在飓风疏散期间激活反流的机器学习

John W. Burris, Rahul Shrestha, B. Gautam, Bibidh Bista
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引用次数: 7

摘要

逆流是紧急疏散计划的重要组成部分。在大多数情况下,逆流车道反转将使关键疏散路线的通行能力增加一倍。在飓风威胁期间疏散路易斯安那州东南部的“反流”计划使用了一个典型的时间表,根据预测的登陆时间激活反流。这项工作将应用机器学习技术,使用实时交通数据来调度车流的激活。对Contraflow计划进行优化,应根据需求增加疏散交通流量,并保持进入交通的可用性,直到需要Contraflow车道,从而提高疏散计划的有效性。这些技术可以应用于其他地点,包括那些没有现有疏散计划的地点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning for the activation of contraflows during hurricane evacuation
Contraflows are a critical part of an emergency evacuation plan. In most cases, a contraflow lane reversal will double the capacity of key evacuation routes. The Contraflow plan for the evacuation of southeast Louisiana during a hurricane threat uses a typical schedule for the activation of contraflows based on the predicted time of landfall. This work will apply machine learning techniques using real-time traffic data to schedule the activation of contraflows. Optimizing the Contraflow plan should increase the effectiveness of the evacuation plan by increasing the flow of evacuation traffic based on demand and retaining the availability of incoming traffic until contraflow lanes are needed. These techniques could be applied to other locations, including those without an existing evacuation plan.
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