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Based on spatial data dynamic analysis technology, the characteristics of urban rail transit passenger flow are analyzed. This study analyzes the passenger flow characteristics of urban rail transit from the perspective of operation and planning by using the dynamic analysis technology of spatial data, combined with social and economic development. Based on the analysis of research data from the study of passenger traffic and passenger traffic data measurement, the characteristics of passenger traffic from urban railways are derived from the time distribution, spatial distribution, and transfer of passengers waiting on the platform. The final length of line 3 is 39.7 m, the passenger volume is 102, with 11 people/day, the passenger flow density is 1.8/2.3, and the high section in peak hours is 2.4/3.2. By considering the various influencing factors, efforts should be made to develop urban rail transit projects to promote the growth of social and economic benefits. [ABSTRACT FROM AUTHOR]

Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods. [ABSTRACT FROM AUTHOR]