Model Predictive Control for Multi-Class Traffic Flows

Deo, Puspita; Schutter, Bart De; Hegyi, Andreas

doi:10.3182/20090902-3-us-2007.0017

Cited by 22 publications

(10 citation statements)

References 14 publications

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“…The multi-class METANET model includes the heterogeneous nature of traffic flows [3] . More specifically, it is assumed that several different classes of vehicles are in the traffic network.…”

Section: B Multi-class Metanet Modelmentioning

confidence: 99%

“…In this paper a benchmark network [3,7] with two links is used as case study. The first link has four segments, and the second link has two segments.…”

Section: A Be N Chmark Net W Orkmentioning

confidence: 99%

“…Therefore, fast multi-class prediction models are needed for efficient traffic management. A few macroscopic multi-class traffic flow models have been proposed for use in traffic control such as the multi-class LW R model [1], the FASTLANE model [2] , and the multi-class METANET model [3]. The corresponding simulation results show that considering the heterogenous nature of traffic flow can significantly improve the performance of traffic network.…”

Section: Introductionmentioning

confidence: 99%

“…parameters are taken from[3,7, 10]. Two vehicle classes are adopted with the following parameters:Vfree,m,1 = 106.34 km/h, am,!…”

mentioning

confidence: 99%

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Multi-class traffic flow and emission control for freeway networks

Liu

Schutter

Hellendoorn

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Multi-class emission models aim at capturing the heterogenous nature of traffic flows. These models are helpful for improving the efficiency of the reduction of emissions. We develop a macroscopic emission model that takes into account the multi-class nature of traffic. This results in a integrated macroscopic multi-class traffic flow and emission model called the multi-class VT-Macro model. Model predictive control is adopted to reduce the total time spent and the total emissions. End point penalties are included in the objective function, taking into account the different distances to the destination of different vehicles. A case study based on a benchmark network is implemented. The simulation results show that better performance is obtained by using the multi-class VT-macro model and end point penalties.

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Section: B Multi-class Metanet Modelmentioning

confidence: 99%

“…In this paper a benchmark network [3,7] with two links is used as case study. The first link has four segments, and the second link has two segments.…”

Section: A Be N Chmark Net W Orkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…parameters are taken from[3,7, 10]. Two vehicle classes are adopted with the following parameters:Vfree,m,1 = 106.34 km/h, am,!…”

mentioning

confidence: 99%

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Multi-class traffic flow and emission control for freeway networks

Liu

Schutter

Hellendoorn

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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“…For freeway networks, MPC is used to coordinate ramp meters (6) or to integrate them with route guidance (5,12). Also, simulations with to control multi-class traffic flow have been performed, although with non-dynamic pce values (13).…”

Section: Multi-class Model-predictive Controlmentioning

confidence: 99%

Vehicle Class–Specific Route Guidance of Freeway Traffic by Model-Predictive Control

Schreiter

Landman

Lint

et al. 2012

Transportation Research Record: Journal of the Transportation R

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Few Active Traffic Management measures proposed in the past consider the distinction of different vehicle classes. Examples of vehicle-class specific measures are truck lanes and high-occupancy/toll (HOT) lanes. We propose that the distinction of different vehicle classes, with different flow characteristics and societal and economic function, can contribute to the effectiveness of traffic management measures.In this paper, we develop a multi-class controller that reroutes the traffic class-specifically dependent on the vehicle class. The vehicle-class specific properties such as vehicle length and value of time are used in a model-predictive control approach to optimize the total time spent and the economic costs. Experiments in a simple network with synthetic data show that a multi-class controller outperforms a single-class controller. We further show that the value of time and the incident strength have an influence on which vehicle class is rerouted by the multi-class controller.Schreiter, Landman, Van Lint, Hegyi, Hoogendoorn 1 FIGURE 1 Multi-class Traffic -Dynamic passenger-car equivalents (pce) of vehicle classes

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