交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (1): 55-64.DOI: 10.16097/j.cnki.1009-6744.2026.01.006

• 智能交通系统与信息技术 • 上一篇    下一篇

基于约束卡尔曼滤波的信控干线动态OD估计

郭瑞军*1,盛诣皓1,姜凯宁2,郝梓彤1   

  1. 1. 大连交通大学,交通工程学院,辽宁大连116028;2.中国铁路沈阳局集团有限公司,沈阳110001
  • 收稿日期:2025-10-17 修回日期:2025-11-24 接受日期:2025-12-17 出版日期:2026-02-25 发布日期:2026-02-13
  • 作者简介:郭瑞军(1977—),男,内蒙古和林格尔人,教授,博士。
  • 基金资助:
    辽宁省兴辽英才计划项目(XLYC2411087);2025年度辽宁省科协科技创新智库项目(LNKX2025XK03)。

Dynamic OD Estimation for Signalized Arterials via Constrained Kalman Filter

GUO Ruijun*1, SHENG Yihao1, JIANG Kaining2, HAO Zitong1   

  1. 1. School of Traffic Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China; 2. China Railway Shenyang Group Co Ltd, Shenyang 110001, China
  • Received:2025-10-17 Revised:2025-11-24 Accepted:2025-12-17 Online:2026-02-25 Published:2026-02-13
  • Supported by:
    Xingliao Talent Plan Project of Liaoning Province, China (XLYC2411087);2025 Technology Innovation Think Tank Project of the Association for Science and Technology, Liaoning Province, China (LNKX2025XK03)。

摘要: 针对城市干线多路径交通需求时变,传统的OD估计方法难以满足信号协调控制对精度与实时性的要求,本文提出一种基于等式约束卡尔曼滤波的干线动态OD估计方法。模型首先建立干线中转向流量与OD之间的时空映射关系,分析信控交叉口上游信号配时对下游车流量到达的关系,增加额外等式关系引入观测方程以提高模型准确性。为确保状态估计满足流量守恒等物理约束,采用带等式约束的卡尔曼滤波方法,并基于最小均方误差原则推导状态修正机制并求解模型。以大连市山东路干线交通为应用案例,结果表明:关键路径OD的平均绝对百分比误差(MAPE)分别为12.3%和17.5%。与传统转向流量模型相比,所提方法在精度上具有一定提升,能更好地捕捉OD流量的时变特征,可以为干线多路径信号协调控制提供可靠的实时输入。

关键词: 城市交通, 动态OD估计, 约束卡尔曼滤波, 信号配时条件, 转向流量

Abstract: To address the time-varying multi-path demand on arterial corridors and the inability of conventional (Origin Destination) OD estimation methods to meet the accuracy and real-time requirements of signal coordination, this paper proposes a dynamic arterial OD estimation method based on an equality-constrained Kalman filter. The model first builds a spatio-temporal mapping between turning flows and OD demands, incorporates signal timing based arrival equalities into the measurement equation to improve model accuracy. To ensure estimates satisfy flow-conservation and other physical constraints, an equality constrained Kalman filter is used and a state-correction mechanism is derived under the minimum mean-square-error criterion to solve the model. A case study on the arterial traffic of Shandong Road in Dalian city shows that the mean absolute errors (MAPE) for two key paths are 12.3% and 17.5%. Compared with a traditional turning-flow model, the proposed approach improves estimation accuracy and better captures the temporal variation of OD flows, providing reliable real-time inputs for multi-path signal coordination.

Key words: urban transportation, dynamic OD estimation, constrained Kalman filter, signal-timing conditions, turning flows

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