Efficient network-wide model-based predictive control for urban traffic networks

Lin, Shu; Schutter, Bart De; Xi, Yugeng; Hellendoorn, Hans

doi:10.1016/j.trc.2012.02.003

Cited by 166 publications

(121 citation statements)

References 17 publications

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“…In the previous subsection, sufficient condition has been presented to guarantee the local synchronization of network model (1). However, it is more interesting and significant to study the global synchronization.…”

Section: Global Adaptive Synchronizationmentioning

confidence: 99%

Synchronization of Uncertain Complex Networks with Time‐Varying Node Delay and Multiple Time‐Varying Coupling Delays

Zhang

Wang

et al. 2017

Asian Journal of Control

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This paper investigates the synchronization problem of a class of complex dynamical networks via an adaptive control method. It differs from existing works in considering intrinsic delay and multiple different time-varying coupling delays, and uncertain couplings. A simple approach is used to linearize the uncertainties with the norm-bounded condition. Simple but suitable adaptive controllers are designed to drive all nodes of the complex network locally and globally synchronize to a desired state. In addition, several synchronization protocols are deduced in detail by virtue of Lyapunov stability theory and a Cauchy matrix inequality. Finally, a simulation example is presented, in which the dynamics of each node are timevarying delayed Chua chaotic systems, to demonstrate the effectiveness of the proposed adaptive method.

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Section: Global Adaptive Synchronizationmentioning

confidence: 99%

Synchronization of Uncertain Complex Networks with Time‐Varying Node Delay and Multiple Time‐Varying Coupling Delays

Zhang

Wang

et al. 2017

Asian Journal of Control

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“…This means an indirect inclusion of the estimated, predicted travel time. Direct co-design of travel time estimation/prediction information with urban tra c control solution gives rise to improved transportation service, e.g., Lin et al (2012) proposes a link travel time minimization in a predictive way. Ensuring performance (e.g., enforced travel time) metrics via tra c control policies is a relevant, non trivial research path, especially in case of large scale tra c networks.…”

Section: Introductionmentioning

confidence: 99%

Network traffic flow optimization under performance constraints

Csikós

Charalambous

Farhadi

et al. 2017

Transportation Research Part C: Emerging Technologies

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In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit ow characteristics. Within this framework of constrained optimal boundary ow gating, two main performance metrics are considered: (a) rst, connected to the NFD, the concept of average network travel time and delay as a performance metric is de ned;(b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled in ow demands. External queue capacities in terms of nite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network's aggregated behaviour.By controlling the number of vehicles joining the internal queue from the external ones, herewith a network tra c ow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically e ciently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be 1 interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satis ed. Comparative numerical simulation studies on a microscopic tra c simulator are carried out to show the bene ts of the proposed method.

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“…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).…”

Section: Introductionmentioning

confidence: 99%

“…By using an improved nonlinear traffic flow model, Ref. [17] proposed a centralized MPC model for network-wide traffic signal coordination. In addition, the optimization model reported in [17] was improved and replaced by a mixed-integer linear programming model in [18].…”

Section: Introductionmentioning

confidence: 99%

“…[17] proposed a centralized MPC model for network-wide traffic signal coordination. 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].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic Model Predictive Control for Urban Traffic Networks

Gao

et al. 2017

Applied Sciences

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This paper proposes a stochastic model predictive control (MPC) framework for traffic signal coordination and control in urban traffic networks. One of the important features of the proposed stochastic MPC model is that uncertain traffic demands and stochastic disturbances are taken into account. Aiming to effectively model the uncertainties and avoid queue spillback in traffic networks, we develop a stochastic expected value model with chance constraints for the objective function of the stochastic MPC model. The objective function is defined to minimize the queue length and the oscillation of green time between any two control steps. Furthermore, by embedding the stochastic simulation and neural networks into a genetic algorithm, we propose a hybrid intelligent algorithm to solve the stochastic MPC model. Finally, numerical results by means of simulation on a road network are presented, which illustrate the performance of the proposed approach.

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Efficient network-wide model-based predictive control for urban traffic networks

Cited by 166 publications

References 17 publications

Synchronization of Uncertain Complex Networks with Time‐Varying Node Delay and Multiple Time‐Varying Coupling Delays

Synchronization of Uncertain Complex Networks with Time‐Varying Node Delay and Multiple Time‐Varying Coupling Delays

Network traffic flow optimization under performance constraints

Stochastic Model Predictive Control for Urban Traffic Networks

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