Users' Departure Time Prediction Based on Light Gradient Boosting Decision Tree.

With the development of urban transportation networks, the flow of people in cities generally shows the characteristics of concentration, periodicity and irregularity, and a typical example is rush hour. For most existing taxi-hailing apps, users frequently queue up for a relatively long time during rush hour and may even fail to get orders taken due to various factors. To solve this problem, we propose a users' departure time prediction model based on Light Gradient Boosting Machine (TP-LightGBM), which will remind users to book taxis before their journeys. As we know, TP-LightGBM may be the first model for departure time prediction. We uncover that travel behavior patterns vary under different external conditions through statistics and analysis of users' historical orders from multiple perspectives. Furthermore, we extract multiple features from these orders and select the favorable features by calculating their information gain as the input of TP-LightGBM to predict users' departure time. Therefore, our model can provide users with the recommendations of the best departure time if they need them. The final experimental results on our datasets indicate that TP-LightGBM has more excellent performance with great stability in predicting user departure time than other baseline models.