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Neural Network for Public Transport Mode Inference on Mobile Devices

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Mobile Web and Intelligent Information Systems (MobiWIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13475))

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Abstract

Digital solutions are evolving and a general trend is to reduce the amount of user interactions needed while moving towards a more automatic and seamless paradigm. Public transportation solutions are no exception and in recent years the concept of Be-In/Be-Out solutions, where no explicit interactions are needed, have gained enhanced research focus. These solutions need to automatically detect travelers on board the different public transport vehicles so that the system is able to issue tickets in a seamless manner. Various on-board equipment has been suggested for this purpose, however, mounting additional equipment in vehicles both reduce scalability and increase cost. In this paper, we instead suggest an approach, completely void of any additional equipment, using only the smartphone of the given traveler. We propose a machine learning approach, where we take advantage of real sensor data, collected by actual travelers. We present a model which is ready to be deployed on-device through off-the-shelf technology, that determine the mode of transport with high accuracy for any given traveler using their smartphone.

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Authors and Affiliations

  1. School of Economics, Innovation, and Technology, Mobile Technology Lab, Kristiania University College, Oslo, Norway

    Anders Skretting & Tor-Morten Grønli

Authors
  1. Anders Skretting
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  2. Tor-Morten Grønli
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Correspondence to Anders Skretting .

Editor information

Editors and Affiliations

  1. University of Bradford, Bradford, UK

    Irfan Awan

  2. Oxford Brookes University, Oxford, UK

    Muhammad Younas

  3. Lodz University of Technology, Lodz, Poland

    Aneta Poniszewska-Marańda

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Skretting, A., Grønli, TM. (2022). Neural Network for Public Transport Mode Inference on Mobile Devices. In: Awan, I., Younas, M., Poniszewska-Marańda, A. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2022. Lecture Notes in Computer Science, vol 13475. Springer, Cham. https://doi.org/10.1007/978-3-031-14391-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-14391-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14390-8

  • Online ISBN: 978-3-031-14391-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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