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

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Summary

This study presents a two-stage adaptive Kalman filter for real-time traffic signal control. The algorithm accurately estimates traffic states, improving accuracy and providing reliable predictions for smoother traffic flow.

Keywords:
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Area of Science:

  • Intelligent Transportation Systems
  • Traffic Engineering
  • Control Theory

Background:

  • Real-time traffic signal control requires accurate estimation and prediction of traffic states.
  • Existing methods often struggle with data limitations and varying market penetration levels of connected vehicles.

Purpose of the Study:

  • To introduce a novel two-stage adaptive Kalman filter algorithm for enhanced traffic state estimation and prediction.
  • To improve the accuracy and reliability of turning movement counts, queue sizes, and traffic density estimations.
  • To validate the algorithm's performance using real-world and simulated data.

Main Methods:

  • A two-stage adaptive Kalman filter algorithm was developed.
  • Stage 1: Estimates turning movement (TM) counts using probe vehicle trajectory and upstream detector data.
  • Stage 2: Estimates upstream approach density and queue sizes.
  • Evaluation involved drone-collected and simulated data from a signalized intersection.

Main Results:

  • The Kalman filter significantly improved traffic state estimation accuracy compared to baseline methods.
  • Stage 1 TM estimates showed up to a 50% improvement in standard deviation.
  • Stage 2 queue size estimation improved by up to 32.8%, and traffic density by up to 18.5%.
  • Reliable predictions were achieved even at low market penetration levels (e.g., 5%).

Conclusions:

  • The proposed two-stage adaptive Kalman filter effectively enhances traffic state estimation and prediction for real-time signal control.
  • The algorithm demonstrates robustness across various market penetration levels, indicating practical applicability.
  • This approach offers a significant advancement for intelligent transportation systems and traffic management.