基于深度学习的网约车供需缺口短时预测研究
摘要
The results of supply-demand gap prediction for online car-hailing services in different areas can provide support for online car-hailing scheduling system, thereby improving efficiency and service levels. In order to realize the short-term forecast of supply-demand gap for online car-hailing services, this paper proposes a novel spatio-temporal deep learning model (S-TDL). The model is composed of three sub-models: spatiotemporal variable model, spatial attribute variable model and environment variable model. It can capture the impact of spatio-temporal correlation, regional difference and environmental change on supply-demand gap. Moreover, a feature selection method named feature clustering-maximum information coefficient two-stage feature selection is proposed to screen out the important features which are strongly correlated with the supply-demand gap, improve training efficiency. The experimental results show that the S-TDL model after feature selection achieves the better performance than the existing methods.