Research on origin-destination travel demand prediction method of inter-regional online taxi based on SpatialOD-BiConvLSTM

Abstract

In order to avoid empty or crowded trips of online taxis and shorten the distance between empty taxis and potential passengers, after adopting the Origin-Destination (OD) big data of taxis and considering geographic location information, cross-regional driving demand, as well as passenger origin demand and destination demand, a method combining SpatialOD and Bidirectional ConvLSTM (BiConvLSTM) neural network model is proposed to predict online taxi travel demand. The network model in the method takes into account the historical and future states of the data, and can extract the time and space characteristics of the data. A custom structural similarity loss function is used in the model training to measure the difference between the data. Through experiments on a large public taxi travel data set, the results show that the SMAPE error of this method is 23.83%, which is better than the existing OD demand forecasting methods and has strong practicability.