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Determining Optimum Toll Charges for Freight Vehicles Considering Multi-Stakeholder Objectives in Urban Conditions
| 题名： | Determining Optimum Toll Charges for Freight Vehicles Considering Multi-Stakeholder Objectives in Urban Conditions |
| 正文语种： | eng |
| 作者： | Loshaka Perera;Russell G. Thompson;Wenyan Wu |
| 作者单位： | Corresponding Author University of Melbourne;University of Melbourne;University of Melbourne |
| 关键词： | Optimum tolls; trucks; externalities; tradeoffs; NSGA II; Pareto optimum |
| 摘要： | Trucks generate more externalities (environmental and social) than passenger vehicles, especially when trucks divert off freeways. When toll charges increase, such as the significant recent rise in Melbourne, Australia, more trucks tend to avoid toll roads (quality roads), generating more externalities. This diversion adds substantial negative impacts on residents, the environment, and society. In fact, determining an optimum toll charge for freight vehicles is a crucial decision to be made by policymakers considering socioeconomic aspects. The objective of this study is to develop an approach to design an optimal toll pricing scheme for multiclass vehicles, including specific truck types, considering both direct costs and externalities. Additionally, the study developed an approach to identify the tradeoffs between various objectives of the designed scheme considering given constraints. Nonlinear programming and user equilibrium techniques are used to model the problem, and the costs (direct costs and externalities) are quantified for Victoria, Australia. This nondeterministic polynomial-time hard (NP-hard), nonconvex problem with nonlinear constraints was solved using the nondominated sorting genetic algorithm (NSGA) II. The model was applied to both a small-sized hypothetical network and a real network, with static demand conditions to illustrate differences between common toll schemes. Results are presented for Pareto-optimal solutions. |
| 出版年： | 2021 |
| 期刊名称： | Transportation Journal |
| 卷： | 60 |
| 期： | 2 |
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