Mass evacuations, particularly those at a statewide level, represent the largest single-event traffic movements that exist. These complex events can last several days, cover thousands of miles of roadway, and include hundreds of thousands of people and vehicles. Often, they are also marked by enormous delay and congestion and are nearly always criticized for their inefficiency and lack of management. However, there are no standardized methods by which to systematically quantify traffic characteristics at the proper scale. This paper describes research to develop and apply an analytical method to measure and describe statewide mass-evacuations in a practical, cost-effective manner. The research methods are based on simple, yet widely available, and easily understood traffic count datasets that support both qualitative and quantitative analyses. By spatially and temporally arranging sensor-based statewide traffic volume data from Hurricane Irma (2017), Michael (2018), Tubbs Fire (2017), and Thomas Fire (2018) evacuations, these methods were able to describe and address several key questions about these events. The methods described herein estimate the start and end of the auto-based evacuation, the loading and peaking characteristics of traffic, and the total number of vehicles used in the evacuation process, as well as the effective start and end of the auto-based reentry. Among the key findings of this work were that the Hurricane Irma and Michael evacuations began several days before landfall, peaking two to three days prior to the storm, suggesting a heightened perception of risk; and that the Thomas and Tubbs fire evacuations traffic were impacted by subsequent fires nearby. It is expected that state departments of transportation and emergency management officials would be able to reproduce the procedure presented here to analyze future evacuations.
","abstract_html":"<p>Mass evacuations, particularly those at a statewide level, represent the largest single-event traffic movements that exist. These complex events can last several days, cover thousands of miles of roadway, and include hundreds of thousands of people and vehicles. Often, they are also marked by enormous delay and congestion and are nearly always criticized for their inefficiency and lack of management. However, there are no standardized methods by which to systematically quantify traffic characteristics at the proper scale. This paper describes research to develop and apply an analytical method to measure and describe statewide mass-evacuations in a practical, cost-effective manner. The research methods are based on simple, yet widely available, and easily understood traffic count datasets that support both qualitative and quantitative analyses. By spatially and temporally arranging sensor-based statewide traffic volume data from Hurricane Irma (2017), Michael (2018), Tubbs Fire (2017), and Thomas Fire (2018) evacuations, these methods were able to describe and address several key questions about these events. The methods described herein estimate the start and end of the auto-based evacuation, the loading and peaking characteristics of traffic, and the total number of vehicles used in the evacuation process, as well as the effective start and end of the auto-based reentry. Among the key findings of this work were that the Hurricane Irma and Michael evacuations began several days before landfall, peaking two to three days prior to the storm, suggesting a heightened perception of risk; and that the Thomas and Tubbs fire evacuations traffic were impacted by subsequent fires nearby. It is expected that state departments of transportation and emergency management officials would be able to reproduce the procedure presented here to analyze future evacuations.</p>","abstract_has_math":false,"creators":["Loreto, Lorraine Margot Acevedo"],"institution":null,"degree_name":"Master of Science in Civil Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-10-01T07:00:00Z","date_published":"2019-10-01T07:00:00Z","updated_at":"2026-07-27T19:25:37Z","subjects":["temporal-spatial","emergency evacuation","traffic","Civil Engineering","Transportation Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/481","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Loreto, Lorraine Margot Acevedo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Civil Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["temporal-spatial","emergency evacuation","traffic","Civil Engineering","Transportation Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/481"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Mass evacuations, particularly those at a statewide level, represent the largest single-event traffic movements that exist. These complex events can last several days, cover thousands of miles of roadway, and include hundreds of thousands of people and vehicles. Often, they are also marked by enormous delay and congestion and are nearly always criticized for their inefficiency and lack of management. However, there are no standardized methods by which to systematically quantify traffic characteristics at the proper scale. This paper describes research to develop and apply an analytical method to measure and describe statewide mass-evacuations in a practical, cost-effective manner. The research methods are based on simple, yet widely available, and easily understood traffic count datasets that support both qualitative and quantitative analyses. By spatially and temporally arranging sensor-based statewide traffic volume data from Hurricane Irma (2017), Michael (2018), Tubbs Fire (2017), and Thomas Fire (2018) evacuations, these methods were able to describe and address several key questions about these events. The methods described herein estimate the start and end of the auto-based evacuation, the loading and peaking characteristics of traffic, and the total number of vehicles used in the evacuation process, as well as the effective start and end of the auto-based reentry. Among the key findings of this work were that the Hurricane Irma and Michael evacuations began several days before landfall, peaking two to three days prior to the storm, suggesting a heightened perception of risk; and that the Thomas and Tubbs fire evacuations traffic were impacted by subsequent fires nearby. It is expected that state departments of transportation and emergency management officials would be able to reproduce the procedure presented here to analyze future evacuations.
"]},{"key":"dc:title","label":"Title","values":["Temporal-Spatial Analysis of Emergency Evacuation Traffic"]}]}],"canonical_facts":{"dc:creator":["Loreto, Lorraine Margot Acevedo"],"dc:description.abstract":["Mass evacuations, particularly those at a statewide level, represent the largest single-event traffic movements that exist. These complex events can last several days, cover thousands of miles of roadway, and include hundreds of thousands of people and vehicles. Often, they are also marked by enormous delay and congestion and are nearly always criticized for their inefficiency and lack of management. However, there are no standardized methods by which to systematically quantify traffic characteristics at the proper scale. This paper describes research to develop and apply an analytical method to measure and describe statewide mass-evacuations in a practical, cost-effective manner. The research methods are based on simple, yet widely available, and easily understood traffic count datasets that support both qualitative and quantitative analyses. By spatially and temporally arranging sensor-based statewide traffic volume data from Hurricane Irma (2017), Michael (2018), Tubbs Fire (2017), and Thomas Fire (2018) evacuations, these methods were able to describe and address several key questions about these events. The methods described herein estimate the start and end of the auto-based evacuation, the loading and peaking characteristics of traffic, and the total number of vehicles used in the evacuation process, as well as the effective start and end of the auto-based reentry. Among the key findings of this work were that the Hurricane Irma and Michael evacuations began several days before landfall, peaking two to three days prior to the storm, suggesting a heightened perception of risk; and that the Thomas and Tubbs fire evacuations traffic were impacted by subsequent fires nearby. It is expected that state departments of transportation and emergency management officials would be able to reproduce the procedure presented here to analyze future evacuations.
"],"dc:identifier":["https://commons.erau.edu/edt/481"],"dc:subject":["temporal-spatial","emergency evacuation","traffic","Civil Engineering","Transportation Engineering"],"dc:title":["Temporal-Spatial Analysis of Emergency Evacuation Traffic"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Civil Engineering"]},"updated_at":"2026-07-27T19:25:37Z"}