Next-Generation Traffic Control System
The rapid development of connected and automated vehicle (CAV) technologies will undoubtedly revolutionize the traffic control system. Although the duration of the transitional process from the state-of-the-practice to the future “signal-free” system is uncertain, it is important for both traffic management agencies and traffic control industry to understand what might be happening during the transition process and how we can better prepare and facilitate the transition. In the following, it presents our road-map to the next generation traffic control system.
Patent:
Liu, H. and Zheng, J. (2019) Traffic Signal Control Using Vehicle Trajectory Data, US Patent, No. 10,497,259 B2, Issued on December 3, 2019. [PDF]
Publications for probe vehicle based traffic state estimation
1. Zheng, J., & Liu, H. X. (2017). Estimating traffic volumes for signalized intersections using connected vehicle data. Transportation Research Part C: Emerging Technologies, 79, 347-362. [PDF]
2. Zheng, F., Jabari, S. E., Liu, H. X., & Lin, D. (2018). Traffic state estimation using stochastic Lagrangian dynamics. Transportation Research Part B: Methodological, 115, 143-165. [PDF]
3. Zhang, H., Liu, H. X., Chen, P., Yu, G., & Wang, Y. (2019). Cycle-based end of queue estimation at signalized intersections using low-penetration-rate vehicle trajectories. IEEE Transactions on Intelligent Transportation Systems. [PDF]
4. Wong W., Shen S., Zhao Y., and Liu, H. X. (2019). On the estimation of connected vehicle penetration rate based on single-source connected vehicle data. Transportation Research Part B: Methodological 126, 169-191. [PDF]
5. Zhao, Y., Zheng, J., Wong, W., Wang, X., Meng, Y., & Liu, H. X. (2019). Estimation of queue lengths, probe vehicle penetration rates, and traffic volumes at signalized intersections using probe vehicle trajectories. Transportation Research Record, 0361198119856340. [PDF]
6. Zhao, Y., Zheng, J., Wong, W., Wang, X., Meng, Y., & Liu, H. X. (2019). Various methods for queue length and traffic volume estimation using probe vehicle trajectories. Transportation Research Part C: Emerging Technologies, 107, 70-91. [PDF]
7. Wang X., Shen S., Feng Y., Bezzina D., Sayer J., and Liu H.X. (2020). A Data Infrastructure for Connected Vehicle Applications. Transportation Research Record. [PDF]
8. Zhao Y., Shen S., and Liu H.X. (2021). A hidden Markov model for the estimation of correlated queues in probe vehicle environments. Transportation Research Part C: Emerging Technologies, Volume 128, July 2021, 103128. [PDF]
Publications for probe vehicle based traffic control
1. Sun, W., Zheng, J., & Liu, H. X. (2018). A capacity maximization scheme for intersection management with automated vehicles. Transportation research part C: Emerging Technologies, 94, 19-31. [PDF]
2. Feng, Y., Zheng, J., & Liu, H. X. (2018). Real-time detector-free adaptive signal control with low penetration of connected vehicles. Transportation Research Record, 2672(18), 35-44. [PDF]
3. Feng, Y., Yu, C., & Liu, H. X. (2018). Spatiotemporal intersection control in a connected and automated vehicle environment. Transportation Research Part C: Emerging Technologies, 89, 364-383. [PDF]
4. Yu, C., Feng, Y., Liu, H. X., Ma, W., & Yang, X. (2018). Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections. Transportation Research Part B: Methodological, 112, 89-112. [PDF]
5. Yu, C., Feng, Y., Liu, H. X., Ma, W., & Yang, X. (2019). Corridor level cooperative trajectory optimization with connected and automated vehicles. Transportation Research Part C: Emerging Technologies, 105, 405-421. [PDF]
6. Yu, C., Sun, W., Liu, H. X., & Yang, X. (2019). Managing connected and automated vehicles at isolated intersections: From reservation-to optimization-based methods. Transportation Research Part B: Methodological, 122, 416-435. [PDF]
7. Yang Z., Feng Y., and Liu H.X. (2021). A cooperative driving framework for urban arterials in mixed traffic conditions. Transportation Research Part C: Emerging Technologies, Volume 124, 2021, 102918. [PDF]
8. Wang, X., Jerome. Z., Zhang C., Shen, S., Kumar, V., and Liu, H. X. (2022). Trajectory Data Processing and Mobility Performance Evaluation for Urban Traffic Networks. Accepted by Transportation Research Record.
9. Jerome Z., Wang X., Shen S., Liu H.X. (2022). Determining Yellow Change and Clearance Intervals for Left-Turning Phases: Evaluation of the Current Guidelines with Connected Vehicle Data. Transportation Research Record. May 2022. doi:10.1177/03611981221091557 [PDF]
10. Wang, X., Yin Y., Feng, Y., and Liu, H.X. (2022). Learning the max pressure control for urban traffic networks considering the phase switching loss. Transportation Research Part C: Emerging Technologies. Volume 140, 2022, 103670. [PDF]
