Christof Teuscher and Philippe Proctor

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Running -- Training, City planning -- Environmental aspects, Route choice


Since the pandemic started, many gyms and indoor classes have been shut down to mitigate the spread of Coronavirus. Many people have been forced to get onto pavement streets to get some fresh air while running around and coping with the new reality. There are over 60 million runners in the U.S., and that number is growing rapidly during this time without any sign of stopping once life gets back to normal. In this project, an agent-based model has been developed to generate a set of routes that runners would take in their daily run in a neighborhood of Portland city. The street network and data from OpenStreetMap are downloaded by the OSMnx package in python before being transformed to a grid world and having runners roam around with several preferences of behaviors. A heat map is generated at the end to compare to the Strava heat map which uses aggregated, public data. This approach is the first step to get to know more about runners’ behavior and transferable to other neighborhoods or cities as well.


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Quang Le - Final Presentation.pdf (4080 kB)
Final Presentation