Station-level passenger flow demand forecast with a combination of ridership and land use data
YI Wang, LV Yitong, WANG Zhuoqun, MO Yihong, LUO Qin
With the continuous development of urbanization, some big cities continue to expand and the travel demand continues to grow. Among them, rail transit plays an increasingly prominent role in urban passenger transport system. As the fundamental information during the process of planning, construction and operation of rail transit, passenger flow is the key to the improvement and development of rail transit. Based on the inbound and outbound data of passenger flow of Shenzhen metro network, this paper applies the clustering algorithm to classify the stations, and uses decision tree model analyze the significant factors to the passenger flow. Finally, a multiple regression model is developed to forecast the passenger flow of each category of stations. After an example verification, the analysis method proposed in this paper can better reflect the relationship between land use and passenger flow demand and the results can provide theoretical guidance and useful reference for the reasonable determination of rail transit station scale, as well as the development of surrounding land use and the adjustment of operation organization plan.