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It is the basis for increasing attraction of rail transit and maximizing the attraction of public transport to focus on summarizing the pattern and experience of growing rail passenger flow, and studying the characteristics of trip assignment. This paper studied specifically on the passenger assembling of the urban rail transit platform, and designed the calculation methods about assembling passengers on platform through the analysis of passenger flow characteristics. Based on the specific situation of the subway of Beijing south station, this paper analyzed the assembling of platform and obtained assembling data under different conditions. The result shows that, in order to smooth the assembling of platform, we could reduce the train arrival time-gap, stagger the up and down train’s arrival time and increase their arrival time-gap in a proper way.

The paper proposes a space–time autoregressive integrated moving average (STARIMA) model to predict the traffic volume in urban areas. The methodological framework incorporates the historical traffic data and the spatial features of a road network. Moreover, the spatial characteristics in a way that reflects not only the distance but also the average travel speed on the links. In order to response the time-varying speed, six traffic modes are classified by level of service (LOS) which is updated in 5 minute interval. In the end, with the real traffic data in Beijing for experiments, the model achieves a very good accuracy on the 5 minute interval forecasting, it also provides a sufficient accuracy of 30 minute interval forecasting compared with ARIMA model.