Analyzing travel behavior in Hanoi using Support Vector Machine

Thi My Thanh Truong · Hai‐Bang Ly · Dongwoo Lee · Binh Thai Pham · Sybil Derrible

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Transportation Planning and Optimization Urban Transport and Accessibility Transportation and Mobility Innovations

Abstract

This study investigates travel decisions (i.e. travel mode and destination) in Hanoi (Vietnam) using Support Vector Machine (SVM). First, a travel interview survey was conducted and 311 responses were collected across Hanoi. Second, a SVM model was trained to predict travel decisions and compared with a multinomial logit (MNL) model (as a benchmark). Third, the most important variables that affect travel decisions were ranked and discussed. The results show that SVM achieves an accuracy of 76.1% (compared to 72.9% for MNL). Moreover, proposed parking charges, household income, trip mode, and trip cost are found to be the most important variables. In contrast, trip purpose, gender, and occupation are found to negatively affect the model. Overall, low travel cost and low motorcycle parking charges, especially for commuters and shoppers, make people less willing to switch to more sustainable modes such as public and active transport.

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References

2003Discrete Choice Methods with Simulation 1984Specification Tests for the Multinomial Logit Model 1977The structure of random utility models 2018Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis 1950Rational Behavior, Uncertain Prospects, and Measurable Utility 2017A comparative study of machine learning classifiers for modeling travel mode choice 2017Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance 2004The Role of Land Use in Travel Mode Choice: Evidence from Boston and Hong Kong

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