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李若怡, 李得伟. 城市轨道交通动态OD矩阵分析及估计模型研究[J]. 铁路计算机应用, 2017, 26(1): 63-67.
引用本文: 李若怡, 李得伟. 城市轨道交通动态OD矩阵分析及估计模型研究[J]. 铁路计算机应用, 2017, 26(1): 63-67.
LI Ruoyi, LI Dewei. Analysis and estimation model of dynamic OD matrixes for urban rail transit[J]. Railway Computer Application, 2017, 26(1): 63-67.
Citation: LI Ruoyi, LI Dewei. Analysis and estimation model of dynamic OD matrixes for urban rail transit[J]. Railway Computer Application, 2017, 26(1): 63-67.

城市轨道交通动态OD矩阵分析及估计模型研究

Analysis and estimation model of dynamic OD matrixes for urban rail transit

  • 摘要: 客流动态起讫点(OD)矩阵是城市轨道交通实现动态运营管理的重要基础,准确地估计动态OD矩阵对城市轨道交通实际运营管理水平的提高有着重要意义。文章从空间方面分析了起讫站点性质、终点站吸引量、线路属性、起讫站点是否同线的影响,从时间方面分析了列车发车间隔、OD间换乘次数和距离的影响。构建了城市轨道交通动态OD矩阵估计模型,并选取北京市城市轨道交通网络的局部区域作为验证案例,对所提出模型的估计效果进行研究分析,所得结果表明,文中的模型较采用历史数据进行估计,在早晚高峰时精度提高约4%~10%、平峰期时精度提高约8%~17%,同时在15 min、30 min、60 min粒度下全日平均精度分别提高8.67%、11.75%、3.46%,验证了模型的可行性。

     

    Abstract: Dynamic origin-destination(OD) matrix is an important basis for the dynamic operation management of urban rail transit, so it is significant to accurately estimate the dynamic OD matrix for the improvement of the actual operation management level of urban rail transit. This article analyzed the influencing factors of the temporal and spatial distribution for the OD passenger flow, such as the space influencing factors including land-use type, attracted traffic flow, line properties, if the starting point and the terminal were on the same line, and the time influencing factors including riding time, transfer times and distance. This article built the estimation model of passenger flow dynamic OD matrix for the urban rail transit, selected the local area of Beijing urban rail transit network as a case study, analyzed the estimation effect of the model. Comparing the proposed model with historical data, the estimation accuracy of the morning and evening peak was improved by 4% ~ 10%, the flat peak was improved by 8% ~17%. Meanwhile, under the granularity of 15 min, 30 mins, and 60 mins, the full-day average accuracy was increased by 8.67%, 11.75%, 3.46%.

     

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