Measuring what really matters
Selecting the right indicators to measure micro-mobility in cities (7 min read)
When dockless e-scooters and e-bikes flooded cities in the late 2010s, city officials didn’t sit idly by. Still licking their wounds from the upheaval triggered by ride-hailing apps, many of them moved quickly to mandate data sharing to the new disruptors.
The enthusiasm lasted just so long. City officials were quickly confronted with a hard truth: accessing loads of data from micro-mobility operators could be burdensome, costly, and often useless. Rather than having tons of real-time data, first they had to decide which information they actually needed.
In other words, they had to identify the key indicators that help them understand how mobility is working - or not - so they can take action accordingly.
Micro-mobility ain’t so “micro”
You now may be thinking, all right, this is cool, but how much does access to the data of a bunch of shared bikes and scooters really matter?
And to some extent you are right. Shared micro-mobility only makes for a small fraction of the total trips happening in a city on any given day. Most people move around in their own vehicles (mainly cars), public transit, or walking. But I think the lessons we can learn from micro-mobility are important because they can be applied to other modes, such as cars (see the increasing regulations around real-time in-vehicle data), autonomous vehicles, or drones which are already or will be soon deployed in cities.
So let’s get into the weeds of what data can help cities make their mobility safer, more accessible, and more sustainable.
The vision from above: from lofty goals to specific indicators
I’ve written before about the data revolution the EU is pushing for in urban mobility. A central piece in this “revolution” is the set of indicators that cities will need to collect and report in their Sustainable Urban Mobility Plans to measure the sustainability, accessibility, and safety of their mobility. While the list is not final yet, the European Commission’s Expert Group on Urban Mobility has already proposed three indicators that require data from micro-mobility operators:
Trips per mode & distance travelled per mode (sustainability)
Total fleet size of shared bikes/scooters/mopeds (access)
Persons killed or seriously injured by user category (safety)
The International Transport Forum (ITF) at the OECD, has also done important work in this area, coming up with a “tool-box” of 17 indicators to link data requests to policy goals.
Both organisations begin from the same principle: collect only the data that answers a real policy question. Together they also give us a disciplined way to monitor the benefits and downsides of mobility without drowning in endless data-feeds and confusing spreadsheets.
The right-sized indicator set for cities
Below is the distilled cheat-sheet that blends both frameworks into nine core indicators:
But again, remember that data are only as good as the questions they answer. So, starting from a broad question (How CO2 efficient is the modal split in my city?), we can outline a quick 5-step guide to translate data into indicators, and indicators into decisions:
Begin with a hypothesis. “We believe shared bikes can replace 15% of sub-5 km car trips.”
Choose the indicator that can falsify it. In this case: alternative mode replaced + trip distance.
Use the lightest possible data feed. An annual in-app survey and monthly trip logs, not full-resolution GPS data.
Tie the result to a decision. If the hypothesis holds, expand bike-share docks near frequent routes and connecting hubs; if not, adjust pricing, communications, or infrastructure.
Publish the outcome. Transparency builds public trust and improves policy decisions.
Towards better questions, not bigger databases
Shared micro-mobility will never dominate the mobility modal split, and that’s fine. Its strategic value lies in its potential for real-world testing of data sharing practices that can be transported to more pervasive modes.
To fully generate that value, cities must resist the give-me-all-you-got reflex. The nine-indicator set and the 5-step approach above can become a powerful tool for cities to make better-informed decisions in a way that makes sense for them and doesn’t create too much burden to innovators out there.