Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach

Geroliminis, Nikolas; Haddad, Jack; Ramezani, Mohsen

doi:10.1109/tits.2012.2216877

Cited by 444 publications

(282 citation statements)

References 36 publications

(49 reference statements)

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“…For the sake of brevity, the full description of the solution method is not presented here, however, the reader can refer to Geroliminis et al (2013) for further information. In the following, we present the outlines of the mixed network MPC through the block diagrams presented in Fig.…”

Section: Solution Approach -An Mpc Controllermentioning

confidence: 99%

“…using micro-simulations of the San-Francisco business district center presented in Geroliminis and Daganzo (2007), Ji et al (2010) or Keyvan-Ekbatani et al (2012), and (III) model-based plant, e.g. Geroliminis et al (2013) and Daganzo (2007). In this paper, we follow the later to present the reality of mixed networks as the model (8)- (29) is utilized but with integrating noise in demand and errors in MFDs, similar to Eqs.…”

Section: Solution Approach -An Mpc Controllermentioning

confidence: 99%

“…In this paper, we follow the later to present the reality of mixed networks as the model (8)- (29) is utilized but with integrating noise in demand and errors in MFDs, similar to Eqs. (23)- (27) in Geroliminis et al (2013), such that the dynamics evolutions of the plant and the model are not the same. Demand noise is considered fully random, while errors in the MFD have some heteroscedastic behavior, i.e.…”

Section: Solution Approach -An Mpc Controllermentioning

confidence: 99%

“…The MPC controller obtains the optimal control sequences for the current horizon by solving an optimization problem using the direct sequential method, also referred to single-shooting or control vector parametrization (CVP) in the literature, see Geroliminis et al (2013). Note that several ''control policies'', see left-bottom of the figure, which will be introduced in Section 4, determine the level of coordination between the two control entities, and the control structure, e.g.…”

Section: Solution Approach -An Mpc Controllermentioning

confidence: 99%

See 3 more Smart Citations

Cooperative traffic control of a mixed network with two urban regions and a freeway

Haddad

Ramezani

Geroliminis

2013

Transportation Research Part B: Methodological

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232

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a b s t r a c tCurrently most optimization methods for urban transport networks (i) are suited for networks with simplified dynamics that are far from real-sized networks or (ii) apply decentralized control, which is not appropriate for heterogeneously loaded networks or (iii) investigate good-quality solutions through micro-simulation models and scenario analysis, which make the problem intractable in real time. In principle, traffic management decisions for different sub-systems of a transport network (urban, freeway) are controlled by operational rules that are network specific and independent from one traffic authority to another. In this paper, the macroscopic traffic modeling and control of a large-scale mixed transportation network consisting of a freeway and an urban network is tackled. The urban network is partitioned into two regions, each one with a well-defined Macroscopic Fundamental Diagram (MFD), i.e. a unimodal and low-scatter relationship between region density and outflow. The freeway is regarded as one alternative commuting route which has one on-ramp and one off-ramp within each urban region. The urban and freeway flow dynamics are formulated with the tool of MFD and asymmetric cell transmission model, respectively. Perimeter controllers on the border of the urban regions operating to manipulate the perimeter interflow between the two regions, and controllers at the on-ramps for ramp metering are considered to control the flow distribution in the mixed network. The optimal traffic control problem is solved by a Model Predictive Control (MPC) approach in order to minimize total delay in the entire network. Several control policies with different levels of urban-freeway control coordination are introduced and tested to scrutinize the characteristics of the proposed controllers. Numerical results demonstrate how different levels of coordination improve the performance once compared with independent control for freeway and urban network. The approach presented in this paper can be extended to implement efficient real-world control strategies for large-scale mixed traffic networks.

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Section: Solution Approach -An Mpc Controllermentioning

confidence: 99%

Section: Solution Approach -An Mpc Controllermentioning

confidence: 99%

Section: Solution Approach -An Mpc Controllermentioning

confidence: 99%

Section: Solution Approach -An Mpc Controllermentioning

confidence: 99%

See 2 more Smart Citations

Cooperative traffic control of a mixed network with two urban regions and a freeway

Haddad

Ramezani

Geroliminis

2013

Transportation Research Part B: Methodological

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232

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“…E-mail: R.Mat-Jusoh.1@research.gla.ac.uk and Konstantinos.Ampountolas@glasgow.ac.uk improve mobility in single-region homogeneous [4], [9], [10] or multi-region heterogeneous networks [11], [12], [13], [14], [15]. A perimeter flow control policy "meters" the input flow to the system and hold vehicles outside a protected network area if necessary, so as to maximise the throughput.…”

Section: Introductionmentioning

confidence: 99%

Multi-gated perimeter flow control of transport networks

Jusoh

Ampountolas

2017

2017 25th Mediterranean Conference on Control and Automation (MED)

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Abstract-This paper develops a control scheme for the multi-gated perimeter traffic flow control problem of urban road networks. The proposed scheme determines optimally distributed input flows (or feasible entrance link green times) for a number of gates located at the periphery of a protected network area. A macroscopic model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, we augment the basic state-space model with additional state variables to account for the queues at store-and-forward origin links at the periphery. We aim to equalise the relative queues at origin links and maintain the vehicle accumulation in the protected network around a desired point, while the system's throughput is maximised. The perimeter traffic flow control problem is formulated as a convex optimal control problem with constrained control and state variables. For real-time control, the optimal control problem is embedded in a rolling-horizon scheme using the current state of the whole system as the initial state as well as predicted demand flows at entrance links. A meticulous simulation study is carried out for a 2.5 square mile protected network area of San Francisco, CA, including fifteen gates of different geometric characteristics. Results demonstrate the efficiency and equity properties of the proposed approach to better manage excessive queues outside of the protected network area and optimally distribute the input flows.

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Nonlinear gating control for urban road traffic network using the network fundamental diagram

Csikós

Tettamanti

Varga

2014

J of Advced Transportation

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Summary This work proposes a nonlinear model predictive controller for the urban gating problem. The system model is formalized based on a research on existing models of the network fundamental diagram and the perimeter control systems. For the existing models, modifications are suggested: additional state variables are allocated to describe the queue dynamics at the network gates. Using the extended model, a nonlinear model predictive controller is designed offering a ‘non‐greedy’ policy compared with previous, ‘greedy’ gating control designs. The greedy and non‐greedy nonlinear model predictive control (NMPC) controllers are compared with a greedy linear feedback proportional‐integral‐derivative (PID) controller in different traffic situations. The proposed non‐greedy NMPC controller outperforms the other two approaches in terms of travel distance performance and queue lengths. The performance results justify the consideration of queue lengths in dynamic modeling, and the use of NMPC approach for controller design. Copyright © 2014 John Wiley & Sons, Ltd.

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Optimal Perimeter Control for Two Urban Regions With Macroscopic Fundamental Diagrams: A Model Predictive Approach

Cited by 444 publications

References 36 publications

Cooperative traffic control of a mixed network with two urban regions and a freeway

Cooperative traffic control of a mixed network with two urban regions and a freeway

Multi-gated perimeter flow control of transport networks

Nonlinear gating control for urban road traffic network using the network fundamental diagram

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