Development of an Evacuation Decision Support Tool: A Combined Optimization and Traffic Microsimulation Modeling Approach

This study develops a novel framework to formalize how to optimally use all available modes of transportation, particularly, auto, transit and school bus for mass evacuation. The study develops an “All Mode Evacuation Decision Support Tool (AMEDST)” to estimate an optimum composition of auto-bus mix that demonstrates an improvement in evacuation times and network congestion. The tool integrates two components: an “All Mode Allocation Module (AMAM)” and a traffic evacuation microsimulation model. The first module prioritizes evacuees for bus allocation accounting for vulnerabilities of populations. The study follows a Knapsack problem formulation to develop AMAM. A Dynamic Programming algorithm is used to solve the Knapsack optimization problem within a Python platform. Individuals not prioritized are assumed to be evacuated by personal vehicles. Traffic microsimulation model follows a dynamic traffic assignment process to simulate evacuation scenarios using all available modes. It informs AMAM the maximum number of individuals that can be evacuated by buses. The results suggest that individuals with higher level of vulnerabilities are prioritized for bus allocation within AMEDST. Moreover, time to allocate bus within an evacuation period is sporadic for a high bus demand. Results from the traffic simulation suggest that traffic congestion and evacuation times significantly decrease as reflected in vehicular traffic reduction of 3.9-7.7% and evacuation time reduction of 9-22.7% if 5-20% auto-based demand can be served by buses. The tool will help emergency personnel evaluate alternative scenarios regarding resource allocation and evacuation planning during extreme events that necessitate large-scale evacuations.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 20p

Subject/Index Terms

Filing Info

  • Accession Number: 01763676
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-00878
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:08AM