Investigating pedestrian behaviour in urban environments: a Wi-Fi tracking and machine learning approach

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dc.contributor.authorStanitsa, Avgousta
dc.contributor.authorHallett, Stephen H.
dc.contributor.authorJude, Simon
dc.date.accessioned2022-11-23T14:45:13Z
dc.date.available2022-11-23T14:45:13Z
dc.date.issued2022-11-11
dc.description.abstractUrban geometry plays a critical role in determining paths for pedestrian flow in urban areas. To improve the urban planning processes and to enhance quality of life for end-users in urban spaces, a better understanding of the factors influencing pedestrian movement is required by decision-makers within the urban design and planning industry. The aim of this study is to present a novel means to assess pedestrian routing in urban environments. As a unique contribution to knowledge and practice, this study: (a) enhances the body of knowledge by developing a conceptual model to assess and classify pedestrian movement behaviours, utilising machine learning algorithms and location data in conjunction with spatial attributes, and (b) extends previous research by revealing spatial visibility as a driver for pedestrian movement in urban environments. The importance of the findings lies in the perspective of revealing novel insights concerning individual preferences and behaviours of end-users and the utilisation of urban spaces. The approaches developed can be utilised for observations in large-scale contexts, as an addition to traditional methods. Application of the model in a high pedestrian traffic-dense retail urban area in London reveals clear and consistent relationships amongst spatial visibility, individuals’ motivation, and knowledge of the area. Key behaviours established in the study area are grouped into two activity categories: (i) Utilitarian walking (with motivation - expert and novice striders) and (ii) Leisure walking (no motivation - expert and novice strollers). The approach offers an insightful and automated means to understand pedestrian flow in urban contexts and informs wider wayfinding, walkability, and transportation knowledge.en_UK
dc.identifier.citationStanitsa A, Hallett SH, Jude S. (2023) Investigating pedestrian behaviour in urban environments: a Wi-Fi tracking and machine learning approach. Multimodal Transportation, Volume 2, Issue 1, March 2023, Article number 100049en_UK
dc.identifier.issn2772-5871
dc.identifier.urihttps://doi.org/10.1016/j.multra.2022.100049
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18729
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPedestrian movementen_UK
dc.subjectMachine-learningen_UK
dc.subjectUrban environmenten_UK
dc.subjectWi-Fi trackingen_UK
dc.subjectHuman behaviouren_UK
dc.titleInvestigating pedestrian behaviour in urban environments: a Wi-Fi tracking and machine learning approachen_UK
dc.typeArticleen_UK

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