Multi-dimensional spatiotemporal demand forecasting and service vehicle dispatching for online car-hailing platforms

Forecasting transportation demands can aid online car-hailing platforms to dispatch their service vehicles in advance to areas with more potential orders. This results in a reduction in passengers’ waiting time and better utilisation of transportation resources. However, the complexity and dynamics of multi-dimensional influential factors make the forecasting and dispatching procedures challenging. This paper addresses these issues by using machine learning techniques and an effective probabilistic dispatching strategy. Multiple influential factors were identified in spatial, temporal, and meteorological dimensions, and effective machine learning algorithms were applied to predict the number of passenger orders. The fusion of the multi-dimensional features enables the proposed algorithms to better reveal the spatiotemporal characteristics and their correlations. A sensing-area-based strategy was introduced to dispatch available service vehicles to high demand-intensity regions efficiently with respect to the global demand-supply-balance and the individual probability of receiving orders. Finally, extensive experiments with large-scale real-world datasets were conducted to evaluate the performance of the machine learning algorithms and the effectiveness of the dispatching strategy. Overall, this paper extensively studies the forecasting of the spatiotemporal demand in multiple cities using point-of-interest data and the dispatching of available service vehicles based on such information for online car-hailing platforms.