From explanations to predictions. Developing a predictive model of pedestrian flows on existing and planned streets.
Paper i proceeding, 2024
The paper outlines the methodology and results of the model, which employs LASSO regression in machine learning to predict numbers of pedestrians at the street segment level. The model is trained using data gathered in Stockholm and is first tested by predicting full-day pedestrian counts at street segments of central Gothenburg. The model is evaluated both in relation to predicting the absolute number of pedestrians and their relative distribution within the area. This concise yet effective model shows promising results for early forecast of pedestrian flows in development plans and infrastructural changes and can offer a valuable tool for planners and designers to influence and optimise the distribution of pedestrian flows in various urban contexts.
Machine learning
Urban design and planning
Pedestrian flows
Space syntax
Predictive modelling
Författare
Ioanna Stavroulaki
Chalmers, Arkitektur och samhällsbyggnadsteknik, Stadsbyggnad
Oscar Ivarsson
Chalmers, Fysik, E-commons
Meta Berghauser Pont
Chalmers, Arkitektur och samhällsbyggnadsteknik, Stadsbyggnad
Vilhelm Verendel
Chalmers, Fysik, E-commons
Proceedings of the 14th International Space Syntax Symposium, SSS 2024
64
Nicosia, Cyprus,
Digital Twin Cities Centre
VINNOVA (2019-00041), 2020-02-29 -- 2024-12-31.
Crowd Movement. Prognostisera gångflöden i det offentliga rummet.
VINNOVA, 2021-03-01 -- 2023-02-28.
Chalmers, 2021-03-01 -- 2023-02-28.
Ämneskategorier
Arkitekturteknik
Data- och informationsvetenskap
Infrastrukturteknik
Miljöanalys och bygginformationsteknik
Drivkrafter
Hållbar utveckling
Relaterade dataset
Spatial Morphology Lab 01. International laboratory for comparative research in urban form. Street networks, Sweden - Non-Motorised network of Gothenburg [dataset]
ID: snd1153-1 DOI: https://doi.org/10.5878/x49h-pv07