城市轨道交通高峰时段站间起讫点矩阵预测模型

Forecasting Model of Peak-Period Station-to-Station Origin-Destination Matrix in Urban Rail Transit Systems

高峰小时单向最大断面客流量是城市轨道交通规划与设计阶段的重要参考依据.为了确定这一参数,需对高峰时段内出发的乘客选择的出发时刻与路径进行预测.高峰时段站间起讫点(OD)矩阵反映了城市轨道交通乘客的出行需求,是整个预测的基础.在全天站间OD矩阵已知的前提下,以中国重庆市为研究对象,首先分析传统的重力模型在预测城市轨道交通高峰时段站间OD矩阵时的优、缺点,并在此基础上进一步提出站间客流高峰时段系数模型.比较结果表明,在同一数据源下,站间客流高峰时段系数模型能有效改善传统的重力模型所存在的缺陷,预测结果明显更优.该模型预测结果的标准误差为12.90人次,相较于重力模型的29.33人次降低了56.02%.

Urban mass transit is playing a significant role in supporting and promoting urban development. An important indicator for the planning and design of urban rail transit may be succinctly summarized by passenger flow models within a peak hour; one important feature of the model is the maximum single-direction flow. To determine this feature, it is necessary to forecast passengers＇ departure time and route choice during a peak period. As the basis of this process, the peak-period station-to-station origin-destination （OD） matrix reflects passengers＇ travel needs. This paper tests the traditional gravity models to find the pattern that forecasts the peak-period station-to-station OD matrix in urban rail transit. A real-world case study of Chongqing, China, is used as a model performance measure. To alleviate its over-estimation when the effect of the deterrence function between two stations is too small, the gravity-model-based peak period coefficient （PPC） model is introduced. By comparing the PPC and gravity models using the same dataset, the results indicate that the PPC model is superior to the gravity model. The standard deviation of the PPC model is 12.90 passengers, which is 56.02% lower than that of the gravity model, which is 29.33 passengers.