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.

本研究利用多种模型，探索基于用户偏好和行程特征（如成本、时间、出行目的和距离）的最佳交通方式，在实际生活中得到的数据支持下，研究发现使用梯度加速树模型结合少数类过采样技术（SMOTE）的表现最为突出，而在对数模型中可以看出：（1）交通成本增加会降低各种交通方式的效用；（2）对于地铁或多模式选项（包含地铁）人们对于出行距离有较高的容忍度，说明地铁线路可能是大城市最佳候选方案。