Demand forecasting of online car‐hailing with combining lstm + attention approaches

Abstract

The accurate prediction of online car‐hailing demand plays an increasingly important role in real‐time scheduling and dynamic pricing. Most studies have found that the demand of online car‐hailing is highly correlated with both temporal and spatial distributions of journeys. However, the importance of temporal and spatial sequences is not distinguished in the context of seeking to improve prediction, when in actual fact different time series and space sequences have different impacts on the distribution of demand and supply for online car‐hailing. In order to accurately pre-dict the short‐term demand of online car‐hailing in different regions of a city, a combined attention-based LSTM (LSTM + Attention) model for forecasting was constructed by extracting temporal fea-tures, spatial features, and weather features. Significantly, an attention mechanism is used to dis-tinguish the time series and space sequences of order data. The order data in Haikou city was col-lected as the training and testing datasets. Compared with other forecasting models (GBDT, BPNN, RNN, and single LSTM), the results show that the short‐term demand forecasting model LSTM + Attention outperforms other models. The results verify that the proposed model can support ad-vanced scheduling and dynamic pricing for online car‐hailing.