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Title: Calculation Method for Load Capacity of Urban Rail Transit Station Considering Cascading Failure
Accession Number: 01659966
Record Type: Component
Abstract: The load capacity of urban rail transit station is of great significance to provide reference in station design and operation management. However, it is difficult to carry out quantitative calculation quickly and accurately due to the complex interaction among passenger behaviors, facility layout and the limit capacity of single facility. In this paper, the association network of facilities is set up based on the analysis of passenger service chain in station. Then the concept of cascading failure is introduced to the dynamic calculation model of load capacity, which is established on the user-equilibrium allocation model. The solution algorithm is optimized with node attack strategy of complex network to effectively reduce the computational complexity. Finally, a case study of Lujiabang Road Station in Shanghai is carried out and compared with the simulation results of StaPass, verifying the feasibility of this approach. The proposed method can not only search for the bottleneck of capacity, but also help to trace the loading variation of facilities network in different scenarios, providing theoretical supports on passenger flow organization.
Supplemental Notes: This paper was sponsored by TRB committee AP065 Standing Committee on Rail Transit Systems.
Report/Paper Numbers: 18-04600
Language: English
Authors: Xi, MengruHuang, JiajunXu, RuihuaZou, XiaoleiPagination: 17p
Publication Date: 2018
Conference:
Transportation Research Board 97th Annual Meeting
Location:
Washington DC, United States Media Type: Digital/other
Features: Figures; References; Tables
TRT Terms: Uncontrolled Terms: Geographic Terms: Subject Areas: Operations and Traffic Management; Planning and Forecasting; Public Transportation; Terminals and Facilities
Source Data: Transportation Research Board Annual Meeting 2018 Paper #18-04600
Files: TRIS, TRB, ATRI
Created Date: Jan 8 2018 11:07AM
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