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Real-time Recommendations for Traffic Control in an Intelligent Transportation System (ITS) During an Emergency Evacuation: Phase II Studies
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2022-06-01
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[PDF-3.80 MB]
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Alternative Title:Real-time Recommendations for Traffic Control in an Its System During an Emergency Evacuation [Project Title from Cover]
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Edition:Final Report May 2020 – August 2021
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Abstract:Recent hurricanes caused mass evacuations and brought the public attention to the issues occurring during these mass evacuations. To address these issues, the project team has investigated significant cues for hurricane evacuation planners’ decisions, built the Rule-based Lens Model (RLM) using Monte-Carlo simulation and supervised machine learning (SML) algorithms to analyze decision-making behavior during a hurricane evacuation, and developed an agent-based hurricane evacuation simulation model using the MATSim framework to estimate evacuation traffic volumes and patterns under different scenarios. Our results show that one cue (wind speed) contributes to evacuation planners’ decisions, and the percentage of families choosing shelter-in-place slightly affects evacuation traffic. We have also proposed and tested the quantitative methods to quantify a hurricane disruption to the US airport network, to reroute disrupted flights, and to recommend multimodal routes for affected passengers. Our results show that the proposed methods can identify the airports to be disrupted by an approaching hurricane and feasible airports for flight rerouting, and suggest hiring buses to transfer passengers affected by a hurricane. These models, methods and findings can support the deployment of an effective evacuation, rescheduling of airline flights and passengers affected, and improve the mobility of the people during a hurricane.
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