Today, macroscopic passenger flow forecasting still uses the four-step model, which was primarily developed for the prediction of traffic on a regional scale and the evaluation of large-scale infrastructure projects. The predicted result for a metro station is extracted from the predicted result of the metro network, as the total passenger flow volume of the metro network must be controlled to reduce forecasting errors.
Peakhour enabler code#
In China, the Code for Design of Metro (the most recent edition of which is from 2003) states that the capacity of a metro station must be determined by the extra peak hour passenger flow, which is the predicted peak hour passenger flow multiplied by the extra peak hour factor, which is between 1.1 and 1.4. Estimating the maximum passenger demand for a platform at a given time is one of the most important steps for station design.
The mixed land use ratio must be considered in urban land use planning, because although non-commuting land can mitigate the traffic pressure of a city’s peak hour, it may cause the deviation of the station’s peak hours from that of the city.Īccording to the Transit Capacity and Quality of Service Manual, the calculation procedure for a desired station’s platform size includes choosing the corresponding average number of passengers, adjusting for passenger characteristics as appropriate, estimating the maximum passenger demand for the platform at a given time, and calculating the required waiting space by multiplying the average space per person by the maximum passenger demand. There are two ridership options when designing stations, namely the extra peak hour ridership during a city’s peak hour and that during a station’s peak hour, and the larger of the two is used to design metro stations. Additionally, the station’s peak hour is more likely to deviate from the city’s peak hour for suburban stations. The results demonstrate that when the land around a metro station is mainly land for work, primary and middle schools, and residences, its station’s peak hour is consistent with the city’s peak hour. Data from 88 metro stations in Xi’an, China, are used to analyze the peak deviation coefficient based on the geographically weighted regression model.
To investigate this inconsistency, this study introduces the peak deviation coefficient to describe this phenomenon. The ridership of a metro station during a city’s peak hour is not always the same as that during the station’s own peak hour.
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