{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/137552"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/137552","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Modeling Human Movement in the Wilderness: Navigating Lost Person Behavior and Searcher Response","abstract":"Search and rescue (SAR) outcomes hinge on two behavioral uncertainties: how lost people move through complex landscapes and how ground teams search those landscapes. This dissertation develops a unified, data-driven view of both. First, we present a dynamic, agent-based model of lost person behavior that adapts empirically observed reorientation strategies (e.g., trail-following, staying put, backtracking), integrates map layers of terrain-specific features (e.g., roads, streams, powerline easements), and is validated against real incident data. Because navigation is so heavily-dependent on the landscape, we further extend the model to include more landscape-dependent behaviors (like following elevation contours), explicit spatial and temporal decision points, and direct comparisons to observed tracks. Through simulation of all possible behavior distributions, we find an average behavioral profile for a hiker in the wilderness that can be adapted to accommodate other categories of lost people in different landscapes and locations. Recognizing that the lost person is only part of the SAR operational equation, we then turn to the searchers themselves by quantifying human searcher movement and coordination using GPS logs from real missions. In analyzing elevation effects on speed, differences in search tactics, and coupled coordination within search teams, we gain a better understanding of searcher dynamics with actionable metrics that can be used to test and refine current SAR assumptions. The overarching contribution is an empirically grounded foundation that links lost person movement and searcher performance, bridging operational practice with formal search theory.","abstract_html":"Search and rescue (SAR) outcomes hinge on two behavioral uncertainties: how lost people move through complex landscapes and how ground teams search those landscapes. This dissertation develops a unified, data-driven view of both. First, we present a dynamic, agent-based model of lost person behavior that adapts empirically observed reorientation strategies (e.g., trail-following, staying put, backtracking), integrates map layers of terrain-specific features (e.g., roads, streams, powerline easements), and is validated against real incident data. Because navigation is so heavily-dependent on the landscape, we further extend the model to include more landscape-dependent behaviors (like following elevation contours), explicit spatial and temporal decision points, and direct comparisons to observed tracks. Through simulation of all possible behavior distributions, we find an average behavioral profile for a hiker in the wilderness that can be adapted to accommodate other categories of lost people in different landscapes and locations. Recognizing that the lost person is only part of the SAR operational equation, we then turn to the searchers themselves by quantifying human searcher movement and coordination using GPS logs from real missions. In analyzing elevation effects on speed, differences in search tactics, and coupled coordination within search teams, we gain a better understanding of searcher dynamics with actionable metrics that can be used to test and refine current SAR assumptions. The overarching contribution is an empirically grounded foundation that links lost person movement and searcher performance, bridging operational practice with formal search theory.","abstract_has_math":false,"creators":["Hashimoto, Amanda Hana"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Engineering Mechanics","degree_department":"Engineering Science and Mechanics","school":null,"contributors":[],"advisors":[],"committee_chairs":["Abaid, Nicole"],"committee_members":["Childs, Lauren M.","Ross, Shane D.","Socha, John J.","Williams, Ryan K."],"year":2025,"date_issued":"2025-08-20","date_published":"2025-08-20","updated_at":"2026-07-22T22:19:16Z","subjects":["Agent-based modeling","Decision points","Lost person behavior","Search and rescue"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44577"],"render_values":[{"text":"vt_gsexam:44577","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/137552","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Abaid, Nicole"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Childs, Lauren M.","Ross, Shane D.","Socha, John J.","Williams, Ryan K."]},{"key":"dc:contributor.department","label":"Department","values":["Engineering Science and Mechanics"]},{"key":"dc:creator","label":"Author","values":["Hashimoto, Amanda Hana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-08-21T08:01:02Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-21T08:01:02Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08-20"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Mechanics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Agent-based modeling","Decision points","Lost person behavior","Search and rescue"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44577"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/137552"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Search and rescue (SAR) outcomes hinge on two behavioral uncertainties: how lost people move through complex landscapes and how ground teams search those landscapes. This dissertation develops a unified, data-driven view of both. First, we present a dynamic, agent-based model of lost person behavior that adapts empirically observed reorientation strategies (e.g., trail-following, staying put, backtracking), integrates map layers of terrain-specific features (e.g., roads, streams, powerline easements), and is validated against real incident data. Because navigation is so heavily-dependent on the landscape, we further extend the model to include more landscape-dependent behaviors (like following elevation contours), explicit spatial and temporal decision points, and direct comparisons to observed tracks. Through simulation of all possible behavior distributions, we find an average behavioral profile for a hiker in the wilderness that can be adapted to accommodate other categories of lost people in different landscapes and locations. Recognizing that the lost person is only part of the SAR operational equation, we then turn to the searchers themselves by quantifying human searcher movement and coordination using GPS logs from real missions. In analyzing elevation effects on speed, differences in search tactics, and coupled coordination within search teams, we gain a better understanding of searcher dynamics with actionable metrics that can be used to test and refine current SAR assumptions. The overarching contribution is an empirically grounded foundation that links lost person movement and searcher performance, bridging operational practice with formal search theory."