Solving the minimum toll revenue problem in real transportation networks

Shirazi, Mohammadali; Aashtiani, Hedayat Z.

doi:10.1007/s11590-014-0817-8

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Bai et al ( 31 ) adopt a node-arc valid toll set formulation and use the Dantzig-Wolfe decomposition method. Shirazi and Aashtiani ( 49 ) and Shirazi et al ( 12 ) adopt an arc-route valid toll set formulation and propose, respectively, a route generation algorithm and a dynamic penalty function approach.…”

Section: Solution Algorithmsmentioning

confidence: 99%

Pricing of Connected and Autonomous Vehicles in Mixed-Traffic Networks

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2021

Transportation Research Record

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For networks with human-driven vehicles (HDVs) only, pricing with arc-specific tolls has been proposed to achieve minimization of travel times in a decentralized way. However, the policy is hardly feasible from a technical viewpoint without connectivity. Therefore, for networks with mixed traffic of HDVs and connected and autonomous vehicles (CAVs), this paper considers pricing in a scenario where only CAVs are charged. In contrast to HDVs, CAVs can be managed as individual vehicles or as a fleet. In the latter case, CAVs can be routed to minimize the travel time of the fleet of CAVs or that of the entire fleet of HDVs and CAVs. We have a selfish user behavior in the first case, a private monopolist behavior in the second, a social planner behavior in the third. Pricing achieves in a decentralized way the social planner optimum. Tolls are not unique and can take both positive and negative values. Marginal cost pricing is one solution. The valid toll set is provided, and tolls are then computed according to two schemes: one with positive tolls only and minimum toll expenditure, and one with both tolls and subsidies and zero net expenditure. Convergent algorithms are used for the mixed-behavior equilibrium (simplicial decomposition algorithm) and toll determination (cutting plane algorithm). The computational experience with three networks: a two-arc network representative of the classic town bypass case, the Nguyen-Dupuis network, and the Anaheim network, provides useful policy insight.

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Section: Solution Algorithmsmentioning

confidence: 99%

Pricing of Connected and Autonomous Vehicles in Mixed-Traffic Networks

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2021

Transportation Research Record

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“…Congestion pricing has been recognized as an efficient approach to alleviate traffic congestion and improve social welfare [1][2][3][4]. e theoretical background of congestion pricing is based on the fundamental economic principle of marginal-cost pricing, which states that travelers using congested roads should pay a toll equal to the difference between the marginal social cost and the marginal private cost [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Trial‐and‐Error Congestion Pricing Method for Day‐to‐Day Dynamic Network Flows considering Travelers’ Heterogeneous Inertia Patterns

Zhou

Zhang

2019

Mathematical Problems in Engineering

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is study proposes a trial-and-error congestion pricing method to achieve system optimum under day-to-day flow dynamics with unknown demand. Travelers are assumed to adjust their route choice day by day so that the resultant traffic flow under a trial of tolls evolves from one day to another. We rigorously demonstrate that if psychological inertia is considered in travelers' day-today route choice behavior, the convergence of the proposed trial-and-error congestion pricing method can be guaranteed without requiring the observed network flows to be in user equilibrium. Furthermore, the proposed method also allows tolls to be updated at irregular time intervals, which greatly relaxes the implementation requirements of existing congestion pricing schemes in the literature. is study is very significant from a practical point of view because it provides a flexibility approach that greatly reduces the implementation time of the traditional trial-and-error congestion pricing method. Numerical experiments are conducted to validate our theoretical findings.

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“…In recent studies, it has been recognized that the structure of the NCP model lends itself well to many efficient solution methods in large-scale problems. As a result, NCP assignment models of auto or transit mode have received more attention in various applications, for example, O-D demand estimation ( 18 ), network design problems ( 19 – 21 ), facility location problems ( 22 , 23 ), and congestion toll pricing ( 24 ). In spite of these efforts, however, there is still a gap in the literature on extending the NCP model to a multi-modal setting with a transit mode.…”

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confidence: 99%

Complementarity Formulation and Solution Algorithm for Auto-Transit Assignment Problem

Zarrinmehr

Aashtiani

Nie

et al. 2019

Transportation Research Record

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In this paper, a combined model of auto-transit assignment is introduced based on two complementarity formulations in the literature. The model accounts for interactions between the auto and transit modes through non-separable asymmetric demand and cost functions. A path-based solution algorithm is presented based on the three ideas of decomposition, column generation, and linearization, which have proved to be effective in tackling large-size networks. Numerical results over the Chicago sketch network suggest that the algorithm converges quickly within the first iterations, but is less effective as the solution gets closer to the neighborhood of the equilibrium solution. The sluggish convergence behavior is attributed to the difficulty of searching the space of strategy-based transit assignment model.

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Solving the minimum toll revenue problem in real transportation networks

Cited by 4 publications

References 12 publications

Pricing of Connected and Autonomous Vehicles in Mixed-Traffic Networks

Pricing of Connected and Autonomous Vehicles in Mixed-Traffic Networks

A Trial‐and‐Error Congestion Pricing Method for Day‐to‐Day Dynamic Network Flows considering Travelers’ Heterogeneous Inertia Patterns

Complementarity Formulation and Solution Algorithm for Auto-Transit Assignment Problem

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