Personalized Context-Aware Multi-Modal Transportation Recommendation
This work addresses personalized transportation recommendations for users, but it is incremental as it applies existing methods to a specific dataset without introducing new paradigms.
The study tackled the problem of recommending personalized multi-modal transportation by considering user preferences and trip characteristics, finding that a gradient boosting tree model with SMOTE performed best, and revealing that people are less sensitive to distance for metro trips, suggesting metro lines are suitable for large cities.
This study proposes to find the most appropriate transport modes with awareness of user preferences (e.g., costs, times) and trip characteristics (e.g., purpose, distance). The work was based on real-life trips obtained from a map application. Several methods including gradient boosting tree, learning to rank, multinomial logit model, automated machine learning, random forest, and shallow neural network have been tried. For some methods, feature selection and over-sampling techniques were also tried. The results show that the best performing method is a gradient boosting tree model with synthetic minority over-sampling technique (SMOTE). Also, results of the multinomial logit model show that (1) an increase in travel cost would decrease the utility of all the transportation modes; (2) people are less sensitive to the travel distance for the metro mode or a multi-modal option that containing metro, i.e., compared to other modes, people would be more willing to tolerate long-distance metro trips. This indicates that metro lines might be a good candidate for large cities.