Cycling Data Isn’t the Same as Car Data — And That Matters for Privacy
Introduction
Cities are increasingly turning to data to understand how people move.
Connected vehicle data is now widely used to analyse traffic patterns, congestion and network performance. As cycling data becomes more important, it is often assumed that the same approaches to data collection and privacy can be applied.
But cycling data is fundamentally different.
And those differences have important implications for how it should be handled.
The overlooked risk in cycling data
At See.Sense, we’ve explored this issue in depth and engaged with the UK’s Information Commissioner’s Office (ICO) to better understand the privacy implications of connected cycling data.
One of the key challenges is re-identification — the ability to link anonymised data back to an individual.
This risk exists in all mobility datasets. But in cycling, it can be significantly higher if not properly addressed.
Why cycling data behaves differently
There are several structural differences between cycling and motor traffic that increase the likelihood of re-identification.
1. Fewer users, more visible journeys
Cycling volumes are typically much lower than motor traffic.
This means individual journeys are more distinguishable, rather than being hidden within a continuous flow of vehicles.
2. More predictable travel patterns
Cyclists often follow highly regular routes and schedules.
This makes it easier to identify recurring patterns over time.
3. Greater exposure to external observation
Cyclists are more visible than drivers.
As a result, external sources such as cameras are more likely to capture identifiable characteristics that can be linked to movement data.
4. Single-user journeys
A bicycle is almost always associated with one individual.
Unlike cars, which may have multiple drivers, this creates a stronger link between a journey and a specific person.
5. More opportunities for cross-referencing
Cyclists frequently use fitness and social platforms such as Strava.
This creates additional datasets that could potentially be cross-referenced, increasing the risk of re-identification.
What the research shows
Research highlights how difficult true anonymisation can be in mobility datasets.
A study in Copenhagen found that using just four weeks of anonymised movement data from 587 individuals, AI models were able to correctly identify individuals 26.4% of the time.
This demonstrates how even anonymised datasets can contain identifiable patterns when analysed in sufficient depth.
This doesn’t mean cycling data shouldn’t be used
Cycling data has enormous potential to improve safety, infrastructure design and planning.
But it does mean that it needs to be handled differently.
Approaches designed for connected car data cannot simply be applied to cycling.
Instead, cities need to adopt a more considered approach — one that balances insight with privacy.
A better approach: insight without individual tracking
At See.Sense, we believe it is possible to generate deep, actionable insight from cycling data without tracking individuals.
This requires a different way of thinking about how data is collected and used.
In practice, this means focusing on three key principles:
1. Aggregated, not individual
The goal is to understand patterns across the network — not individual journeys.
By aggregating data, it becomes possible to identify trends such as risk hotspots or infrastructure issues without exposing personal movement patterns.
2. Contextual, not just positional
Understanding cycling requires more than knowing where people ride.
It requires insight into how they experience the network — for example through signals such as braking, swerving or surface roughness.
These types of signals provide meaningful insight into infrastructure performance without relying on identifiable tracking.
3. Purpose-driven, not open-ended
Cycling data should be collected and used with a clear purpose — such as improving safety or evaluating infrastructure.
This ensures that data collection remains focused, proportionate and aligned with public interest.
Closing the gap between insight and trust
As cycling becomes a more important part of urban mobility, cities need better data to support decision-making.
But they also need to maintain public trust.
The challenge is not choosing between insight and privacy.
It is designing systems that deliver both.
By focusing on aggregated, contextual and purpose-driven data, it is possible to unlock the value of cycling data while handling it responsibly.
Closing line
Because better cycling decisions don’t require tracking people — they require understanding experience.