Using spatio‐temporal deep learning for forecasting demand and supply‐demand gap in ride‐hailing system with anonymised spatial adjacency information

Abstract

To reduce passenger waiting time and driver search friction, ride-hailing companies need to accurately forecast spatio-temporal demand and supply-demand gap. However, due to spatio-temporal dependencies pertaining to demand and supply-demand gap in a ridehailing system, making accurate forecasts for both demand and supply-demand gap is a difficult task. Furthermore, due to confidentiality and privacy issues, ride-hailing data are sometimes released to the researchers by removing spatial adjacency information of the zones, which hinders the detection of spatio-temporal dependencies. To that end, a novel spatio-temporal deep learning architecture is proposed in this paper for forecasting demand and supply-demand gap in a ride-hailing system with anonymised spatial adjacency information, which integrates feature importance layer with a spatio-temporal deep learning architecture containing 1D convolutional neural network (CNN) and zone-distributed independently recurrent neural network (IndRNN). The developed architecture is tested with real-world datasets of Didi Chuxing, which shows that the models based on the proposed architecture can outperform conventional time-series models (e.g. ARIMA) and machine learning models (e.g. gradient boosting machine, distributed random forest, generalized linear model, artificial neural network). Additionally, the feature importance layer provides an interpretation of the model by revealing the contribution of the input features utilized in prediction.

Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand

“…Jiang et al [24] used an SVM neural network to build a least squares support vector machine (LS-SVM) based on an online car-hailing travel demand prediction model, and through experiments they showed that the prediction results of the proposed prediction model method were better than those of other methods. Rahman et al [25] used a one-dimensional convolutional neural network (1D CNN) and zone-distributed independently recurrent neural network (IndRNN) to build a spatiotemporal deep learning structure, and tested it on real online car-hailing data; their network outperformed traditional time series models and machine learning models. In response to the analysis of the spatiotemporal correlation of the car-hailing data, Ke et al [26] combined the ConvLSTM model and the LSTM network to extract the spatiotemporal and temporal characteristics of the travel demand of the car-hailing, and finally verified its effectiveness through experiments.…”

Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model

“…Online ride-hailing has become popular across the country and is an important means of transportation for people because online ride-hailing is more convenient and flexible than bus and metro for passengers. The travel demand prediction of online ride-hailing has great significance for the development of online ride-hailing [1][2][3][4]. Deep learning is a new research direction in the field of machine learning, which is now widely applied in the field of transportation.…”