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["When someone goes missing in the wilderness, every second counts. Search and rescue (SAR) teams must quickly decide two things: where the lost person is most likely to be and how to move searchers through the area efficiently and safely. This dissertation tackles both problems using real-world SAR mission data and behavior-based simulations. Lost people commonly use reorientation strategies like following roads and trails, climbing to higher ground for a better view or cell signal, or simply staying put, just to name a few. We also know that their choices can easily shift depending on the environment, and points like trail intersections, steep obstacles, or time of day can change a person's behavior. Using these patterns and a database of past cases, we built a model that simulates lost person movement on a map to predict where they may end up. Having this kind of information ahead of time can give SAR planners a clearer picture of likely locations the lost person may be and how to deploy their resources to best find them. It also helps to understand how searchers actually move: how slope and search strategies affect speed or how teams coordinate in the field. We find that uphill and downhill slopes slow down searchers in essentially the same way, and that teammates tend to stay within visual range and adjust to each other's speed changes within just a few seconds. These evidence-based numbers could enhance current practices that often depend on experience and rules of thumb to determine where to search. The overall goal is straightforward: make searches faster, safer, and more effective by modeling, with real data, both the people who are lost and the people trying to find them."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Modeling Human Movement in the Wilderness: Navigating Lost Person Behavior and Searcher Response"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Abaid, Nicole"],"dc:contributor.committeemember":["Childs, Lauren M.","Ross, Shane D.","Socha, John J.","Williams, Ryan K."],"dc:contributor.department":["Engineering Science and Mechanics"],"dc:creator":["Hashimoto, Amanda Hana"],"dc:date.accessioned":["2025-08-21T08:01:02Z"],"dc:date.available":["2025-08-21T08:01:02Z"],"dc:date.issued":["2025-08-20"],"dc:description.abstract":["Search and rescue (SAR) outcomes hinge on two behavioral uncertainties: how lost people move through complex landscapes and how ground teams search those landscapes. This dissertation develops a unified, data-driven view of both. First, we present a dynamic, agent-based model of lost person behavior that adapts empirically observed reorientation strategies (e.g., trail-following, staying put, backtracking), integrates map layers of terrain-specific features (e.g., roads, streams, powerline easements), and is validated against real incident data. Because navigation is so heavily-dependent on the landscape, we further extend the model to include more landscape-dependent behaviors (like following elevation contours), explicit spatial and temporal decision points, and direct comparisons to observed tracks. Through simulation of all possible behavior distributions, we find an average behavioral profile for a hiker in the wilderness that can be adapted to accommodate other categories of lost people in different landscapes and locations. Recognizing that the lost person is only part of the SAR operational equation, we then turn to the searchers themselves by quantifying human searcher movement and coordination using GPS logs from real missions. In analyzing elevation effects on speed, differences in search tactics, and coupled coordination within search teams, we gain a better understanding of searcher dynamics with actionable metrics that can be used to test and refine current SAR assumptions. The overarching contribution is an empirically grounded foundation that links lost person movement and searcher performance, bridging operational practice with formal search theory."],"dc:description.abstractgeneral":["When someone goes missing in the wilderness, every second counts. Search and rescue (SAR) teams must quickly decide two things: where the lost person is most likely to be and how to move searchers through the area efficiently and safely. This dissertation tackles both problems using real-world SAR mission data and behavior-based simulations. Lost people commonly use reorientation strategies like following roads and trails, climbing to higher ground for a better view or cell signal, or simply staying put, just to name a few. We also know that their choices can easily shift depending on the environment, and points like trail intersections, steep obstacles, or time of day can change a person's behavior. Using these patterns and a database of past cases, we built a model that simulates lost person movement on a map to predict where they may end up. Having this kind of information ahead of time can give SAR planners a clearer picture of likely locations the lost person may be and how to deploy their resources to best find them. It also helps to understand how searchers actually move: how slope and search strategies affect speed or how teams coordinate in the field. We find that uphill and downhill slopes slow down searchers in essentially the same way, and that teammates tend to stay within visual range and adjust to each other's speed changes within just a few seconds. These evidence-based numbers could enhance current practices that often depend on experience and rules of thumb to determine where to search. The overall goal is straightforward: make searches faster, safer, and more effective by modeling, with real data, both the people who are lost and the people trying to find them."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:44577"],"dc:identifier.uri":["https://hdl.handle.net/10919/137552"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Agent-based modeling","Decision points","Lost person behavior","Search and rescue"],"dc:title":["Modeling Human Movement in the Wilderness: Navigating Lost Person Behavior and Searcher Response"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Engineering Mechanics"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:16Z"}