Short-time taxi demand prediction based on Transformer-LSTM in integrated transportation hub

Taxis, as an important part of the comprehensive transportation passenger flow connection, is one of the main tools for passengers in an integrated transportation hubs due to their better accessibility and convenience. Due to the impact of passenger travel choices and the frequent collection and distribution of large passenger flows in comprehensive transportation hubs, the demand for taxis fluctuates greatly. In addition, there are many other uncertainties in the process of taxi transfer because of the unreasonable scheduling of taxis, such as long transfer time, passengers stranded at the taxi stand, etc. Thus will cause delay and time waste of passengers. In order to improve the productivity of the hub operation in passenger flow distribution and serve the dynamic decision-making of taxi drivers, it is very necessary to predict the demand for taxis in the hub in time. Based on artificial intelligence deep learning method, this paper wants to build a short-term taxi demand forecasting model, which can assist taxi drivers to make decisions and choose a reasonable time to wait go to the taxi storage yard, so as to match the taxi demand of passengers with the supply of taxis perfectly and reduce the waiting time. By learning to fully mine the time-series characteristics of the historical data of taxis flow, the model integrates the Transformer and the LSTM neural network for the short-term prediction of demand of taxis every 15 minutes. Then taking the Shanghai Hongqiao transportation hub as an example, the experiment collected 3months of taxi cross-section traffic data to train the model. The results shows that the trained Transformer-LSTM model has a high accuracy in predicting short-term taxi demand. In order to verify the superiority of the model, the designed model is compared with other prediction models, such as CNN, LSTM. The experimental results show that the comprehensive performance of the Transformer-LSTM model has the highest accuracy. The model can be used to provide a forecast service for passengers' demand for taxis in transportation hubs, and provide a powerful reference for the optimization of taxi dispatching.