Freeway Traffic Management and Control

Hegyi, Andreas; Bellemans, Tom; Schutter, Bart De

doi:10.1007/978-0-387-30440-3_232

Cited by 18 publications

(3 citation statements)

References 88 publications

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“…to optimize and control the traffic flows in urban and freeway networks. For more information on traffic management the interested reader is referred to [1], [2], [3], [4], [5], [6].…”

Section: Traffic Managementmentioning

confidence: 99%

Model predictive traffic control for green mobility

Schutter

2014

2014 European Control Conference (ECC)

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This tutorial provides a short overview of modelbased predictive control approaches for traffic management using multiple performance criteria, including green mobility performance measures such as reduction of emissions and reduction of fuel consumption. In this paper we will briefly discuss the most important aspects of the topic, while also providing the interested reader with a non-exhaustive list of references that provide a more in-depth treatment of the subject.We start with a brief description of traffic management. Next, we discuss traffic flow and emission models, with an emphasis on macroscopic models, as they are well suited for on-line model-based predictive traffic control, highlighting the importance of the balance between accuracy and control performance. Next, we focus on the use of model-based predictive control (MPC) for coordinated control of various traffic control measures in small-scale freeway networks. Finally, we discuss various ways to improve the computation speed when solving the MPC optimization problem.

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“…to optimize and control the traffic flows in urban and freeway networks. For more information on traffic management the interested reader is referred to [1], [2], [3], [4], [5], [6].…”

Section: Traffic Managementmentioning

confidence: 99%

Model predictive traffic control for green mobility

Schutter

2014

2014 European Control Conference (ECC)

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“…Unforeseen or even planned infrastructural deviations may result in notable traffic slowdown or congested traffic conditions [1]. These may subsequently create significant challenges for traffic control systems which need to preserve the traffic continuity and ensure its safe operation.…”

Section: Introductionmentioning

confidence: 99%

Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

Kljaić

Pavković

Mlinarić

et al. 2021

PROMET

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This paper presents the design of a fuzzy logic-based traffic scheduling algorithm aimed at reducing traffic congestion for the case of partial obstruction of a bidirectional traffic lane. Such a problem is typically encountered in rail traffic and personal rapid transportation systems with predefined and fixed traffic corridors. The proposed proportional-derivative (PD) fuzzy control algorithm, serving as a traffic control automaton, alternately assigns adaptive green light periods to traffic coming from each direction. The proposed fuzzy logic-based traffic controller has been compared with the conventional traffic control automaton featuring fixed-durations of green light intervals. The comparison has been carried out within a simulation environment for four different probability distributions of stochastic traffic flows at each end of the considered traffic corridor. Results have shown that the proposed fuzzy logic-based traffic controller performance is far superior to that of the conventional traffic control law in terms of achieving shorter vehicle queue lengths and less disparity in queue lengths for all considered simulation scenarios.

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“…Jongwan Kim by developing a simulation model for discrete and 4-phase supervised intersections with the help of their own developed control algorithm, were determined of difference from fixed time signalization [4]. Desai and Somani (2014), Hegyi et al (2009), and Kuhne (1991), were determined different vehicle determination techniques based real-time sensor readings to help computer's vision at the solve of traffic congestion [5,6,7]. Some problems were emerged at the end of failure to provide effective traffic flow of conventional intersection systems.…”

Section: Introductionmentioning

confidence: 99%

Optimization Of Traffic Signalization For Complex Roundabout By Fuzzy Logic According To Various Parameters

Palandiz

Şenol

Bayrakçı

2019

International Journal of Computational and Experimental Science and Engineering

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In this study, traffic signalization by fuzzy logic according to number of vehicles, vehicle type, fuel and time parameters of 5 leg roundabouts were simulated in computer environment. For this purpose, city surveillance cameras (mobese) were used for the selected intersection. In the simulation, the Mamdani type fuzzy model was used. The results of the application are listed in tables. According to the vehicle density coming near the intersection, the transition times or stopping times applied to the intersection were optimized. Also, types of vehicles coming near the intersection are analyzed and vehicles with high fuel consumption are given priority. Thus, fuel consumption and environmental pollution can be reduced. The methods in this study are compared in terms of fuel saving, environmental effects and faster road flow. Accordingly, neutral fuel consumption with fuzzy logic method is averagely %15.9 less than classical method.

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Freeway Traffic Management and Control

Cited by 18 publications

References 88 publications

Model predictive traffic control for green mobility

Model predictive traffic control for green mobility

Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

Optimization Of Traffic Signalization For Complex Roundabout By Fuzzy Logic According To Various Parameters

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