CAJ | 학술논문

基于状态空间方法构建适用于城市轨道交通网络的短时客流OD(origin-destination)估计模型.利用自动售检票数据分析得到OD间乘客行程时间分布特征，构建基于行程时间分布的客流到达系数，以此建立OD流与车站进出站客流间相互关系，并以车站客流分离率为状态变量构建结构化OD矩阵估计状态空间模型.以北京市轨道交通为对象进行案例分析，结果表明，当估计时段长度为15 min时，估计平均相对误差为35.5%；为30 min时，估计平均相对误差为20.4%；为60 min时，估计平均相对误差为16.3%.所构建模型能能有效解决城市轨道交通短时客流估计问题，具有一定的实用性.
기우상태공간방법구건괄용우성시궤도교통망락적단시객류OD(origin-destination)고계모형.이용자동수검표수거분석득도OD간승객행정시간분포특정，구건기우행정시간분포적객류도체계수，이차건립OD류여차참진출참객류간상호관계，병이차참객류분리솔위상태변량구건결구화OD구진고계상태공간모형.이북경시궤도교통위대상진행안례분석，결과표명，당고계시단장도위15 min시，고계평균상대오차위35.5%；위30 min시，고계평균상대오차위20.4%；위60 min시，고계평균상대오차위16.3%.소구건모형능능유효해결성시궤도교통단시객류고계문제，구유일정적실용성.
A short-time passenger flow origin-destination matrix estimation model based on state-space approach is proposed for urban rail transit network. An arrive coefficient is specially defined for establishing the relationship between OD flows and in-and out-flows at stations, using the passengers' travel time distribution through statistical analysis of historical automatic fare collection records, then a structured state-space model using station passenger flow split rate as the state variable is proposed. Finally, a numerical example of Beijing subway network is made. The results show that, when the estimation time interval is 15 minutes, the average relative deviations is 35.5%;if the time interval is 30 minutes, the relative deviations is 20.4%;if the time interval is 60 minutes, the relative deviations is 16.3%. Case study validates that the model meets the request of short-time passenger flow estimation for large-scale urban rail transit network and has strong practicability.