Centralized and Decentralized Signal Control with Short-Term Origin-Destination Demand for Network Traffic

Zhang, Can; Qiu, Tony Z.; Kim, Amy

doi:10.1155/2022/5806160

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…Conversely, distributed architectures can signifcantly decrease the overall runtime by decomposing the problem into multiple subproblems and allocating the computations to the signal controllers, thereby reducing the communication load and computational complexity of the key nodes [8,9]. However, its performance is inferior to that of the centralized architecture [10][11][12][13]. Each architecture has its own specifc focus and suitable applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Model Predictive Control for Coordinated Signals along an Arterial under Relaxing Assumptions

Zhang,

Xu,

Pan

et al. 2024

Journal of Advanced Transportation

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This paper proposes modified model predictive control (MMPC) for coordinated signals, aiming to enhance a model’s fidelity to the realistic traffic environment by relaxing typical assumptions. We focus on the arterial, where every intersection is equipped with a dual-ring-barrier signal controller that complies with the standards of the National Electric Manufacturers Association. MMPC employs the store-and-forward model to describe traffic flow, thereby transforming the signal control problem into a model-based rolling-horizon optimization problem, in which the prediction horizon is composed of several future sample intervals, commonly equal to the cycle length. A radar detector is used to collect vehicle data upstream of the stop line at every sampling instant. The optimization problem is solved to minimize the number of vehicles within the prediction horizon, and the next timing plan is determined based on the optimization results. Constraints are added and modified in order to incorporate the typical relaxed assumptions in the optimization process. For this purpose, MMPC introduces a transition-free ring-barrier structure, vehicle distribution ratio, and percent arrival before the end of green. Simulation results indicate that coordination can be maintained by MMPC without the need for transitions, and the estimation of current and future traffic states can be improved with the assistance of modified constraints. Compared with benchmark techniques, MMPC offers superior vehicle progression for coordinated movement and significant improvements in delays, number of stops, and total travel time from a system-wide perspective, with an acceptable small increase in runtime.

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