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Abstract
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.
Highlights
Real-time traffic state estimates have been increasingly recognized following the introduction of recent advanced technologies such as connected vehicle (CV) technologies
The first subsection evaluates the performance of the adaptive Kalman filter (AKF) model and compares the AKF with the Kalman filter (KF) model (Section 5.1)
The second subsection presents the performance of the neural network model (NN) model used for estimating the level of market penetration (LMP) of probe vehicles at the exit of the link (Section 5.2)
Summary
Real-time traffic state estimates have been increasingly recognized following the introduction of recent advanced technologies such as connected vehicle (CV) technologies. Nowadays, conducting research with limited probe vehicle data (e.g., CVs) is a challenge, especially when no additional data sources are provided. Past research has utilized probe data in conjunction with existing detection systems to enhance proposed traffic models, despite the limitation that fixed detection techniques (e.g., loop detectors) always have some noise in their data [2,3,4]. A probe vehicle is defined as a vehicle that provides real-time information, such as its instantaneous position and speed. Several benefits of using probe vehicle data have been recognized; for example, the high quality of data compared with existing data sources (e.g., cameras and loop detectors), and data can be collected at any location inside the network, offering a clear picture about traffic behavior at any time. Transportation agencies are putting effort into facilitating the use of probe vehicle data
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