This paper focuses on traffic parameters estimation at signalized intersections based on a framework combining shockwave analysis (SA) and Bayesian Network (BN) using vehicle trajectory data. Detailed queuing evolution and spillback across adjacent intersections are considered. According to shockwave analysis, the analytical probability distribution of individual vehicle’s travel time is derived based on different initial conditions. This probability distribution is parameterized by the fundamental diagram (FD) parameters, traffic volume, and cycle state (queue length). A three-layer recursive BN model is then proposed to construct the state evolution process as well as the relationships between traffic volume, cycle state, FD parameters, sampled vehicles’ arrival times and intersection travel times. As traffic volume and initial queue cannot be measured directly from sampled trajectory data, the expectation maximization (EM) algorithm and particle filtering (PF) are introduced to solve this recursive BN model. By shockwave analysis, such estimated traffic parameters are then used to estimate the maximum queue length and traffic volume of each cycle. The proposed method is evaluated using microscopic traffic simulation data as well as empirical data. Numerical results show that the proposed method achieves promising accuracy even under low penetration rates, with the mean absolute percentage error (MAPE) of the estimation bounded by 15% and generally around 10%.

本文着眼于基于结合了冲击波分析（SA）和贝叶斯网络（BN），使用车辆轨迹数据的框架的信号交叉口交通参数估计。考虑了相邻交叉口的详细排队演化和溢出。根据冲击波分析，根据不同的初始条件得出了单个车辆行驶时间的分析概率分布。此概率分布通过基本图（FD）参数，流量和周期状态（队列长度）进行参数化。然后，提出了一个三层递归BN模型来构造状态演化过程，以及交通量，自行车状态，FD参数，采样车辆的到达时间和交叉路口行驶时间之间的关系。由于无法直接从采样的轨迹数据中测量流量和初始队列，因此引入了期望最大化（EM）算法和粒子滤波（PF）来解决此递归BN模型。通过冲击波分析，这些估计的流量参数然后用于估计每个周期的最大队列长度和流量。使用微观交通模拟数据以及经验数据对提出的方法进行评估。数值结果表明，所提出的方法即使在低穿透率下也能达到有希望的精度，估计的平均绝对百分比误差（MAPE）为15％，通常约为10％。通过冲击波分析，这些估计的流量参数然后用于估计每个周期的最大队列长度和流量。使用微观交通模拟数据以及经验数据对提出的方法进行评估。数值结果表明，所提出的方法即使在低穿透率下也能达到有希望的精度，估计的平均绝对百分比误差（MAPE）为15％，通常约为10％。通过冲击波分析，这些估计的流量参数然后用于估计每个周期的最大队列长度和流量。使用微观交通模拟数据以及经验数据对提出的方法进行评估。数值结果表明，所提出的方法即使在低穿透率下也能达到有希望的精度，估计的平均绝对百分比误差（MAPE）为15％，通常约为10％。