A decentralized control strategy for freeway regulation

Goldstein, Nahum Ben-Zion; Kumar, K. S. P.

doi:10.1016/0191-2615(82)90012-1

Cited by 59 publications

(13 citation statements)

References 6 publications

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“…Wattleworth and Berry, 1965;Yuan and Kreer, 1971;Wang and May 1973;Chen and Cruz, 1974) or on automatic control theory (e.g. Isaksen and Payne, 1973;Goldstein and Kumar, 1982;Papageorgiou et al, 1990Papageorgiou et al, , 1991Zhang et al, 1994). Popular examples of the latter case are the Time-of-Day control method initiated by Wattleworth and Berry (Wattleworth and Berry, 1965) and the Linear Quadratic feedback control method initiated by Payne et al (Payne et al, 1973).…”

Section: Introductionmentioning

confidence: 99%

Some general results on the optimal ramp control problem

Zhang

Ritchie

Recker

1996

Transportation Research Part C: Emerging Technologies

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Abstrac&--In an effort to relieve peak hour congestion on freeways, various ramp metering algorithms have been employed to regulate the inputs to freeways from entry ramps. In this paper, we consider a freeway system comprised of a freeway section and its entry/exit ramps, and formulate the ramp control problem as a dynamic optimal process to minimize the total time spent in this system. Within this framework, we are able to show when ramp metering is beneficial to the system in terms of total time savings, and when it is not, under the restriction that the controlled freeway has to serve all of its ramp demand, and the traffic flow process follows the rules prescribed by the LWR theory with a triangular flowdensity relationship. We also provide solution techniques to the problem and present some preliminary numerical results and empirical validation.

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Section: Introductionmentioning

confidence: 99%

Some general results on the optimal ramp control problem

Zhang

Ritchie

Recker

1996

Transportation Research Part C: Emerging Technologies

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“…The latter is proved to be more effective in combatting freeway congestion than the former. Typical algorithms of this kind include demand-capacity, occupancy control [3], ALINEA [4], and linear quadratic regulation (LQR) [5]. Since these metering algorithms only provide linear control laws, they may not be able to control the ramp system which is known as a nonlinear plant.…”

Section: Introductionmentioning

confidence: 99%

Freeway Ramp Metering Control Based on Neural Dynamic Optimization

2006

2006 Chinese Control Conference

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In this paper, we consider the optimal metering control problem for a local freeway ramp. The traffic model is formulated as one with stochastic and nonlinear properties. The control objective is to maintain the freeway operated at a desired traffic density and to diminish the queue length on the ramp as possible. Such a nonlinear stochastic optimal control problem is known hard to solve exactly, then we concentrate on providing approximate solution. Our approach consists of two steps: the first is to solve a finite-horizon optimal control problem based on neural dynamic optimization; the second is to adapt the finite-horizon optimal controller to work for the infinite-horizon case by using the recedinghorizon control mechanism. We choose the multilayer feedforward neural network as the approximating structure, and the stochastic steepest descent method for the training. Extensive simulation studies demonstrate the efficiency of our approach.

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“…The coordinated ramp metering strategies can be divided into multivariable ramp metering strategies and optimal control strategies. The multivariable strategies, see for example [41,85], make use of measurements from a larger area and try to coordinate the traffic flow based on all on-ramps included in the area. These algorithms are based on linear equations of the traffic flow models and might therefore be inefficient during congestion.…”

Section: Traffic Control On Motorwaysmentioning

confidence: 99%

Traffic Management for Smart Cities

Allström

Barceló

Ekström

et al. 2016

Designing, Developing, and Facilitating Smart Cities

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Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.

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A decentralized control strategy for freeway regulation

Cited by 59 publications

References 6 publications

Some general results on the optimal ramp control problem

Some general results on the optimal ramp control problem

Freeway Ramp Metering Control Based on Neural Dynamic Optimization

Traffic Management for Smart Cities

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