基于智能体的疏散交通管理模型

Manini Madireddy, D. Medeiros, S. Kumara
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引用次数: 22

摘要

在本文中,我们建立了一个基于智能体的疏散模型,并用它来测试一种新的交通控制策略——节流。疏散代理从源头到目的地采取动态最短时间路径(总行程时间取决于到目的地的距离和拥堵程度)。节流包括在拥堵程度达到一个上限时暂时关闭一个路段,在拥堵程度低于一个下限时开放路段。实验通过使用小型测试网络和更现实的Sioux Falls网络,将节流获得的总疏散时间与基本情况(非节流)进行比较。我们发现节流显著提高了总疏散时间。为了进一步测试控制策略的有效性,我们将其与测试网络上的反流进行了比较,发现结果是可比较的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An agent based model for evacuation traffic management
In this paper we build an agent based evacuation model and use it to test a novel traffic control strategy called throttling. The evacuee agents travel from a source to a destination taking the dynamic shortest time path (total travel time depends on the distance to destination and the congestion level). Throttling involves closing a road segment temporarily when its congestion level reaches an upper threshold and opening it when congestion level falls below a lower threshold. Experimentation was performed by comparing the total evacuation time obtained with throttling to a base case (non-throttling) using a small test network and the more realistic Sioux Falls network. We found that throttling improves the total evacuation time significantly. To further test the effectiveness of our control strategy we compared it to contraflow on the test network and found the results to be comparable.
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