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A study of tour-based mode choice based on a Support Vector Machine classifier

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  • Miriam Pirra
  • Marco Diana

Abstract

A new approach in recognizing travel mode choice patterns is proposed, based on the Support Vector Machine classification technique. The tour-based travel demand dataset that is analysed is for New York State, derived from the 2009 U.S. National Household Travel Survey. The main features characterizing each tour are the means used, travel-related variables and socioeconomic aspects. Results obtained demonstrate the ability to predict to some extent, in real settings where car use dominates, which tours are likely to be made by public transport or non-motorized means. Moreover, the flexibility of the technique allows assessing the predictive power of each feature according to the combination of travel means used in different tours. Potential applications range from activity-based travel choice simulators to search engines supporting personalized travel planners – in general, whenever ‘best guesses’ on mode choice patterns have to be made quickly on large amounts of data prejudicing the possibility of setting up a statistical model.

Suggested Citation

  • Miriam Pirra & Marco Diana, 2019. "A study of tour-based mode choice based on a Support Vector Machine classifier," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(1), pages 23-36, January.
    • Handle: RePEc:taf:transp:v:42:y:2019:i:1:p:23-36
    DOI: 10.1080/03081060.2018.1541280
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    Cited by:

    1. Kailai Wang & Xize Wang, 2022. "Generational Differences in Automobility: Comparing America's Millennials and Gen Xers Using Gradient Boosting Decision Trees," Papers 2206.11056, arXiv.org.
    2. Niu, Zhipeng & Hu, Xiaowei & Fatmi, Mahmudur & Qi, Shouming & Wang, Siqing & Yang, Haihua & An, Shi, 2023. "Parking occupancy prediction under COVID-19 anti-pandemic policies: A model based on a policy-aware temporal convolutional network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    3. Wang, Kailai & Wang, Xize, 2021. "Generational Differences in Automobility: Comparing America's Millennials and Gen Xers Using Gradient Boosting Decision Trees," SocArXiv n3a9e, Center for Open Science.
    4. Hamed Naseri & Edward Owen Douglas Waygood & Bobin Wang & Zachary Patterson, 2022. "Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters," IJERPH, MDPI, vol. 19(24), pages 1-19, December.
    5. Kim, Eui-Jin & Kim, Youngseo & Jang, Sunghoon & Kim, Dong-Kyu, 2021. "Tourists’ preference on the combination of travel modes under Mobility-as-a-Service environment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 236-255.
    6. Tang, Xinyi & Wang, Dianhai & Sun, Yilin & Chen, Mengwei & Waygood, E. Owen D., 2020. "Choice behavior of tourism destination and travel mode: A case study of local residents in Hangzhou, China," Journal of Transport Geography, Elsevier, vol. 89(C).
    7. 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.

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