Second-based queue length estimation with fusing MMW and low penetration rate CAV trajectory data

Abstract

This paper combines millimeter-wave radar (MMW) data with connected autonomous vehicle (CAV) trajectory data to estimate queue length on a second-by-second basis. Firstly, queued vehicles on multiple lanes with the same traffic movement are mapped to a virtual lane. Then, the presence of CAV or human-driven vehicle (HDV) for any given queueing index is determined. A Bayesian joint probability model is subsequently established for queue length expectation, considering the existence of the queued vehicle type as a condition. The average time headway and dissipation speed distribution are derived from departure timestamps, which allows for the calculation of the prior probability ratio of queue length. Lastly, the maximum likelihood estimation (EM) algorithm is employed for iteratively estimating the CAV penetration rate. Simulation results demonstrate the method offers a compelling trade-off between precision and second-based real-time performance, while field test results further confirm the wide applicability under various traffic conditions.

KEYWORDS:

• Queue length
• low penetration rate
• CAV trajectory data
• data fusing

Acknowledgment

This research has been jointly supported by the National Natural Science Foundation of China ‘Network Traffic Situation Awareness and Cooperatively Regulated Signalized Intersection in Low-penetration of Intelligent Connected Vehicle Environment’ (No. 52172332) and Jiangxi Provincial Natural Science Foundation (No. 20224BAB204066).

Author contributions

The authors confirm their contribution to the paper as follows: study conception and design: SH, JC, LP, and TZQ; data collection: SH, YD, and JZ; analysis and interpretation of results: SH and YD; draft manuscript preparation: SH, YD, JC, YZ, and TZQ. All authors reviewed the results and approved the final version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research has been jointly supported by the National Natural Science Foundation of China ‘Network Traffic Situation Awareness and Cooperatively Regulated Signalized Intersection in Low-penetration of Intelligent Connected Vehicle Environment’ (No. 52172332) and Jiangxi Provincial Natural Science Foundation (No. 20224BAB204066).