Simulation Evaluation of a Large-Scale Implementation of Virtual-Phase Link–Based Model Predictive Control
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 9
Abstract
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.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under Contract no. DE-AC36-08GO28308. Funding was provided by US Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office. A portion of the research was performed using computational resources sponsored by the US Department of Energy Office of Energy Efficiency and Renewable Energy, located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the US Government. By accepting the paper for publication, the publisher acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US Government purposes. This work was made possible through the close cooperation of the Chattanooga Department of Transportation and Siemens Mobility. The research team acknowledges and appreciates guidance and technical support of Cindy Shell, Tommy Trotter, Airton Kohls, Casey Lewis, and Michael Gaertner.
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Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 9 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: May 8, 2023
Accepted: Jan 30, 2024
Published online: Jun 25, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 25, 2024
ASCE Technical Topics:
- Engineering fundamentals
- Errors (statistics)
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Intersections
- Mathematics
- Models (by type)
- Simulation models
- Statistics
- Traffic congestion
- Traffic engineering
- Traffic management
- Traffic models
- Traffic signals
- Transportation engineering
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