[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124187-en":3,"doc-seo-124187-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124187,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Machine learning for human mobility during disasters - A systematic literature review","Understanding and predicting human mobility during disasters is essential for effective disaster management and for enhancing rescue missions and evacuations using knowledge about population locations. Realistic mobility models that reflect observable patterns and volumes are required, yet existing approaches often fit regular mobility only. Machine learning likewise depends on training data, but disaster mobility data are scarce. This systematic review presents and discusses strategies to address these limitations and supports future research synthesis by mapping, classifying, analyzing, and comparing relevant contributions. It also details general challenges and future research directions.","Progress in Disaster Science 25 (2025) 100405  \nContents lists available at ScienceDirect  \nProgress in Disaster Science  \njournal [homepage: www.elsevier.com/locate/pdisas](homepage: www.elsevier.com/locate/pdisas)  \n| Machine learning for human mobility during disasters: A systematic literature review\u003Cbr>Jonas Gunkela,*, Max Mühlh¨auserb, Andrea Tundisa\u003Cbr>a Institute for the Protection of Terrestrial Infrastructures, German Aerospace Center (DLR), Rathausallee 12, St. Augustin 53757, Germany b Department of Computer Science, Technische Universit¨at Darmstadt (TU Darmstadt), Hochschulstrasse 10, 64289 Darmstadt, Germany |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Human mobility\u003Cbr>Disaster mobility\u003Cbr>Disaster response\u003Cbr>Machine learning Deep Learning |  | Understanding and predicting human mobility during disasters is crucial for effective disaster management. Knowledge about population locations can greatly enhance rescue missions and evacuations. Realistic models that reflect observable mobility patterns and volumes are crucial for estimating population locations. However, existing models are limited in their applicability to disasters, as they are typically restricted to describing regular mobility patterns. Machine learning models trained to capture patterns observable in provided training data also face this limitation. The necessity of large amounts of training data for machine learning models, coupled with the scarcity of data on mobility in disasters, often constrains the feasibility of their training. Various strategies have been developed to overcome this issue, which we present and discuss in this systematic literature review. Our review aims to support and accelerate the synthesis of novel approaches by establishing a knowledge base for future research. This review identified a condensed field of related contributions exhibiting high methodology and context diversity. We classified and analyzed the relevant contributions based on their proposed approach and subsequently discussed and compared them qualitatively. Finally, we elaborated on general challenges and highlighted areas for future research. |\n\n1. Introduction  \nIn recent years, the economic loss and human harm caused by disasters have reached consistently high levels. Large-scale events, such as hurricanes, typhoons, and earthquakes, have caused billions of dollars in damage and claimed thousands of lives [1,2]. Moreover, these events severely threaten the vital function of critical infrastructures. Due to its spatial extent, the transportation infrastructure is especially exposed to disasters [3]. Their impact on human mobility is twofold: On the one hand, the physical components of the transportation network may be damaged and become unusable. On the other, the mobility behavior of the population is affected as people may leave their routines and behave unexpectedly [4]. Consequently, the regular spatio-temporal mobility patterns are disrupted, introducing a complexity that is challenging to comprehend. However, a systematic understanding and situation assessment of human mobility during disasters is essential for developing preventive measures or planning rescue and evacuation missions. Models that describe the mobility dynamics in such situations are necessary to gain insights into the population’s location and its mobility behavior. Machine learning (ML) models can adopt this role as they can  \ncapture and reproduce patterns from observed mobility data.  \nThe interest in ML models for predicting future mobility has raised increasingly in recent years [5], accelerated by a growing amount of mobility data. ML has been applied to various tasks in mobility modeling, e.g., predicting a person’s next location, predicting crowd flows between different regions, and generating synthetic trajectories [6]. Despite the heterogeneity of ML applications for mobility, most publications have focused on regular mobility","cbCaidazK22WB3GU","https://ap.wps.com/l/cbCaidazK22WB3GU","pdf",697841,1,17,"English","en",105,"# Introduction\n# Machine learning for disaster mobility\n# Systematic literature review approach\n# Classification and qualitative analysis\n# Challenges and future research directions","[{\"question\":\"Why is predicting human mobility during disasters important?\",\"answer\":\"It supports effective disaster management by helping estimate population locations and plan rescue and evacuation missions, since mobility patterns change when infrastructure is disrupted and routines are left.\"},{\"question\":\"What limitations affect existing mobility models and machine learning approaches?\",\"answer\":\"Many models are tailored to regular mobility patterns, limiting applicability to irregular disaster situations; additionally, machine learning training needs large datasets, while mobility data from disasters are often scarce.\"},{\"question\":\"What does the systematic literature review contribute?\",\"answer\":\"It establishes a knowledge base by identifying related contributions, classifying and analyzing them by proposed approach, comparing them qualitatively, and outlining general challenges and future research opportunities.\"}]","Machine learning for human mobility during disasters - A systematic literature review | PDF",1785820936,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-human-mobility-during-disasters-a-systematic-literature-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-human-mobility-during-disasters-a-systematic-literature-review/124187/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting human mobility during disasters important?","Question",{"text":75,"@type":76},"It supports effective disaster management by helping estimate population locations and plan rescue and evacuation missions, since mobility patterns change when infrastructure is disrupted and routines are left.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations affect existing mobility models and machine learning approaches?",{"text":80,"@type":76},"Many models are tailored to regular mobility patterns, limiting applicability to irregular disaster situations; additionally, machine learning training needs large datasets, while mobility data from disasters are often scarce.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the systematic literature review contribute?",{"text":84,"@type":76},"It establishes a knowledge base by identifying related contributions, classifying and analyzing them by proposed approach, comparing them qualitatively, and outlining general challenges and future research opportunities.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]