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Investigating Feature Selection for Predicting the Number of Bus Passengers by Monitoring Wi-Fi Signal Activity from Mobile Devices

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Advanced Information Networking and Applications (AINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1151))

Abstract

The quality of service on public transportation system is motivated by passengers. It can be improved in several ways, such as bus arrival on time and seats available on the bus. Our research goal tries to provide congestion information to passengers in a bus transportation system by predicting the number of passengers. This can be done using several techniques. However, the most important factor is the prediction model, which consists of features and an algorithm. In this paper, we focus on feature selection using the Recursive Feature Elimination (RFE) method which can improve the prediction accuracy. We also show our experimental results by comparing the actual observed number of passengers at the bus stop to the predicted number of passengers.

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Authors and Affiliations

  1. Ritsumeikan University, Kusatsu, Shiga, Japan

    Thongtat Oransirikul & Hideyuki Takada

Authors
  1. Thongtat Oransirikul
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  2. Hideyuki Takada
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Corresponding author

Correspondence to Thongtat Oransirikul .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania “Luigi Vanvitelli”, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Oransirikul, T., Takada, H. (2020). Investigating Feature Selection for Predicting the Number of Bus Passengers by Monitoring Wi-Fi Signal Activity from Mobile Devices. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_31

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