基于效用的多式联运行为代理模型:以里尔城市收费为例

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Intelligence Pub Date : 2025-01-08 DOI:10.1007/s10489-024-05869-1
Azise Oumar Diallo, Guillaume Lozenguez, Arnaud Doniec, René Mandiau
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引用次数: 0

摘要

为了减少城市的拥堵和污染,政府当局鼓励从私家车转向可持续的出行方式,如多式联运(通过私家车和公共交通的结合)。强制性决策,如城市通行费,也是一种越来越被研究的解决方案。为了避免通行费的成本,代理商因此选择多式联运方式(私家车和公共交通),将车辆停放在收费区域入口处的停车换乘设施中。本文提出了一种基于主体模型(ABM)的方法,特别是基于效用的主体模型(utility-based agent),用于再现城市中多式联运出行行为并评估城市收费的影响。在这种情况下,我们将重点放在多项logit模型上,并使用agent-and-activity仿真工具MATSim来确定每个agent的模态选择。基于开放数据(针对MEL的欧洲大都市里尔),仿真结果表明,通行费税率为\(\varvec b{20}\)€(\(\varvec{21.75}\) $)足以使私家车使用量减少\(\varvec{20}\%\)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Utility-based agent model for intermodal behaviors: a case study for urban toll in Lille

To reduce the congestion and pollution in urban cities, the political authorities encourage the modal shift from private cars in favor of sustainable trip behaviors such as intermodality (through combinations of private cars and public transport). Coercive decisions such as urban tolls are also an increasingly investigated solution. To avoid the cost of toll taxes, agents thus select intermodal transportation modes (private cars and public transport) by parking their vehicles in park-and-ride (PR) facilities at the entrance to the area toll. This paper proposes a methodology for an agent-based model (ABM), particularly a model called utility-based agent, to reproduce intermodal trip behaviors in a city and to assess the impact of an urban toll. In this context, we focus on multinomial logit models, coupled with the agent-and-activity simulation tool MATSim, is used to determine the modal choice for each agent. Based on open data (for European Metropolis of Lille, MEL), the simulation shows that \(\varvec{20}\) € (\(\varvec{21.75}\) $) of toll tax is sufficient to reduce by \(\varvec{20}\%\) the use of private vehicles.

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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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