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 contrafl...

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Bibliographic Details
Published in2015 IEEE Global Humanitarian Technology Conference (GHTC) pp. 254 - 258
Main Authors Burris, John W., Shrestha, Rahul, Gautam, Bibek, Bista, Bibidh
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2015
Subjects
Algorithm design and analysis
Classification algorithms
Delays
disaster management
Hurricanes
machine learning
Machine learning algorithms
Prediction algorithms
Supervised learning
traffic control
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Summary: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.
DOI:10.1109/GHTC.2015.7343981