Linköping, 16:15 (English)
Supervisors
Examiners
Available from: 2020-05-15 Created: 2020-04-16 Last updated: 2020-05-15Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • Other locale
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Output format
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