Like many of you, I use bike-share system pretty regularly. And, honestly, it drives me nuts: either there’s not a single bike when I need one, or the station is packed and I can’t return mine. It feels inefficient—but is it really, or is it just bad luck?
That nagging question led me to start tinkering with BiziData, a little side project to dig into the data behind the system.
Why this matters
Cities release mobility data all the time, but it’s usually buried in APIs or spreadsheets that are a pain to make sense of. So, I thought: What if we could actually see what’s going on?
So I started exploring things:
- How does bike availability change throughout the day?
- Where do the biggest imbalances happen?
What patterns emerge in how people move around?
I’m also curious about how outside factors play into usage:Do big events in the city spike demand?
How does weather mess with usage patterns?
How do other transport options (like buses or scooters) interact with bike-share?
Could changes in bike lanes or infrastructure make a difference?
The big picture? Using data to understand—and maybe even improve—how urban mobility works.
What’s built so far
Right now, BiziData pulls data from Zaragoza’s bike-share and turns it into a simple dashboard. It shows:
- Real-time station availability
- Historical usage trends
- Visualizations of system activity Tech stack: Next.js, TypeScript, Prisma, Redis
Could this work in other cities?
For now, it’s just Zaragoza—but here’s the cool part: a lot of bike-share systems (especially those run by Lyft) use similar tech. That means the same approach could work elsewhere, and even let us compare how different cities handle mobility.
The tough parts
Some things were harder than I expected:
Building a reliable data pipeline from open APIs
Figuring out which metrics actually matter (and not drowning in noise)
Resisting the urge to scope-creep—there are so many directions to take this!
Monetization? Still a mystery
Honestly, I haven’t figured this out yet. I've been battling AI with the role of an investor who doesn't want to part with a penny, and it's won..But I do think there’s potential: if data can help cities manage bike-share better—like improving bike distribution, guiding infrastructure decisions, or cutting inefficiencies—everyone wins.
I’d love your thoughts!
If you’ve got experience with mobility data, bike-share systems, or just ideas, I’d love to hear:
- What metrics would you want to see in a mobility dashboard?
- Would cross-city comparisons be useful?
- Have you worked with similar data before?
Check out the repo if you’re curious: https://github.com/gcaguilar/bizidashboard
Spoiler: There’s more coming…
(Shhh… I’m already working on a mobile app to help you find the nearest station with available bikes or free slots. Because, let’s be real, nobody wants to walk five blocks just to find out they can’t park their bike.)
Thanks for reading—and let me know what you think! 🚴♂️
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