Abstract

The Multimodal Transportation Educational Virtual Appliance (MTEVA) is an application developed within the framework of the broader Coastal Science Educational Virtual Appliance (CSEVA) to enhance coastal resiliency through the integration of coastal science and transportation congestion models for emergency situations. The first generation MTEVA enabled users to perform and visualize simulations using an integrated storm surge and inundation model (CH3D-SSMS) and transportation evacuation/return modeling system that supports contraflow in a simple synthetic domain (order of tens of intersections/roads) under tropical storm conditions. In this study, the second generation MTEVA has been advanced to apply storm surge and evacuation models to the greater Jacksonville area of Northeast Florida (order tens of thousands of transportation intersections/roads). To support solving the evacuation problem with a significantly larger transportation network, new models have been developed, including a heuristic capable of efficiently solving large-scale problems. After initial testing on several smaller stand-alone transportation networks (e.g., Anaheim, Winnipeg), the heuristic is applied to the Jacksonville area transportation network. Results presented show the heuristic produces a nearly optimal (average optimality gap <0.5%) solution in 90% less wall clock time than needed by the exact solver. The MTEVA’s new capabilities are then demonstrated through the simulation of a Hurricane Katrina-sized storm impacting the region and studying how the evacuation patterns are affected by the closing of roads due to flooding and bridges due to high winds. To ensure residents are able to leave the area, evacuations are shown to need to have begun at least 36 h prior to landfall. Additionally it was shown that large numbers of residents would be left behind if evacuation does not begin within 18 h of landfall and ~97% would not escape if evacuation did not begin until landfall, when areas of the coast that are the most prone to flooding are already cut off from the “safe” nodes of the transportation network.

Highlights

  • Hurricanes, earthquakes, industrial accidents, terrorist attacks and other such emergency situations pose great dangers to lives and property

  • The Multimodal Transportation Educational Virtual Appliance (MTEVA) is an application developed within the framework of the broader Coastal Science Educational Virtual Appliance (CSEVA)

  • We present the results of a computational study using the MTEVA to show how the heuristic performs on both synthetic and real transportation networks and show the heuristics feasibility for potential use in real-time evacuation planning

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Summary

Introduction

Hurricanes, earthquakes, industrial accidents, terrorist attacks and other such emergency situations pose great dangers to lives and property. Efficient evacuation during these events is one way to increase safety and avoid escalation of damages. Optimality here can be defined in different ways: number of evacuees that reach safety, smallest overall time to safety, average time to safety. In this effort, we aim to maximize the number of people that are safely evacuated to one of the nodes in set S

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