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Are we there yet?: Prediciting bus arrival times with an artificial neural network
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Public transport authority UL (Upplands Lokaltrafik) aims to reduce emissions, air pollution, and traffic congestion by providing bus journeys as an alternative to using a car. In order to incentivise bus travel, accurate predictions are critical to potential passengers. Accurate arrival time predictions enable the passengers to spend less time waiting for the bus and revise their plan for connections when their bus runs late. According to literature, Artificial Neural Networks (ANN) has the ability to capture nonlinear relationships between time of day and position of the bus and its arrival time at upcoming bus stops. Using arrival times of buses on one line from July 2018 to February 2019, a data-set for supervised learning was curated and used to train an ANN. The ANN was implemented on data from the city buses and compared to one of the models currently in use. Analysis showed that the ANN was better able to handle the fluctuations in travel time during the day, only being outperformed at night. Before the ANN can be implemented, real time data processing must be added. To cement its practicality, whether its robustness can be improved upon should be explored as the current model is highly dependent on static bus routes.

Place, publisher, year, edition, pages
2019. , p. 32
Series
TVE-F ; 19009
Keywords [en]
artificial neural network, prediction, regression, tensorflow, UL, Upplands Lokaltrafik
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-386548OAI: oai:DiVA.org:uu-386548DiVA, id: diva2:1327972
External cooperation
Region Uppsala - Upplands lokaltrafik
Subject / course
Computer Systems Sciences
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2019-06-25 Created: 2019-06-20 Last updated: 2019-06-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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More languages
Output format
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