Optimal Coordinated Ramp Metering with Advanced Motorway Optimal Control

Kotsialos, Apostolos; Papageorgiou, Markos; Middelham, Frans

doi:10.3141/1748-07

Cited by 58 publications

(34 citation statements)

References 6 publications

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“…MPC is an optimal control method applied in a rolling horizon framework. Optimal control has successfully been applied in [29][30][31] to coordinate or integrate traffic control measures. Both optimal control and MPC have the advantage that the controller generates control signals that are optimal according to a user-supplied objective function.…”

Section: Motivationmentioning

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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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: Motivationmentioning

confidence: 99%

“…for x ∈ {CO, NO x , HC, fuel}, where E total x,m,i is defined by (31). In order to get smoother control signals, one often also imposes a penalty on the temporal variation of the control signal c:…”

Section: Objective Functionmentioning

confidence: 99%

Model-based Control of Intelligent Traffic Networks

Schutter

Hellendoorn

Hegyi

et al. 2009

Intelligent Infrastructures

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show abstract

“…Gini coefficient, proposed by Gini [9], is used to measure the equity performance of the motorway system, which can be expressed as follows:…”

Section: Performance Measuresmentioning

confidence: 99%

“…Kotsialos and Papageorgiou [9] observed a partially conflicting relationship between efficiency and equity, or in other words, a trade-off between the two. Zhang and Levinson [10] reported that the most efficient RM strategy is the least equitable one.…”

Section: Introductionmentioning

confidence: 99%

Efficiency and Equity Performance of a Coordinated Ramp Metering Algorithm

Ranjitkar

Zhao

2016

PROMET

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“…MPC is an optimal control scheme applied within a rolling horizon framework. Optimal control was successfully applied by Kotsialos et al [26] to coordinate or integrate traffic control measures. Both optimal control and MPC have the advantage of a controller that generates an optimal control signal or decision according to a user-defined objective function.…”

Section: Introductionmentioning

confidence: 99%

Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand

Bie

Seraj

Zhang

et al. 2018

Journal of Advanced Transportation

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Variable speed limit (VSL) is becoming recognized as an effective way to improve traffic throughput and road safety. In particular, methods based on traffic state prediction exhibit promising potential to prevent future traffic congestion and collisions. However, field observations indicate that the traffic state prediction model results in nonnegligible error that impacts the next step decision making of VSL. Thus, this paper investigates how to eliminate this prediction error within a VSL environment. In this study, the traffic state prediction model is a second-order traffic flow model named METANET, while the VSL control is model predictive control (MPC) based, and the VSL decision is discrete optimized choice. A simplified version of the switching mode stochastic cell transmission model (SCTM) is integrated with the METANET model to eliminate the prediction error. The performance of the proposed method is assessed using field data from a VSL pilot test in Edmonton, Canada, and is compared with the prediction results of the baseline METANET model during the road test. The results show that during the most congested period the proposed SCTM-METANET model significantly improves the prediction accuracy of regular METANET model.

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Optimal Coordinated Ramp Metering with Advanced Motorway Optimal Control

Cited by 58 publications

References 6 publications

Model-based Control of Intelligent Traffic Networks

Model-based Control of Intelligent Traffic Networks

Efficiency and Equity Performance of a Coordinated Ramp Metering Algorithm

Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand

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