Mapping Urban Cycle Efficiency in Munich

“From Stops to Flow: Breaking the Cycle of Delays.”

What it is about

In cities worldwide, urban cycling has become a preferred mode of transportation due to its health benefits, environmental sustainability, and convenience. This project aims to evaluate how traffic lights in Munich contribute to cycling delays and provide a visual representation of the existing cycling landscape. This tool can be instrumental when advocating for cyclist-centred urban planning, ensuring that infrastructure designs account not only for cycle path quantity and quality but also for flow and uninterrupted travel.

How we built it

This project is entirely based on Open Street Map data. The workflow consists of three blocks of Python coding and three blocks of QGIS operators. The building blocks are robust and can be reused with other input parameters. For the visualisation, we chose to create two distinct output types. The first is an interactive web map that enables users to explore the total delays experienced by the 14th intake of the MSc Cartography. Users can click on routes, segments, and homes to access detailed data. The second output consists of personalised maps tailored to each member of this intake.

Challenges we ran into

Our primary challenge has been and continues to be the quality of the cycling network data. Due to imperfections in this network (nodes and links), our analysis occasionally encounters difficulties. This includes instances where a route navigates around traffic lights due to irregularities in the network links. The coding of both the data analysis and parts of the web map along with as model-building processes involved a significant amount of trial and error to achieve satisfactory results.

What we're proud of

This project was developed entirely using open-source data. Thanks to a structured workflow utilising Python and custom models in QGIS, we can conduct similar analyses for any city or region, based on any input address and central starting point. This makes our work for Munich reproducible and transferable to other cities.

What we learned

During this project, we learned valuable lessons in patience and problem-solving. We both developed custom models in QGIS for the first time, improved our programming skills, and explored new Python libraries.

What's next

In the future, we could enhance this analysis by utilising a more accurate network dataset, such as Garmin's cycling GPS data. Rather than average statistics, individual traffic light data would improve estimation accuracy. Adding a feature to the interactive web map that allows navigation from any address within the network and directly estimates predicted delays would enhance the user experience. Comparing cycling efficiency between different cities could also help to make relative efficiency assessments.
Students
Thijs Van Laar and Emiel Verté

14th intake
Supervisor
Juliane Cron, M.Sc.
Keywords
Urban mobility, Cycling infrastructure, Munich, Web-map, Commute, Traffic lights
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