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Simple agents – complex emergent path systems: Agent-based modelling of pedestrian movement
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.ORCID iD: 0000-0001-9579-6344
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.ORCID iD: 0000-0002-3884-3084
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Computer Science. Division of Visual Information and Interaction, Department of Information Technology, Uppsala University, Uppsala, Sweden.ORCID iD: 0000-0003-0085-5829
Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, China.ORCID iD: 0000-0001-9328-9584
2024 (English)In: Environment and planning B: Urban analytics and city science, ISSN 2399-8083, E-ISSN 2399-8091, Vol. 51, no 2, p. 479-495Article in journal (Refereed) Published
Abstract [en]

In well-planned open and semi-open urban areas, it is common to observe desire paths on the ground, which shows how pedestrians themselves enhance the walkability and affordance of road systems. To better understand how these paths are formed, we present an agent-based modelling approach that simulates real pedestrian movement to generate complex path systems. By using heterogeneous ground affordance and visit frequency of hotspots as environmental settings and by modelling pedestrians as agents, path systems emerge from collective interactions between agents and their environment. Our model employs two visual parameters, angle and depth of vision, and two guiding principles, global conception and local adaptation. To examine the model’s visual parameters and their effects on the cost-efficiency of the emergent path systems, we conducted a randomly generated simulation and validated the model using desire paths observed in real scenarios. The results show that (1) the angle (found to be limited to a narrow range of 90–120°) has a more significant impact on path patterns than the depth of vision, which aligns with Space Syntaxtheories that also emphasize the importance of angle for modelling pedestrian movement; (2) the depth of vision is closely related to the scale-invariance of path patterns on different map scales; and(3) the angle has a negative exponential correlation with path efficiency and a positive correlation with path costs. Our proposed model can help urban planners predict or generate cost-efficient path installations in well- and poorly designed urban areas and may inspire further approaches rooted in generative science for future cities.

Place, publisher, year, edition, pages
Sage Publications, 2024. Vol. 51, no 2, p. 479-495
National Category
Geosciences, Multidisciplinary
Identifiers
URN: urn:nbn:se:hig:diva-42413DOI: 10.1177/23998083231184884ISI: 001011852000001Scopus ID: 2-s2.0-85162881096OAI: oai:DiVA.org:hig-42413DiVA, id: diva2:1773446
Available from: 2023-06-22 Created: 2023-06-22 Last updated: 2024-11-26Bibliographically approved
In thesis
1. From Understanding to Generative Design of Sustainable Urban Forms
Open this publication in new window or tab >>From Understanding to Generative Design of Sustainable Urban Forms
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Urban forms, as a physical proxy of human mobility and activity, are crucial for understanding the mechanisms and factors behind urban dynamics. The rapid expansion of urban sensors and big data has significantly advanced our comprehension of cities and societies. Currently, the development of complexity sciences has led to more sophisticated simulation models, presenting new opportunities for understanding complex urban phenomena. This dissertation integrates fractal geometry theory, deep learning models, and agent-based modelling (ABM) to enhance the understanding and future generative design of sustainable urban forms. Three models were developed in this dissertation based on: Graph-based fractality index (GFI), Spatio-structural self-similarity, Simple agents–complex emergent path systems (SACP), as well as a model and web-based tool for road evaluation by desire path system (RED-PaSS). (1) The GFI model, grounded in fractal theory and deep learning techniques, is capable to characterize the complexity of building groups; (2) the spatio-structural self-similarity model examines self-similarity from a spatial and structural perspective, correcting long-standing misinterpretations in classical statistical fractal theories. The spatio-structural self-similarity model is capable to understand historical urban forms through validation with data from London building groups and US street networks; (3) the SACP model is based on ABM and simulates pedestrian movement based on visibility parameters and simple principles of global destination awareness and local environmental adaptation. The findings of SACP indicate that the angle of vision is crucial for path pattern emergence; and (4) the RED-PaSS model and tool that evaluates road networks by simulating optimal pedestrian paths based on the SACP model. Case studies of 708 US neighbourhood-scale road networks demonstrate RED-PaSS's potential to evaluate, rank, and enhance road networks, improving pedestrian mobility and convenience. This dissertation's holistic approach not only aids in the characterization of current urban patterns but also in generative design of future urban landscapes that are sustainable and resilient. The integration of advanced computational techniques, such as deep learning and ABM, enables exploration of urban dynamics at unprecedented scales and resolutions. The continuous advancement of these models is crucial for addressing urbanization challenges and fostering sustainable, liveable cities.

