Abstract
The dynamic spatiotemporal characteristics of queues at urban intersections are crucial to traffic operation tasks such as signal performance measure and signal optimization. This paper addresses the high time-resolution estimation of queue profile at urban signalized intersection using Extended Kalman Filtering (EKF) with data of connected vehicles (CVs). The main features of this work are as follows: (i) a machine learning method was applied to construct a dynamic shockwave propagation model based on shockwave theory and historical data of CVs; (ii) a heuristic approach was proposed to measure the shockwave speed for use in EKF; (iii) an urban queue estimator was designed to combine the dynamic shockwave propagation model and real-time shockwave information via EKF to deliver second-by-second queue profile estimates. The queue estimator does not require any priori information about vehicle arrival patterns and the market penetration rate (MPR) of CVs. The performance and robustness of the queue estimator were evaluated using both simulation and real-world CV data. The results show that the method can provide satisfactory queue estimation results at various MPR levels of CVs, with the estimation error of 2.5 vehicles at the MPR of 5%, and of 0.5 vehicle at the MPR of 40%.
Original language | English (US) |
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Pages (from-to) | 21274-21290 |
Number of pages | 17 |
Journal | IEEE Transactions on Intelligent Transportation Systems |
Volume | 23 |
Issue number | 11 |
DOIs | |
State | Published - Nov 1 2022 |
Keywords
- Connected vehicles (CVs)
- Data models
- data-fusion-based shockwave sensing
- dynamic shockwave propagation model
- Estimation
- extended Kalman filtering (EKF).
- Heuristic algorithms
- queue profile estimation
- Queueing analysis
- Real-time systems
- Sensors
- Vehicle dynamics
- extended Kalman filtering (EKF)
ASJC Scopus subject areas
- Mechanical Engineering
- Automotive Engineering
- Computer Science Applications