Forecasting the Gap Between Demand and Supply of E-hailing Vehicle in Large Scale of Network Based on Two-stage Model

Recently, taxi play an increasingly important role in transit mode due to its accessibility and convenient. However, vacant e-hailing vehicles occupy the road capacity, thus, aggravated traffic congestions. With the availability of real-time data, one way to deal with these concerns is to improve the forecast accuracy of demand and supply thus help improving the dispatching efficiency. This paper proposes a two-stage forecast model based on big data to real-time predict the gap between demand and supply in large scale of network. The framework includes four steps, GPS data dimensionality reduction based on Principle Component Analysis, pattern analysis, the proposed methodology of two-stage forecast model and model verification. The methodology combines both non-linear Support Vector Machine and Backpropagation neural network. In the cast study of Beijing city, the model is testified and the results show that two-stage forecast model fastens responsive performance and improves the prediction accuracy. The proposed framework not only reveals the mobility pattern, it also improves the prediction accuracy for the gap between demand and supply of taxis thus helps to improve the taxi utilizations.