Analyzing travel behavior in Hanoi using Support Vector Machine

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.