Travel mode choice prediction based on personalized recommendation model

The accurate prediction of individual travel mode in the city is necessary for the development of urban traffic intelligence. Through deep analysis on distribution of travel modes, policy makers can make proper policies to improve traffic conditions. With the development of sensing techniques and telecommunication technologies, it is easy to collect travel data of individuals and use some popular models to detect travel modes. However, existing methods are limited to aggregation analysis and these models suggest that travelers' preference mainly depend on the characteristics of travelers, little is known considering to extract individual travel preference from individual historical trips. Due to collect the user information is expensive and time-consuming, one of the difficult tasks facing the prediction of travel mode by traditional methods is the lack of user's profile. What is more, data of different travel modes are always imbalanced. Accordingly, the paper proposes a model Balance Multi Travel Mode Deep Learning Prediction (BMTM-DLP) for individual travel mode prediction inspired by the idea of recommender system. For the first time, the theory of recommender system is introduced into the field of individual travel mode choice prediction and we take advantage of the model to extract individual travel preference for travel mode prediction. Further, the focal loss function module is introduced into the model to reduce the impact of unbalanced categories. Experiments show that the model performs well on both datasets LPMC and XSCR, achieving 92.2% and 82.8% prediction accuracy in respective. It showed that the proposed model performed well in individual travel mode prediction.