Alighting and boarding time prediction in different types of waiting areas at subway stations

Abstract

The alighting and boarding behavior of passengers at the subway station can directly affect the dwell time of trains, and then affect the service level of stations. The HPO-BP prediction model for the alighting and boarding time in scenarios with different types of waiting areas is proposed in this paper. The number of passengers, gender, whether to carry luggage or children, and the degree of passenger civilization are selected as the main influence factors after analyzing the data of field investigations, which are defined as the input variables of the prediction model. The alighting and boarding time is defined as the output variable. The HPO-BP prediction model is trained, tested and validated by using the data collected in the field investigations. The comparison results indicate that the prediction accuracies of the proposed model in a single waiting area and double waiting areas are 35.8% and 62.8% higher than that of the BP neural network, and are also significantly improved compared with other advanced algorithms. It can be concluded that the gender of passengers is the most important factor affecting passengers’ alighting and boarding time after the interpretation and analysis of the SHAP model. Moreover, the degree of passenger civilization also has a certain impact on the alighting and boarding efficiency.