HU Zhanghao, GUO Xudong. Study on influencing factors and improving strategies of urban rail transit passenger flow in ShenzhenJ. Urban Mass Transit, 2026, 29(5): 39-47. DOI: 10.16037/j.1007-869x.20240262
Citation: HU Zhanghao, GUO Xudong. Study on influencing factors and improving strategies of urban rail transit passenger flow in ShenzhenJ. Urban Mass Transit, 2026, 29(5): 39-47. DOI: 10.16037/j.1007-869x.20240262

Study on influencing factors and improving strategies of urban rail transit passenger flow in Shenzhen

  • Objective In recent years, the construction of Shenzhen's urban rail transit network has developed rapidly, but the growth rate of passenger flow has gradually slowed down, with the marginal effect becoming increasingly prominent. To improve the passenger flow efficiency of existing lines and provide planning references for urban rail transit, it is necessary to study the influencing factors of passenger flow of Shenzhen's urban rail transit and further put forward improvement strategies.
    Method Based on Shenzhen urban rail transit passenger flow data from 2012 to 2023, road and bus data, mobile phone travel data, and relevant passenger flow improvement cases, the characteristics of urban rail transit passenger flow in Shenzhen is depicted and the influencing factors are analyzed. The impacts on urban rail transit passenger flow are examined from six aspects: urban development-generated passenger flow, external passenger flow, tourism and commercial passenger flow, network matching level, transit connection level, and operation management and service. Improvement strategies for urban rail transit passenger flow are proposed from the perspectives of urban construction, connection facilities, operation management, and coordinated development of multi-level networks.
    Result & Conclusion  Among the influencing factors, urban development-generated passenger flow has the greatest impact, with a correlation coefficient of 0.87. The average daily passenger entry/exit volume at stations is 0.61 times the number of jobs for the commuters. At some hub, port, tourist and commercial stations, affected by external, tourist and commercial passenger flows, the daily entry/exit volumes are notably higher than the passenger flow level corresponding to the number of jobs in surrounding areas for the commuters. By contrast, at stations on peripheral low-speed lines, affected by network matching level, the daily entry/exit volumes are lower than the above- mentioned passenger flow level. Efficient transit connection and high-quality operation management and service can effectively increase the passenger flow.
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