Real-time congestion pricing strategies for toll facilities

Laval, Jorge A.; Cho, Hyun W.; Muñoz, Juan Carlos; Yin, Yafeng

doi:10.1016/j.trb.2014.09.015

Cited by 25 publications

(20 citation statements)

References 27 publications

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“…Note that t 2 is the moment when λ(t) initially exceeds µ 2 . Based on the optimality condition of Equation 3, as mentioned in Muñoz and Laval (2006) and Laval et al(2015), as long as the arrival rate is lower than µ 2 , there is no need for re-routing. Re-routing starts immediately when λ(t) > µ 2 (i.e., at t 2 ) and ends at T 1 .…”

Section: Review Of Muñoz and Laval's Methodsmentioning

confidence: 99%

“…where w r (t) is the queuing delay, which can be expressed as (see Arnott et al, 1990b;Laval et al, 2015):…”

Section: Problem Formulationmentioning

confidence: 99%

“…Until now, many methods have been proposed to reduce the traffic congestion in both static and dynamic cases, including (1) economic policies, e.g., congestion pricing (Pigou, 1920;Evans, 1992;Yang and Huang, 2005;Laval et al, 2015;Daganzo and Lehe, 2015); tradable credits (Yang and Wang, 2011;Akamatsu and Wada, 2017;Lahlou and Wynter, 2017); rewards (Rouwendal et al, 2012); transit subsidy (Parry and Small, 2009); parking pricing (D'Acierno et al, 2006); and (2) engineering control schemes, e.g., speed limits (Knoop et al, 2010;Yang et al, 2012;Chen and Ahn, 2015); ramp metering (Papageorgiou and Kotsialos, 2002); license plate rationing (Nie, 2017); lane control (Dahlgren, 1998;Daganzo and Cassidy, 2008;Fosgerau, 2011) and so on. Although the popular congestion pricing schemes have been implemented in some cities, there are still a long way toward eliminating the public's reluctance to accept tolls.…”

Section: Introductionmentioning

confidence: 99%

“…To our best knowledge, Muñoz and Laval (2006) are the first to introduce the method based on dynamic optimality conditions and calculus of varia-tions to draw the dynamic system optimum (DSO) allocation lines. Recently, Laval et al(2015) analyzed the effects of system optimum tolls on dynamic traffic assignment problem in a two-alternative network. As mentioned in the above two papers, the time intervals when the alternatives are used at capacity can be defined uniquely, but the allocation of the queues is not.…”

Section: Introductionmentioning

confidence: 99%

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Graphical solution for system optimum dynamic traffic assignment with day-based incentive routing strategies

Zhao

Leclercq

2018

Transportation Research Part B: Methodological

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This paper analyzes the dynamic traffic assignment problem on a three-alternative network with day-based incentive routing strategies by using graphical solution method. It is assumed that the cumulative count curve of vehicles is known and that the arrival rate is unimodal. The dynamic system optimum (DSO) allocation lines are first drawn based on calculus of variations. Three possible optimal allocation lines are analyzed. A day-based incentive routing strategy is designed and conditions that when and how to implement the incentive scheme to realize DSO are then derived. Extension to general parallel networks is also given. Examples are presented to demonstrate the effectiveness of the scheme.

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Section: Review Of Muñoz and Laval's Methodsmentioning

confidence: 99%

“…where w r (t) is the queuing delay, which can be expressed as (see Arnott et al, 1990b;Laval et al, 2015):…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Graphical solution for system optimum dynamic traffic assignment with day-based incentive routing strategies

Zhao

Leclercq

2018

Transportation Research Part B: Methodological

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“…The upper level problem is to investigate the desired objective from the manager perspective, in which various purposes may be included within the previous literatures, such as minimizing the total travel time [31,32]. In the simulation experiment, the total travel time can be formulated as the integral of the number of vehicles (vehicle-hours) in the two-layer network as follows:…”

Section: The Upper Level Modelmentioning

confidence: 99%

A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram

Wei

Sun

2018

Journal of Advanced Transportation

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Dynamic congestion pricing has attracted increasing attentions during the recent years. Nevertheless, limited research has been conducted to address the dynamic tolling scheme at the network level, such as to cooperatively manage two alternative networks with heterogeneous properties, e.g., the two-layer network consisting of both expressway and arterial network in the urban areas. Recently, the macroscopic fundamental diagram (MFD) developed by both field experiments and simulation tests illustrates a unimodal low-scatter relationship between the mean flow and density network widely, providing the network traffic state is roughly homogeneous. It reveals traffic flow properties at an aggregated level and sheds light on dynamic traffic management of a large network. This paper proposes a bilevel programming toll model, incorporating MFD to solve the unbalanced flow distribution problem within the two-layer transportation networks. The upper level model aims at minimizing the total travel time, while the lower level focuses on the MFD-based traffic assignment, which extends the link-based traffic assignment to network wide level. Genetic algorithm (GA) and the method of successive average were adopted for solving the proposed model, on which an online experimental platform was established using VISSIM, MATLAB, and Visual Studio software packages. The results of numerical studies demonstrate that the total travel time is decreased by imposing the dynamic toll, while the total travel time savings significantly outweigh the toll paid. Consequently, the proposed dynamic toll scheme is believed to be effective from both traffic and economic points of view.

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