Distributed Model Predictive Control Method for Optimal Coordination of Signal Splits in Urban Traffic Networks

Abstract

Coordination and control approaches based on model predictive control (MPC) have been widely investigated for traffic signal control in urban traffic networks. However, due to the complex non‐linear characters of traffic flows and the large scale of traffic networks, a basic challenge faced by these approaches is the high online computational complexity. In this paper, to reduce the computational complexity and improve the applicability of traffic signal control approaches based on MPC in practice, we propose a distributed MPC approach (DCA‐MPC) to coordinate and optimize the signal splits. Instead of describing the dynamics of traffic flow within each link of the traffic network with a simplified linear model, we present an improved nonlinear traffic model. Based on the nonlinear model, an MPC optimization framework for the signal splits control is developed, whereby the interactions between subsystems are accurately modeled by employing two interconnecting constraints. In addition, by designing a novel dual decomposition strategy, a distributed coordination algorithm is proposed. Finally, with a benchmark traffic network, experimental results are given to illustrate the effectiveness of the proposed method.

Stochastic Model Predictive Control for Urban Traffic Networks

“…Note that, for computational simplicity, we assume the covariance matrix Σ 1 and Σ 2 remain unchanged during the whole simulation period. Since previous studies [20][21][22] have shown that it seems reasonable to select the prediction horizon P = 3, the experiment results reported in this study are all achieved under the condition that P = 3. As a result, the covariance matrix Σ 1 and Σ 2 are three-dimensional matrices.…”

“…It worth noting that, compared with linear models, nonlinear models usually describe the traffic dynamics of road networks more precisely. In future, in order to develop a nonlinear stochastic MPC framework, we can reference the ideas of modeling that were used in the nonlinear MPC models [21,36].…”

“…In addition, the optimization model reported in [17] was improved and replaced by a mixed-integer linear programming model in [18]. To enhance the computational efficiency and make the application of the MPC approach feasible for large scale urban traffic networks, hierarchical (or decentralized) and distributed MPC models can be found in later works [19][20][21][22].…”

“…Since model predictive control (MPC) can deal with complex constraints and avoid myopic control schemes in large-scale control problems, MPC approaches are used to model and coordinate traffic signals in urban traffic networks [15][16][17][18][19][20][21][22]. For instance, the authors in [15] proposed a linear model and a multi-agent control strategy for traffic signal coordination in urban traffic networks (UTN).…”

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

“…Ye et al [25] have used a distributed version of MPC to optimize the signal splits in an urban traffic network. Yu et al [26], [27] used MPC to control inter-vehicle distances, and minimize fuel efficiency.…”