Where to build cycle lanes and bicycle parking?

Welcome to our tool, a game-changer in urban cycling infrastructure. Cities are increasingly adopting cycling to promote sustainability, yet this comes with significant costs. Our solution? Intelligently integrating cycle lanes and bicycle parking with existing transit networks. This approach not only economizes resources but also creates a harmonious synergy between different modes of transportation. Let's redefine urban commuting together!

Imagine living in Amsterdam: if you're in the city center or along metro lines, job accessibility is high. Yet, the farther you are from these transit hubs, the fewer opportunities within easy reach.

Now, picture your daily commute from this neighborhood.

You might start with a bus ride or a long walk to reach the metro, extending your commute time significantly.

Alternatively, cycling directly to the metro station could save you time and avoid the hassle of transferring, a practice known as multimodality.

Our estimates show that when cycling and transit can be combined, accessibility increases significantly. However, for this to be feasible, proper cycling infrastructure is essential. Where to place it?

This is where our tool comes in . We quantify the contribution of bicycle parking at transit stops in the total potential accessibility gains.

Our analysis extends to cycle lanes, suggesting the most strategic placements to serve these transit stops efficiently.

The outcome? Detailed, street-level insights that equip urban planners with the data needed to make informed decisions on where to implement these crucial amenities.

You want to know how to apply this approach to your city? Check out this open scientific publication! I am always up to discuss the results further, feel free to contact me at contact-me@lucas-spierenburg.eu.

Details

Written by Lucas Spierenburg
Supervised by Hans van Lint and Niels van Oort
at the TU Delft, 2024

References

Paper
Code
Data

Credits

Raw data from city of Amsterdam and OpenStreetMap.
Analysis conducted using Geopandas and OSMnx.
Data story realized with d3.js and scrollama.