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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Developing a Neural–Kalman Filtering Approach for Estimating Traffic Stream Density Using Probe Vehicle Data
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Developing a Neural–Kalman Filtering Approach for Estimating Traffic Stream Density Using Probe Vehicle Data

机译：开发神经-卡尔曼滤波方法以使用探测车辆数据估算交通流密度

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摘要

This paper presents a novel model for estimating the number of vehicles along signalized approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to produce reliable traffic vehicle count estimates, considering real-time estimates of the system noise characteristics. The AKF utilizes only real-time probe vehicle data. The AKF is demonstrated to outperform the traditional Kalman filter, reducing the prediction error by up to 29%. In addition, the paper introduces a novel approach that combines the AKF with a neural network (AKFNN) to enhance the vehicle count estimates, where the neural network is employed to estimate the probe vehicles’ market penetration rate. Results indicate that the accuracy of vehicle count estimates is significantly improved using the AKFNN approach (by up to 26%) over the AKF. Moreover, the paper investigates the sensitivity of the proposed AKF model to the initial conditions, such as the initial estimate of vehicle counts, initial mean estimate of the state system, and the initial covariance of the state estimate. The results demonstrate that the AKF is sensitive to the initial conditions. More accurate estimates could be achieved if the initial conditions are appropriately selected. In conclusion, the proposed AKF is more accurate than the traditional Kalman filter. Finally, the AKFNN approach is more accurate than the AKF and the traditional Kalman filter since the AKFNN uses more accurate values of the probe vehicle market penetration rate.
机译：本文提出了一种新的模型，用于估计沿信号途径的车辆数量。考虑到系统噪声特性的实时估计，所提出的估计算法利用自适应卡尔曼滤波器（AKF）来生成可靠的交通车辆计数估计。 AKF仅使用实时探测车辆数据。 AKF被证明优于传统的卡尔曼滤波器，可将预测误差降低多达29％。此外，本文介绍了一种新颖的方法，该方法将AKF与神经网络（AKFNN）相结合以增强车辆数量估计，其中神经网络用于估计探测车辆的市场渗透率。结果表明，与AKF相比，使用AKFNN方法显着提高了车辆计数估计的准确性（最多提高了26％）。此外，本文研究了所提出的AKF模型对初始条件的敏感性，例如车辆计数的初始估计，状态系统的初始平均估计以及状态估计的初始协方差。结果表明，AKF对初始条件敏感。如果适当选择初始条件，则可以实现更准确的估计。总之，提出的AKF比传统的Kalman滤波器更准确。最后，由于AKFNN使用更准确的探测车市场渗透率值，因此AKFNN方法比AKF和传统的卡尔曼滤波器更准确。

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