Real-Time Turning Movement, Queue Length, and Traffic Density Estimation and Prediction Using Vehicle Trajectory and Stationary Sensor Data

This paper introduces a two-stage adaptive Kalman filter algorithm to estimate and predict traffic states required for real-time traffic signal control. Leveraging probe vehicle trajectory and upstream detector data, turning movement (TM) counts in the vicinity of signalized intersections are estimated in the first stage, while the upstream approach density and queue sizes are estimated in the second stage. The proposed approach is evaluated using drone-collected and simulated data from a four-legged signalized intersection in Orlando, Florida. The performance of the two-stage approach is quantified relative to the baseline estimation without a Kalman filter. The results show that the Kalman filter is effective in enhancing traffic state estimates at various market penetration levels, where the filter both improves the estimation accuracy over the baseline case and provides reliable state predictions. In the first stage, the standard deviation (SD) in TM estimates improves by up to 50% compared to the estimates provided by the sole use of probe vehicle headings. The proposed approach also provides predictions with a minimal SD of 92.8 veh/h at a 5% level of market penetration. In the second stage, the proposed queue size estimation method results in an enhancement to the queue size estimation of up to 32.8% compared to the estimates obtained from the baseline approach. In addition, the estimated traffic density is enhanced by up to 18.5%. The proposed two-stage approach demonstrates the capability of providing reliable turning movement predictions across varying levels of market penetration. This highlights the readiness of this approach for practical application in real-time traffic signal control systems.