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.
The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates—with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application.
My mom has always been there for me. She is the biggest role model in my life and always be. I would like to thank her for all her support, her advice, her patience, and her faith. I dedicate this accomplishment to my mom because she is the best for me. I love you, mom. قوي رجل كون ل يب العتناهئم ادلي و شكر أ ان ود أ I would like to thank my parents for raising me to be a strong man I would like to dedicate my lovely wife (Lona) for her support. I would like also to dedicate my brothers (Anas, Odai, Dr. Qusai, Loai, Suhaib, and Shahed) for their support, their advice. Lastly, I would like to thank my lovely sisters (Raeda, Hana, Maram, and Liana) for their emotional support. v ACKNOWLEDGEMENTS I would like to express my gratitude to my advisor, Professor Hesham Rakha, for giving me the opportunity to work with him. Many appreciations for his support, his guidance, and his patience. He always has been there for me. My thanks also go to my committee member, Dr. Pamela Murray-Tuite. She is one of my best teachers that affected on me during My Master's. I would like to thank her for accepting to be in my committee. I would like also to acknowledge Dr. Jianhe Du for writing the simulation file construction and calibration section. Also, I would like to thank her for helping me to understand the network characteristics and evacuation scenarios. I would like also to thank my teachers that taught me during my graduate study at Virginia Tech. Lastly, I must thank the MATS University Transportation Center for funding this research and allowing me to obtain my M.S. degree. vi
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