How Do Metro Station Crowd Flows Influence the Taxi Demand Based on Deep Spatial-Temporal Network?

Forecasting taxi demand is of great significance to the intelligent transportation systems in a smart city. Traditional demand prediction methods mostly considered about inter-regional traffic, events, activities, and weather, while they overlooked the influence of other travel modes, such as metro. In this paper, we propose a Deep Taxi-Metro Spatial-Temporal Network framework, namely TMST-Net, to model the spatiotemporal relationships between...