A taxi demand prediction model based on spectral domain graph convolution

Abstract

Taxis are part of the important components of urban transportation, and the demand for taxis is essential to build an efficient transportation system in smart cities. Accurate taxi demand forecasts can guide vehicle scheduling, improve vehicle utilization, ease traffic congestion, and improve passenger ride experience. Aiming at the complex spatiotemporal and spatial dependence of taxi demand, how to accurately predict taxi demand is a current research hotspot. This paper proposes a new taxi usage demand forecasting model, namely CGRU model, which uses spectral domain graph convolutional networks(ChebNet) to encode the topology of taxi usage requirements to obtain topological correlations, while modelling spatial correlations with reference to usage requirements of other regions of functional similarity in that region, using gated recurrent units(GRU) model the temporal correlation, combine the spatial correlation with the temporal correlation, and complete the analysis of the temporal and spatial correlation of the taxi demand. The proposed model is assessed on the NYCTAXI_DYNA open-source dataset, and the results show that the CGRU model outperforms the baseline model on evaluation metrics such as MAE and RMSE.