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Use of acceleration data for transportation mode prediction

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  • Muhammad Shafique
  • Eiji Hato

Abstract

Most smartphones today are equipped with an accelerometer, in addition to other sensors. Any data recorded by the accelerometer can be successfully utilised to determine the mode of transportation in use, which will provide an alternative to conventional household travel surveys and make it possible to implement customer-oriented advertising programmes. In this study, a comparison is made between changes in pre-processing, selection methods for generating training data, and classifiers, using the accelerometer data collected from three cities in Japan. The classifiers used were support vector machines (SVM), adaptive boosting (AdaBoost), decision tree and random forests. The results of this exercise suggest that using a 125-point moving average during pre-processing and selecting training data proportionally for all modes will maximise prediction accuracy. Moreover, random forests outperformed all other classifiers by yielding an overall prediction accuracy of 99.8 % for all three cities. Copyright The Author(s) 2015

Suggested Citation

  • Muhammad Shafique & Eiji Hato, 2015. "Use of acceleration data for transportation mode prediction," Transportation, Springer, vol. 42(1), pages 163-188, January.
    • Handle: RePEc:kap:transp:v:42:y:2015:i:1:p:163-188
    DOI: 10.1007/s11116-014-9541-6
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    References listed on IDEAS

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    1. Eui-Hwan Chung & Amer Shalaby, 2005. "A Trip Reconstruction Tool for GPS-based Personal Travel Surveys," Transportation Planning and Technology, Taylor & Francis Journals, vol. 28(5), pages 381-401, August.
    2. Chen, Cynthia & Gong, Hongmian & Lawson, Catherine & Bialostozky, Evan, 2010. "Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 830-840, December.
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    Cited by:

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    2. Zhenbo Lu & Zhen Long & Jingxin Xia & Chengchuan An, 2019. "A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data," Sustainability, MDPI, vol. 11(21), pages 1-21, October.
    3. Weijia (Vivian) Li & Kara M. Kockelman, 2022. "How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE," Growth and Change, Wiley Blackwell, vol. 53(1), pages 342-376, March.
    4. Yanjun Qin & Haiyong Luo & Fang Zhao & Zhongliang Zhao & Mengling Jiang, 2018. "A traffic pattern detection algorithm based on multimodal sensing," International Journal of Distributed Sensor Networks, , vol. 14(10), pages 15501477188, October.
    5. Thomas Feilhauer & Florian Braun & Katja Faller & David Hutter & Daniel Mathis & Johannes Neubauer & Jasmin Pogatschneg & Michelle Weber, 2021. "Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis," Sustainability, MDPI, vol. 13(4), pages 1-23, February.
    6. Montserrat Guillen & Ana M. Pérez-Marín & Mercedes Ayuso & Jens Perch Nielsen, 2018. "“Exposure to risk increases the excess of zero accident claims frequency in automobile insurance”," IREA Working Papers 201810, University of Barcelona, Research Institute of Applied Economics, revised May 2018.
    7. Montserrat Guillen & Jens Perch Nielsen & Mercedes Ayuso & Ana M. Pérez‐Marín, 2019. "The Use of Telematics Devices to Improve Automobile Insurance Rates," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 662-672, March.

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