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Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data

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  • Tao Feng
  • Harry J.P. Timmermans

Abstract

Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.

Suggested Citation

  • Tao Feng & Harry J.P. Timmermans, 2016. "Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(2), pages 180-194, March.
    • Handle: RePEc:taf:transp:v:39:y:2016:i:2:p:180-194
    DOI: 10.1080/03081060.2015.1127540
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    References listed on IDEAS

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    1. Du, Jianhe & Aultman-Hall, Lisa, 2007. "Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(3), pages 220-232, March.
    2. 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.
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    Cited by:

    1. Broach, Joseph & Dill, Jennifer & McNeil, Nathan Winslow, 2019. "Travel mode imputation using GPS and accelerometer data from a multi-day travel survey," Journal of Transport Geography, Elsevier, vol. 78(C), pages 194-204.
    2. 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.
    3. Seo, Toru & Kusakabe, Takahiko & Gotoh, Hiroto & Asakura, Yasuo, 2019. "Interactive online machine learning approach for activity-travel survey," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 362-373.
    4. 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.

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