Simulation Evaluation of a Large-Scale Implementation of Virtual-Phase Link-Based Model Predictive Control

Shams, Andalib; Wang, Qichao; Ugirumurera, Juliette; Severino, Joseph; Jones, Wesley; Sanyal, Jibonananda

doi:10.1061/JTEPBS.TEENG-8079

Simulation Evaluation of a Large-Scale Implementation of Virtual-Phase Link-Based Model Predictive Control

Journal Article · Tue Jun 25 04:00:00 EDT 2024 · Journal of Transportation Engineering, Part A: Systems

DOI:https://doi.org/10.1061/JTEPBS.TEENG-8079· OSTI ID:2447842

; ; Ugirumurera, Juliette; Severino, Joseph; Jones, Wesley; Sanyal, Jibonananda

Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly.

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 2447842

Report Number(s):: NREL/JA-2C00-86241; MainId:87014; UUID:136729eb-9c27-4851-af6d-0f8932c24fad; MainAdminId:73865

Journal Information:: Journal of Transportation Engineering, Part A: Systems, Journal Name: Journal of Transportation Engineering, Part A: Systems Journal Issue: 9 Vol. 150

Country of Publication:: United States

Language:: English

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