Cycle-Based Queue Length Estimation for Signalized Intersections Using Sparse Vehicle Trajectory Data

• Published in: IEEE transactions on intelligent transportation systems Vol. 22; no. 1; pp. 91 - 106
• Main Authors: Tan, Chaopeng, Yao, Jiarong, Tang, Keshuang, Sun, Jian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Flow velocity; Intersections; Maximum likelihood estimation; Parameter estimation; Probes; Queue length; Queueing analysis; Queues; Real time; Real-time systems; Sensors; signalized intersections; sparse vehicle trajectory data; Statistical analysis; Traffic flow; Traffic information; Trajectory
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2954937

Intersection queue length estimation using high-resolution probe vehicle trajectory data has received increasing attentions in recent years. Existing methods for cycle-based queue length estimation still face the challenge of low and/or unstable estimation accuracies under the condition of sparse vehicle trajectory data, i.e., there is no greater than one vehicle trajectory per cycle on average. To address this challenge, this study proposed a novel approach for cycle-based queue length estimation by fusing real-time and historical probe vehicle trajectory data, through a statistical parameter estimation method, i.e., maximum likelihood estimation (MLE). With known signal timing information, firstly, the historical probe trajectory data are used to acquire the arrival flow rate distribution over the entire study period. Then, a likelihood function of queue length is derived by fully exploiting real-time traffic flow information provided by the queued and non-queued probe vehicles. Finally, the MLE method is adopted to estimate the cycle-based queue lengths with the maximum probability. The proposed approach is verified using both simulation and empirical data. Results indicate that precise estimation for cycle-based queue lengths can be realized based on sparse vehicle trajectory data, while showing superiority to a representative existing method. The proposed method is basically an offline method, but it can also work in an online manner if provided a priori arrival distribution either acquired from historical probe vehicle trajectory data or a theoretical assumption.