基于非线性卡尔曼滤波的城市轨道交通客流密度短时预测方法
王何斐1滕靖2叶亮1陈宇毅3
Short-term Prediction Method for Passenger Density in Urban Rail Transit Based on Non-linear Kalman Filter
WANG Hefei1TENG Jing2YE Liang1CHEN Yuyi3
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作者信息:1.深圳市城市交通规划设计研究中心股份有限公司，518057，深圳
2.同济大学交通运输工程学院，201804，上海
3.阿里云计算有限公司，310012，杭州
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Affiliation:1.Shenzhen Urban Transport Planning Center Co., Ltd., 518057, Shenzhen, China
2.College of Transportation Engineering, Tongji University, 201804, Shanghai, China
3.Alibaba Cloud Computing Co., Ltd., 310012, Hangzhou, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.06.006
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中图分类号/CLCN:U293.13
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栏目/Col:研究报告
摘要:
［目的］为应对大客流事件，需准确识别城市轨道交通对大客流时空分布状态及演化规律，有必要基于EKF(扩展卡尔曼滤波)和UKF(无迹卡尔曼滤波)，对城市轨道交通客流密度进行短时预测。［方法］从车站和断面两个层面，介绍了自动售检票设备数据的处理方法，并划分了城市轨道交通车站及断面的舒适度等级。通过定义客流密度状态方程和量测方程，分别介绍了EKF模型和UKF模型的城市轨道交通客流密度短时预测计算方法。以国内某城市轨道交通网络化运营城市某条线路为案例，比较了EKF模型及UKF模型的预测精度。［结果及结论］算例结果表明，EKF模型及UKF模型均能通过实时采集当前时段车站自动售检票设备数据来预测下一时段的车站客流密度和断面客流密度，适用于城市轨道交通客流密度短时预测场景。相比于EKF模型，UKF模型全天分时段预测值更接近真实变化趋势，UKF模型预测值与真实值的散点分布更趋集中收敛；UKF模型的均方根误差、平均绝对误差及平均绝对百分比误差均相对更低，说明UKF模型预测精度相对更高。
Abstracts:
［Objective］ In order to cope with massive passenger flow incidents, it is necessary to identify accurately the spatial and temporal distribution state and the evolution law of massive passenger flow in urban rail transit, making short-term prediction of passenger flow density in urban rail transit based on EKF(extended Kalman filter) and UKF(unscented Kalman filter). ［Method］ From both the station and section levels, AFC(automatic fare collection) data processing method is introduced, and the comfort levels of urban rail transit station and section are classified. By defining the state equation and measurement equation of passenger flow density, the short-term prediction and computation methods of passenger density in urban rail transit using EKF and UKF models are introduced respectively. The prediction accuracy of EKF model and UKF model is compared based on a certain line under the networked operation of rail transit in a Chinese city. ［Result & Conclusion］ The results show that both EKF model and UKF model can predict the passenger flow density of the station and the section for the next period by collecting the real-time AFC data in the current period, applicable to the short-term prediction scenario of urban rail transit passenger flow density.Compared to the EKF model, the predicted values of UKF model for different time periods in a day are closer to the real trend of changes, and the scatter distribution of the UKF model predicted value and the real value is more convergent. All the RMSE(root mean square error), MAE (mean absolute error)and MAPE (mean absolute percentage error)of UKF model are relatively lower, indicating that the prediction accuracy of UKF model is relatively high.
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