Estimation of Bus Passengers' Residential Locations Based on Morning Rush Hour Travel Data and POI Information

To address the issues of inefficiency and high costs in obtaining data on the residential distribution of public transport passengers at present, this paper proposes an approach of "estimating the residential distribution of public transport passengers based on characteristics such as housing prices of residential Point of Interest (POI) and the convenience of public transport and its stops". First, from two aspects—public transport travel and the selection of public transport stops—eight influencing factors for the selection of public transport stops during travel are identified. Based on these factors, a regression model for the number of public transport passengers from residential POI to their corresponding stops is constructed, through which the number of passengers traveling from each residential POI to all accessible public transport stops is obtained. This number is then used as a weight to allocate the actual passenger flow of each public transport stop to respective residential POI, thereby realizing the estimation of the residential distribution of public transport passengers. Furthermore, this approach enables the estimation of the proportion of trips made from residential areas to specific public transport stops and the overall proportion of public transport trips among all travel modes from residential areas. The proposed estimation method is verified and evaluated using Shenzhen as a case study. The results show that the Mean Absolute Percentage Error (MAPE) of the proposed model is 72.024%, which outperforms the XGBoost model that uses the same set of characteristics.