STEPFlow_Spatio-temporal modelling and estimation of pedestrian flow

For urban planning and design, one of the main challenges is to understand how urban form impacts urban life where the people’s co-presence in public space (i.e. walking, sitting, standing) is one of the most important factors. How many people share public space is known to affect many socio-economic processes in cities such as segregation (i.e. who meets whom) and local commercial markets (where economic activities can flourish and where not). Sweden is in the middle of an exceptional attempt to build 700.000 housing units until 2025, which has the potential to redirect Swedish cities into more sustainable trajectories, where knowledge how the built environment affects movement flows is key.
The Spatial Morphology Group at Chalmers University of Technology, ACE, has conducted a large pedestrian survey including almost 900 streets in Stockholm, Amsterdam and London which allows to study this relation in depth. The objective of the STEPFlow project is to create a user-friendly web-interface visualization platform and APIs to ease access and utilisation of the developed models, with the intention to bring wider utilisation. From the scientific perspective, it is expected to provide new ways to understand and model pedestrian behaviour. From the practical side, the project aims at more effective design and planning of cities, where the aim is to use this knowledge, for example, to locate services where they fit best.
The expected outcome is a web-interface with at least the following two capabilities: First, to extract and graphically present typical statistics of pedestrian behaviour based on the database of the described survey. It should include flow intensity maps, basic measures of fluctuation during the day (such as the standard deviation of the intensity over the day), and individual routes, as well as goodness-of-fit measures for the model such as histograms of residuals; second, to visualize the pedestrian flow intensities, and classifications, in areas where no data is available, based on our statistical models.
The project is funded by BIGDATA@Chalmers and will be conducted in cooperation with them as well as the Department of Mathematical Sciences and SMoG at ACE.