Model predictive control for ramp metering of motorway traffic: A case study

Bellemans, Tom; Schutter, Bart De; Moor, Bart De

doi:10.1016/j.conengprac.2005.03.010

Cited by 188 publications

(95 citation statements)

References 8 publications

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“…The many advantages that MPC offers are also relevant for traffic control. In fact, MPC has already been extended to conventional roadside-based non-IV traffic management [13], [14]. However, MPC can also be used for traffic management and control with intelligent vehicles.…”

Section: B Iv-based Mpcmentioning

confidence: 99%

Model-based predictive traffic control for intelligent vehicles: Dynamic speed limits and dynamic lane allocation

Baskar

Schutter

Hellendoorn

2008

2008 IEEE Intelligent Vehicles Symposium

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Abstract-We consider traffic management and control approaches for automated highway systems with a combination of intelligent vehicles and roadside controllers. The vehicles are organized in platoons with short intraplatoon distances, and larger distances between platoons. Moreover, all vehicles are assumed to be automated, i.e., throttle, braking, and steering commands are determined by an automated on-board controller. Within a platoon the vehicles coordinate their actions so as to maintain a small but safe intervehicle distance, using adaptive cruise control methods. Platoon leaders receive speed set-points and lane change commands from the roadside controller. We propose a model-based predictive control (MPC) approach to determine appropriate speed limits and lane allocations for the platoons. In general, this results in mixed-integer optimization problems. We discuss some methods to solve these problems suboptimally on-line. The proposed approach is then applied to a simple simulation example in which the aim is to minimize the total time all vehicles spend in the network by optimally assigning dynamic speed limits and lane changes.

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Section: B Iv-based Mpcmentioning

confidence: 99%

Model-based predictive traffic control for intelligent vehicles: Dynamic speed limits and dynamic lane allocation

Baskar

Schutter

Hellendoorn

2008

2008 IEEE Intelligent Vehicles Symposium

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“…ramp metering of freeway networks in Bellemans et al (2006), variable speed limits and route guidance for freeway networks in Kotsialos et al (2002) and Hegyi et al (2005a,b), signal control for large-scale urban networks in Gartner et al (2002), Aboudolas et al (2010), and Lin et al (2011), and mixed urban and freeway networks in van den Berg et al (2007). A historical survey for industrial applications (other than traffic control) of MPC can be found in Qin and Badgwell (2003), while theoretical issues of MPC can be found in Garcia et al (1989), Camacho and Bordons (1999), and Mayne et al (2000).…”

Section: Solution Approach -An Mpc Controllermentioning

confidence: 99%

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

Haddad

Ramezani

Geroliminis

2013

Transportation Research Part B: Methodological

234

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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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“…MPC for ramp metering and for conventional road-side based non-IV traffic management has been developed and implemented in [9,42,43]. Similar models as used for optimal control approach are considered.…”

Section: Control Application For Conventional Trafficmentioning

confidence: 99%