Agent-based transportation: Demand management demand effects of reserved parking space and priority lanes in comparison and combination

2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS) Pub Date : 2015-05-20 DOI:10.5220/0005411503170323

Markus C. Beutel, Sebastian Addicks, B. Zaunbrecher, S. Himmel, Karl-Heinz Krempels, M. Ziefle

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引用次数: 3

Abstract

Fostering the usage of alternative mobility modes, e.g., carsharing or carpooling becomes more and more urgent in modern urban planning. Politicians and city planners have already recognized that putting targeted incentives can influence people;s mobility behavior in an effective way. Agent-based simulations of transportation demand can be a valuable tool to support these planning processes. This work is based on a state-of-the-art transportation demand simulation and shows modeling and simulation modifications related with agents under the influence of incentives. These agents have been assessed in qualitative and quantitative studies prior to the simulation. Results show that agent-based simulation of transportation demand is suitable to evaluate impacts of transportation demand management measures. More specifically, all investigated measures show certain impacts on mobility mode choice, at which an incentive combination is most effective.

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基于agent的交通:需求管理:预留车位与优先车道对比与组合的需求效应

在现代城市规划中，鼓励使用其他出行方式，如拼车或拼车，变得越来越紧迫。政治家和城市规划者已经认识到，采取有针对性的激励措施可以有效地影响人们的出行行为。基于agent的交通需求模拟是支持这些规划过程的有价值的工具。这项工作基于最先进的运输需求模拟，并展示了在激励影响下与代理相关的建模和仿真修改。在模拟之前，这些药剂已经在定性和定量研究中进行了评估。结果表明，基于智能体的交通需求模拟可以很好地评价交通需求管理措施的影响。更具体地说，所有被调查的措施都显示出对移动方式选择的一定影响，其中激励组合最有效。

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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2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS)

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