[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126195-en":3,"doc-seo-126195-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126195,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Hansen’s Accessibility Theory and Machine Learning: a Potential Merger - Research Article","Accessibility is a central concept in transport geography because it connects mobility with land development. The work studies urban accessibility dynamics by combining Hansen’s Accessibility Model (HAM) with machine learning, aiming to reproduce HAM behaviour from data and to embed geography, transport-network and socioeconomic influences. It also examines ML feasibility when flow data are unavailable, ensuring consistent measurements over time. A case study covers inter-urban accessibility in Lombardia and Emilia Romagna using 2011 data, showing the potential of the joint HAM-ML approach for theory, empirical analysis, and policy perspectives.","This is the peer reviewd version of the followng article:  \nHansen’s Accessibility Theory and Machine Learning: a Potential Merger / Hadjidimitriou, N.; Reggiani, A.;Östh, J. ; Mamei, M.. - In: NETWORKS AND SPATIAL ECONOMICS. - ISSN 1566-113X. - (2025), pp. 1-26.[10.1007/s11067-025-09674-2]  \nTerms of use:  \nThe terms and conditions for the reuse of this version of the manuscript are specified in the publishing policy. For all terms of use and more information see the publisher's website.  \n12/06/2025 11:44  \n(Article begins on next page)  \nNetworks and Spatial Economics  \n[https://doi.org/10.1007/s1](https://doi.org/10.1007/s1)1067-025-09674-2  \nRESEARCH  \nHansen’s Accessibility Theory and Machine Learning: a Potential Merger  \nNatalia Selini Hadjidimitriou1 · Aura Reggiani2 · John Östh3 · Marco Mamei1  \nAccepted: 21 January 2025 © The Author(s) 2025  \nAbstract  \nAccessibility is a central concept in transport geography, given its relationship with land development. It is defined as \"the opportunity that an individual at a given location possesses to take part in a particular activity or set of activities\" (Hansen, 1959) . Hansen’s Accessibility Model (HAM) can be computed using mobility flows between regions or employment data as a proxy for centres of attraction, coupled with an impedance function that incorporates travel costs. It has been the basis of multiple theoretical and empirical approaches over the years. In the last decades, advances in Machine Learning (ML) have also opened new possibilities for developing innovative approaches in the transport field. The objective of this work is primarily the study of the dynamics of urban accessibility, by considering two interrelated perspectives. Firstly, we aim to explore whether alternative data-based techniques, such as ML, can replicate the behaviour of HAM and thus capture, from data, the underlying theory linked to the spatial interaction model, by embedding the influence of geography, transport network, and socioeconomic factors on accessibility. Secondly, we investigate the feasibility of employing ML where flow data are unavailable, ensuring consistent measurements over time. A combined approach HAM-ML is developed and applied to this aim. As a case study, we examine inter-urban accessibility in two Italian regions, Lombardia and Emilia Romagna, based on socioeconomic and transport data from 2011. The results show the potential of this joint approach, opening new research prospects on accessibility from the theoretical, empirical, and policy viewpoints.  \nKeywords Accessibility · Hansen’s model · Spatial interaction model · Machine learning · Random forest · Neural networks  \nExtended author information available on the last page of the article  \n1 3  \n1 Introduction  \nAccessibility allows reaching different destinations with the minimum effort and time. When the objective is to go to work, accessibility might impact employment opportunities and quality of life (Geurs et al. 2012; Litman 2013; Preston and Rajé 2007) .  \nFrom the modelling viewpoint, we can consider Hansen (1959) as the ’founding father of accessibility’, thanks to his fundamental article, where he considers accessibility as: a) \u003CThe potential of opportunities for interaction>; b) \u003CA measure of the intensity of the possibility of interaction rather than just a measure of the ease of interaction>; and c) \u003Caccessibility is the measurement of the spatial distribution of activities about a point (area/city/region), adjusted for the ability and the desire of people or firms to overcome spatial separation> (again Hansen 1959, p. 73) .  \nTherefore, accessibility is strictly linked to spatial structures and the related social processes and behavioural rules.  \nInterestingly, from a theoretical viewpoint, Hansen’s Accessibility Model (HAM) can be linked to the Spatial Interaction Model (SIM), and thus to entropy maximization, as well as to connectivity networks (see Section 2) .  \nFollowing Hansen, accessibil","cbCaiuUZAWYQNSta","https://ap.wps.com/l/cbCaiuUZAWYQNSta","pdf",3370718,5,1,27,"English","en",105,"# Introduction\n## Accessibility and its role in transport geography\n## Hansen’s Accessibility Model (HAM) and spatial interaction links\n## Motivation: mobility flows, COVID-19, and data limitations\n## Objective and research questions","[{\"question\":\"What is the main objective of combining HAM with machine learning?\",\"answer\":\"To study urban accessibility dynamics by testing whether ML techniques can replicate HAM behaviour using data and capture the underlying spatial interaction theory.\"},{\"question\":\"How does the approach address situations when flow data are unavailable?\",\"answer\":\"It investigates the feasibility of using ML to obtain consistent accessibility measurements over time without relying on mobility flow data.\"},{\"question\":\"What data and regions are used in the case study?\",\"answer\":\"The case study analyzes inter-urban accessibility in two Italian regions—Lombardia and Emilia Romagna—using socioeconomic and transport data from 2011.\"}]","Hansen’s Accessibility Theory and Machine Learning: a Potential Merger - 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