Módszerek összehasonlítása

	Pedestrian Flow Analysis	Spatial Design Network Analysis (sDNA)
Tudományterület	Urban Studies	Urban Studies
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	1995	2020
Megalkotó	Dirk Helbing & Péter Molnár (social force model)	Crispin H. V. Cooper & Alain J. F. Chiaradia
Típus	Measurement and simulation of pedestrian movement and flow	Link-based spatial network analysis of street and path networks
Alapmű	Helbing, D., & Molnár, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282–4286.	Cooper, C. H. V., & Chiaradia, A. J. F. (2020). sDNA: 3-d spatial network analysis for GIS, CAD, Command Line & Python. SoftwareX, 12, 100525.
Alternatív nevek	Pedestrian Movement Analysis, Footfall Analysis, Crowd Flow Modelling, Pedestrian Traffic Analysis	sDNA, Spatial Design Network Analysis, Link-Based Network Analysis, 3D Spatial Network Analysis
Kapcsolódó	4	4
Összefoglaló	Pedestrian flow analysis measures and models how people move on foot through streets, plazas, transit stations and buildings, combining empirical counts with simulations of individual walking behaviour. It treats walking as a flow phenomenon — characterised by density, speed and volume — while also resolving the micro-scale decisions of individual pedestrians through agent-based and social-force models. Building on the social force model of Dirk Helbing and Péter Molnár (1995), the approach links observed gate counts and flow–density relationships to mechanistic simulations that can predict congestion, evacuation times and the effect of design changes before they are built.	Spatial Design Network Analysis (sDNA) is a toolkit for analysing street and path networks as link-based spatial graphs, measuring how individual road segments function as routes and destinations within the larger network. Developed by Crispin Cooper and Alain Chiaradia at Cardiff University, it computes closeness- and betweenness-style measures over geometrically accurate, optionally three-dimensional networks, using hybrid distance metrics that blend metric length, angular turn cost and topological steps. By weighting links and analysing them within chosen radii, sDNA predicts pedestrian and vehicle flows, land values and accessibility, bridging the configurational tradition of space syntax with mainstream geographic-information-system network analysis.