Research on a Prediction Method for Passenger Waiting-Area Demand in High-Speed Railway Stations

Abstract

The rapid development of intelligent transportation systems and high-speed railways has shortened the waiting time of passengers and the demand for waiting areas. Large-scale stations not only increase the difficulty for passengers when traveling but also waste a great deal of land resources and construction funds. Therefore, this research analyzes passenger waiting area demand according to the characteristics of urban development, passenger travel characteristics and station departure passenger flow. This paper establishes a prediction model for the number of passengers spending time in the waiting room, taking into account passenger traffic and train departure timetables. We used Beijing South Railway Station, Xi’an North Railway Station, Hefei South Railway Station and Zhoukou East Railway Station as examples to predict the numbers of passengers spending time in waiting rooms of different types and scales of station. Research results show that shortening the length of passengers’ early arrival times can effectively reduce the number of passengers gathered in medium-sized stations, which are located in new first-tier cities. Under the influence of the urban traffic environment and passenger flow, the uncertainties regarding travel time and passenger flow in first-tier cities are lower than those in new first-tier cities and higher than those in third-tier and fourth-tier cities. Therefore, the waiting area demand of passengers departing from medium-sized stations in new first-tier cities is lower than that of passengers from large stations in first-tier cities, and higher than that of passengers from small stations in third-tier and fourth-tier cities.