A Method for Private Car Transportation Dispatching Based on a Passenger Demand Model

Although the demand for taxis is increasing rapidly with the soaring population in big cities, the number of taxis grows relatively slowly during these years. In this context, private transportation such as Uber is emerging as a flexible business model, supplementary to the regular form of taxis. At present, much work mainly focuses on the reduction or minimization of taxi cruising miles. However, these taxi-based approaches have some limitations in the case of private car transportation because they do not fully utilize the order information available from the new type of business model. In this paper we present a dispatching method that reduces further the cruising mileage of private car transportation, based on a passenger demand model. In particular, we partition an urban area into many separate regions by using a spatial clustering algorithm and divide a day into several time slots according to the statistics of historical orders. Locally Weighted Linear Regression is adopted to depict the passenger demand model for a given region over a time slot. Finally, a dispatching process is formalized as a weighted bipartite graph matching problem and we then leverage our dispatching approach to schedule private vehicles. We assess our approach through several experiments using real datasets derived from a private car hiring company in China. The experimental results show that up to 74% accuracy could be achieved on passenger demand inference. Additionally, the conducted simulation tests demonstrate a 22.5% reduction of cruising mileage.