@article{ref1,
title="A dynamic programming optimization for traffic microsimulation modelling of a mass evacuation",
journal="Transportation research part D: transport and environment",
year="2021",
author="Alam, MD Jahedul and Habib, Muhammad Ahsanul",
volume="97",
number="",
pages="e102946-e102946",
abstract="This study develops a novel framework to formalize the optimal utilization of all available modes of transportation, particularly transit and school buses for a mass evacuation. The study develops an &quot;All-Mode Evacuation Decision Support Tool (AMEDST)&quot; to determine an optimum auto-bus composition that yields an improvement in evacuation time and network congestion. The study follows a Knapsack optimization and adopts a solution algorithm called Dynamic Programming within a Python platform to optimally allocate buses to evacuees exposed to higher level of vulnerabilities. A traffic microsimulation model follows a dynamic traffic assignment process to simulate evacuation scenarios using all available modes. <br><br>RESULTS from the traffic simulation yield a vehicular traffic reduction of 3.9-7.7% and an evacuation time reduction of 9-22.7% if 5-20% of auto-based demand are served by buses. The tool will help emergency personnel evaluate alternative scenarios for making informed decisions regarding resource allocation and emergency budget policies for large-scale evacuations.<p /> <p>Language: en</p>",
language="en",
issn="1361-9209",
doi="10.1016/j.trd.2021.102946",
url="http://dx.doi.org/10.1016/j.trd.2021.102946"
}