Coordinated and integrated control of motorway networks via non-linear optimal control

Kotsialos, Apostolos; Papageorgiou, Markos; Mangeas, Morgan; Haj-Salem, Habib

doi:10.1016/s0968-090x(01)00005-5

Cited by 270 publications

(138 citation statements)

References 15 publications

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“…The use of variable speed limits on freeways is described in [2,21,32,49], and the use of route guidance in [11,13,26]. Several authors have described methods for coordinated control of freeways using different traffic control measures [6,19,28,29].…”

Section: The Need For Intelligent Dynamic Traffic Managementmentioning

confidence: 99%

“…Using a receding horizon approach, only the first step of the computed control signal is applied, and next the optimization is started again with the prediction horizon shifted one time step further. MPC has already been applied to coordinated control of freeway networks in [6,19,29]. In this chapter, which collects and extends several of our previous results reported in [19-21, 52-54, 59, 60], we demonstrate how MPC can be used to obtain coordinated and integrated control of traffic networks containing both freeways and urban roads as well as a wide variety of traffic control measures, such that a balanced trade-off is obtained between various performance criteria and such that (hard) constraints are taken into account.…”

Section: The Need For Intelligent Dynamic Traffic Managementmentioning

confidence: 99%

“…In this chapter, which collects and extends several of our previous results reported in [19-21, 52-54, 59, 60], we demonstrate how MPC can be used to obtain coordinated and integrated control of traffic networks containing both freeways and urban roads as well as a wide variety of traffic control measures, such that a balanced trade-off is obtained between various performance criteria and such that (hard) constraints are taken into account. Other publications that deal with MPC or MPC-like approaches for traffic control are [12,16,29,43]. …”

Section: The Need For Intelligent Dynamic Traffic Managementmentioning

confidence: 99%

“…For the prediction of the traffic flows on the freeway part of the network we use the destination-independent METANET model from [29,30,39]. We will briefly present the basic METANET model here.…”

Section: Freeway Traffic Flow Modelmentioning

confidence: 99%

“…We will briefly present the basic METANET model here. For a full description we refer the interested reader to [29,30,39]. The METANET model represents a network as a directed graph with the links (indicated by the index m) corresponding to freeway stretches.…”

Section: Freeway Traffic Flow Modelmentioning

confidence: 99%

See 4 more Smart Citations

Model-based Control of Intelligent Traffic Networks

Schutter

Hellendoorn

Hegyi

et al. 2009

Intelligent Infrastructures

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Road traffic networks are increasingly being equipped and enhanced with various sensing, communication, and control units, resulting in an increased intelligence in the network and offering additional handles for control. In this chapter we discuss some advanced model-based control methods for intelligent traffic networks. In particular, we consider model predictive control (MPC) of integrated freeway and urban traffic networks. We present the basic principles of MPC for traffic control including prediction models, control objectives, and constraints. The proposed MPC control approach is modular, allowing the easy substitution of prediction models and the addition of extra control measures or the extension of the network. Moreover, it can be used to obtain a balanced trade-off between various objectives such as throughput, emissions, noise, fuel consumption, etc. Moreover, MPC also allows the integration and network-wide coordination of various traffic control measures such as traffic signals, speed limits, ramp metering, lane closures, etc. We illustrate the MPC approach for traffic control with two case studies. The first case study involves control of a freeway stretch with a balanced trade-off between total time spent, fuel consumption, and emissions as control objective. The second case study has a more complex layout and involves control of a mixed urban-freeway network with total time spent as control objective.

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Section: The Need For Intelligent Dynamic Traffic Managementmentioning

confidence: 99%

Section: The Need For Intelligent Dynamic Traffic Managementmentioning

confidence: 99%

Section: The Need For Intelligent Dynamic Traffic Managementmentioning

confidence: 99%

Section: Freeway Traffic Flow Modelmentioning

confidence: 99%

Section: Freeway Traffic Flow Modelmentioning

confidence: 99%

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Model-based Control of Intelligent Traffic Networks

Schutter

Hellendoorn

Hegyi

et al. 2009

Intelligent Infrastructures

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Modeling and Control of Traffic Flow

2012

Advanced Mobility and Transport Engineering

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Robust receding horizon parameterized control for multi‐class freeway networks: A tractable scenario‐based approach

Liu

Sadowska

Frejo

et al. 2016

Intl J Robust & Nonlinear

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In this paper, we propose a tractable scenario-based receding horizon parameterized control (RHPC) approach for freeway networks. In this approach, a scenario-based min-max scheme is used to handle uncertainties. This scheme optimizes the worst case among a limited number of scenarios that are considered. The use of parameterized control laws allows us to reduce the computational burden of the robust control problem based on the multi-class METANET model w.r.t. conventional model predictive control. To assess the performance of the proposed approach, a simulation experiment is implemented, in which scenario-based RHPC is compared with nominal RHPC, standard control ignoring uncertainties, and standard control including uncertainties. Here, the standard control approaches refer to state feedback controllers (such as PI-ALINEA for ramp metering). A queue override scheme is included for extra comparison. The results show that nominal RHPC approaches and standard control ignoring uncertainties may lead to high queue length constraint violations, and including a queue override scheme in standard control may not reduce queue length constraint violations to a low level. Including uncertainties in standard control approaches can obviously reduce queue length constraint violations, but the performance improvements are minor. For the given case study, scenario-based RHPC performs best as it is capable of improving control performance without high queue length constraint violations.In contrast, in heterogeneous multi-class models, these differences are considered, and vehicles are divided into different classes, such as cars, trucks, vans, and so on. The density of vehicles can be maintained around a critical value corresponding to maximum flow with a single-class fundamental diagram for mixed traffic. However, for model predictive control (MPC), in which the objective function depends on the dynamics of the controlled system, a more accurate model implies better prediction of future characteristics of the controlled system; thus, the controller has better information for determining control inputs. In general, the heterogeneous multi-class models are more accurate than homogeneous sing-class models, without increasing the computing complexity significantly. Some researchers have investigated the advantages of multi-class models. Wong and Wong [5] extended the Lighthill-Whitham-Richards (LWR) model to a multi-class version and found that the multi-class LWR model can reproduce some traffic phenomena (e.g., two-capacity phenomenon, hysteresis phenomenon of phase transition and platoon dispersion) that the single-class case cannot reproduce. Schreiter et al.[6] developed a multi-class controller that rerouted the traffic class specifically and showed that a multi-class controller outperformed a single-class controller. Liu et al. [7] extended METANET to a multi-class version, in which each vehicle class is assumed to be limited within its assigned space of the road, subjecting to its own fundamental diagram. Note, however, t...

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Coordinated and integrated control of motorway networks via non-linear optimal control

Cited by 270 publications

References 15 publications

Model-based Control of Intelligent Traffic Networks

Model-based Control of Intelligent Traffic Networks

Modeling and Control of Traffic Flow

Robust receding horizon parameterized control for multi‐class freeway networks: A tractable scenario‐based approach

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