Passenger Demand Forecast Model Based on Deformable Convolution Spatial-temporal Network

With the increasing popularity of taxi services such as Didi and Uber, passengers' demand has gradually become an important part of smart cities and smart transportation. The accurate prediction model can not only meet the travel needs of users, but also reduce the no-load rate of road vehicles, which can effectively avoid waste of resources and relieve traffic pressure. Vehicle service providers can collect a large amount of GPS data and passenger demand data, but how to use this big data to forecast demand is a key and practical problem. This study proposes a deformable convolution spatial-temporal network (DCSN) model that combines urban POI to predict regional ride demand. Specifically, the model proposed in this study consists of two parts: the deformable convolution spatial-temporal model and the POI requirement correlation model. The former models the correlation between future demand and time and space through DCN and LSTM, while the latter captures the similar relationship among regions through the regional POI differentiation index and the demand differentiation index. Finally, the two models are integrated by a fully connected network. Then the prediction results are obtained. In this study, the large real ride demand data of Didi trips is used for experiments. The final experimental results show that the proposed method outperforms the existing forecasting methods in terms of prediction accuracy. © Copyright 2021, Institute of Software, the Chinese Academy of Sciences. All rights reserved.