Coupling Makes Better: An Intertwined Neural Network for Taxi and Ridesourcing Demand Co-Prediction

While a variety of innovative travel modes, such as taxi service and ridesourcing service, have been launched to improve the transportation efficiency, people still encounter travel problems in real life. The major cause is the imbalance between transportation supply and demand. To strike a balance, it is well-recognized that an accurate and timely passenger demand prediction model is the foundation to enable high-level human intelligence (i.e., taxi drivers) or machine intelligence (i.e., ride-hailing platforms) to allocate resources in advance. Although quite a lot of deep models have been designed to model the complicated spatial and temporal dependencies in a data-driven way, they focus on the demand prediction of a single mode and ignore the fact that passengers may shift between different modes, especially between taxis and ridesourcing cars. In this paper, we target a co-prediction problem that considers the prediction of taxi and ridesourcing as two coupled and associated tasks, and propose a novel Temporal and Spatial Intertwined Network (TSIN) that consists of two twin components and an intertwined component. Each twin in the TSIN model is able to extract spatial and temporal dependencies from its corresponding travel mode separately (i.e., intra-mode features), and the in-between intertwined component is designed to bridge the twins and allow them to exchange information (i.e., inter-mode features), thus enabling better prediction. We first evaluate our model on four real-world datasets. Results demonstrate the outstanding performance of our model and the necessity to take into account the influence between modes. Based on an additional demand data from bike in NYC, we then discuss the generalizability in coupling more transportation modes. Further results demonstrate that our proposed intertwined neural network is highly flexible and extendable, and can yield better prediction performance.