Abstract [sv]

Stadsformer, som en fysisk proxy för mänsklig mobilitet och aktivitet, är avgörande för att förstå mekanismerna och faktorerna bakom urban dynamik. Hållbarheten i mänskliga aktiviteter fångas och förutsägs också genom studiet av stadsformer. Den snabba expansionen av urbana sensorer och stordata (eng. big data) har avsevärt förbättrat vår förståelse av städer och samhällen. För närvarande har utvecklingen av komplexitetsvetenskap lett till mer sofistikerade modeller, vilket ger nya möjligheter att förstå komplexa urbana fenomen. Denna avhandling integrerar fraktalgeometriteori, djupinlärningsmodeller och agentbaserad modellering (ABM) för att förbättra förståelsen och framtida generativ design av hållbara stadsformer. Fyra modeller utvecklades i denna avhandling baserade på: grafbaserat fraktalitetsindex (GFI), spatio-strukturell självlikhet, enkla agenter–komplexa emergenta vägsystem (SACP), samt en modell med tillhörande webbaserade verktyg för vägutvärdering genom önskat stigsystem (RED-PaSS). (1) GFI-modellen, grundad i fraktalteori och djupinlärningstekniker, kan karakterisera komplexiteten hos byggnadsgrupper; (2) den spatio-strukturella självlikhetsmodellen undersöker självlikhet ur ett rumsligt och strukturellt perspektiv och korrigerar den sedan länge misstolkningen av självlikhet i klassisk statistisk fraktalteori. Denna modell kan också förutsäga årtalen för historiska stadsformer, inklusive byggnadsgrupper i London och gatunät i USA; (3) SACP-modellen är baserad på ABM och simulerar uppkomsten av önskade stigar genom att modellera fotgängares naturliga rörelse med enkla parametrar på syn och interaktionsprinciper; och (4) RED-PaSSmodellen med sitt verktyg utvärderar, rangordnar, och förbättra gångbarheten i vägnät jämfört med de genererade optimala vägsystemen baserade på fotgängares naturliga rörelse. Denna avhandlings holistiska angreppssätt hjälper inte bara till att karakterisera nuvarande urbana mönster utan också i generativ design av framtida urbana landskap som är naturliga och hållbara. Integrationen av avancerade beräkningstekniker, såsom djupinlärning och ABM, möjliggör utforskning av urban dynamik i aldrig tidigare använd skala och upplösning. Den kontinuerliga utvecklingen av dessa modeller är avgörande för att hantera urbaniseringsutmaningar och främja hållbara, levande städer.

Place, publisher, year, edition, pages
Gävle: Gävle University Press, 2024. p. 66
Series
Doctoral thesis ; 52
Keywords
urban forms, agent-based modelling, pedestrian movement, desire paths, fractals, self-similarity, building groups, street networks, generative design, urbanistiska former, agentbaserad modellering, gångrörelser, önskade vägar, fraktaler, självlikhet, byggnadsgrupper, gatunätverk, generativ design
National Category
Geosciences, Multidisciplinary
Identifiers
urn:nbn:se:hig:diva-45835 (URN)978-91-89593-46-6 (ISBN)978-91-89593-47-3 (ISBN)
Public defence
2024-12-17, Lilla Jadwigasalen, Kungsbäcksvägen 47, Gävle, 13:00 (English)
Opponent
Supervisors
Available from: 2024-11-26 Created: 2024-10-14 Last updated: 2024-11-26Bibliographically approved

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Ma, LeiBrandt, S. AndersSeipel, Stefan

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