Distributed identification of the Cell Transmission traffic model: A case study

Rinaldi, Marco; Capisani, Luca Massimiliano; Ferrara, Antonella; Núñez, Alfredo; Hajiahmadi, Mohammad; Schutter, Bart De

doi:10.1109/acc.2012.6315461

Cited by 7 publications

(9 citation statements)

References 16 publications

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“…Furthermore, we set R = 0.4 and impose, to prevent congestion in the area surrounding the freeway, the additional constraint that the queues at the ramps do not exceed 200 vehicles. The METANET model parameters have been identified by using real data acquired from the sensors placed on the considered Dutch freeway ( [21]). …”

Section: Simulation Resultsmentioning

confidence: 99%

Adaptive networked model predictive control of freeway traffic systems

Bianchi

Ferrara

Benedetto

2013

2013 American Control Conference

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A networked traffic control scheme is presented in this paper. We refer to a realistic case in which the control actions are computed in a control center located far from the traffic system and then transmitted, through a wireless communication channel, to the actuators placed along the road, i.e. on-ramp traffic lights in the considered case of ramp metering control. More specifically, we assume that the communication channel is affected by delays and packet loss. We propose to adopt a control strategy of model predictive control (MPC) nature based on the use of a buffer to compensate for the time delays, so that the designed traffic networked control scheme provides performance significantly close to those of the ideal case (i.e. that with no delay). Moreover, in order to limit the computational load, a modified version of the control strategy is proposed, in which the length of the optimization horizon of the predictive control algorithm, and, consequently, the buffer length, is updated relying on a delay estimation. A simulation analysis of the different networked control proposals applied to a Dutch freeway is provided, along with the comparison with a conventional MPC equipped with an hold mechanism. The superiority in terms of performance of the proposed networked MPC with adaptive buffer length is apparent.

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Section: Simulation Resultsmentioning

confidence: 99%

Adaptive networked model predictive control of freeway traffic systems

Bianchi

Ferrara

Benedetto

2013

2013 American Control Conference

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“…In the literature [34], the number of modes is

2^{N}

for a road segment with N cells. They only considered the modes in the vertices and ignored the multi-mode switchings of the edges.…”

Section: Dgha Model For Urban Freeway Networkmentioning

confidence: 99%

“…Thus we develop an algorithm to implement modeling procedure of traffic networks by accomplishing parallel composition of cells with the help of a computer program. So compared with the traditional manual calculation [17,18,34], the modeling procedure is easily-extensible with the help of the algorithm and can be applied to traffic networks with arbitrary topology structures and sizes, including the road networks with signal intersections.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata

Chen

Guo

Wang

2017

Sensors

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In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

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“…The freeway CTM cells have been identified, following the procedure presented in [30], on the basis of real data acquired on the Dutch freeway. The structure of the considered freeway stretch in terms of cells and the consequent subdivision into clusters is reported in Table I.…”

Section: Case Studymentioning

confidence: 99%

Freeways as Systems of Systems: A Distributed Model Predictive Control Scheme

Ferrara

Oleari

Sacone

et al. 2015

IEEE Systems Journal

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The objective of this paper is the definition of a distributed model predictive control scheme for freeway systems. The proposed control scheme is based on an innovative reformulation of the adopted prediction model. The control approach is stated in a centralized framework, in two different cluster-based distributed architectures and in a decentralized one. Specifically, control distribution is exploited in this paper as a means to increase the practical applicability of an advanced control scheme that is often limited, due to its computational complexity and to the typical size of the systems under concern. The novelty of the proposed approach mainly relies on the way in which the control distribution is operated, since it is dictated by the subdivision of the freeway system into portions, possibly with heterogeneous features and of different sizes, called clusters. A cluster, in our view, is a system, so that the whole freeway can be regarded as a system of systems. The performance of the control scheme in its different versions is assessed in simulation, making reference to a case study developed on the basis of real data. Index Terms-Distributed control, model predictive control (MPC), traffic control.1932-8184

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Distributed identification of the Cell Transmission traffic model: A case study

Cited by 7 publications

References 16 publications

Adaptive networked model predictive control of freeway traffic systems

Adaptive networked model predictive control of freeway traffic systems

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata

Freeways as Systems of Systems: A Distributed Model Predictive Control Scheme

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