深圳市城市轨道交通客流影响因素及改善策略研究

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

  • 摘要:
    目的 近年来,深圳市城市轨道交通网络建设发展迅速,但客流增速逐渐放缓,边际效应逐渐显现。为提升既有线路客流效益,也为给城市轨道交通规划提供参考,有必要研究深圳市城市轨道交通客流的影响因素,进而提出改善策略。
    方法 基于深圳市城市轨道交通2012—2023年客流数据、道路公交数据、手机应用程序出行数据及相关客流改善案例,描绘深圳市城市轨道交通客流特征,分析城市轨道交通客流影响因素。从城市开发客流、对外客流、旅游商业客流、网络匹配水平、交通接驳水平和运营管理服务等6个方面,分析了城市轨道交通客流所受影响。从城市建设、接驳设施、运营管理和多层次网络协调发展等方面提出城市轨道交通客流的改善策略。
    结果及结论 客流影响因素中影响程度最大的为城市开发客流,相关性系数达0.87;车站日均进出站量为通勤人口岗位数的0.61倍。部分对外枢纽或口岸类车站、旅游景点或商业类车站受对外客流及旅游商业客流影响,车站日均进出站量显著高于周边通勤人口岗位对应的客流水平。外围普速线路车站受网络匹配水平影响,日均进出站量低于周边通勤人口岗位对应的客流水平。高效的交通接驳水平及运营管理服务能有效提升客流。

     

    Abstract:
    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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