Closing the Gap: Why Connected Cycling Data Matters More Than Ever

Cities around the world are investing heavily in cycling and micromobility — driven by climate targets, public health goals, and the need to make urban transport more efficient and inclusive. Yet despite this progress, many decisions about cycling infrastructure are still made using partial, outdated, or unrepresentative data.

To plan effectively, cities need more than counts and surveys. They need to understand who is cycling, how journeys actually unfold, and how different groups experience the network. This blog explores the current cycling data landscape, the critical gaps that remain, and how See.Sense is helping cities build a more complete, representative picture of everyday cycling and micromobility.

The Cycling Data Landscape Today

There is no shortage of cycling-related data. The challenge is that each source tells only part of the story.

1. Manual and Automatic Counters

What they provide: Counts of cyclists passing a fixed point.

Strengths:

• Useful for tracking long‑term trends at specific locations.
  
  

• Often well understood by transport authorities.
  
  

Limitations:

• Capture data only where installed.
  
  

• Provide no insight into journey quality, safety, or comfort.
  
  

• High deployment and ongoing maintenance costs limit coverage.
  
  

• Often represent snapshots, not continuous network-wide data.
  
  

2. Sensor Cameras at Junctions

What they provide: Movement and interaction data at intersections, including turning flows and some conflict detection.

Strengths:

• Valuable for understanding behaviour at complex junctions.
  
  

• Can highlight potential conflict points.
  
  

Limitations:

• Fixed to specific locations.
  
  

• Expensive to deploy and scale.
  
  

• Do not capture full journeys or broader network behaviour.
  
  

• Limited to what is visible within the camera frame.
  
  

3. Shared Micromobility Data

What it provides: Trip volumes, start/end points, and timing from shared bike and scooter fleets.

Strengths:

• Scalable where shared schemes operate.
  
  

• Useful for understanding adoption and general demand.
  
  

Limitations:

• Represents only a small proportion of total cycling and micromobility trips.
  
  

• Data structures vary between operators.
  
  

• Does not capture rider experience, stress, or surface conditions.
  
  

• Reflects where fleets are deployed, not necessarily where demand exists.
  
  

4. Fitness and Navigation Apps

What they provide: GPS-based route data from app users.

Strengths:

• Can highlight popular routes and corridors.
  
  

• Often provides large volumes of data.
  
  

Limitations:

• Strongly skewed toward sport and leisure use.
  
  

• Under-represents commuting, local trips, and risk-averse riders.
  
  

• Poor proxy for everyday cycling behaviour.
  
  

• Limited insight into safety or comfort.
  
  

5. Surveys and Citizen Engagement Tools

What they provide: Qualitative insights into perceptions, confidence, and barriers to cycling.

Strengths:

• Capture subjective experience and sentiment.
  
  

• Useful for understanding why people feel unsafe or avoid routes.
  
  

Limitations:

• Costly and time‑consuming to run well.
  
  

• Typically conducted infrequently.
  
  

• Often reflect a point in time, not ongoing behaviour.
  
  

• Sample sizes can be limited or unrepresentative.
  
  

6. Census and Household Travel Data

What it provides: High‑level information on mode share and commuting patterns, including where trips start and end.

Strengths:

• Useful for macro‑level planning and policy targets.
  
  

• Widely recognised and comparable across regions.
  
  

Limitations:

• Usually collected only once every 10 years.
  
  

• Focused primarily on commuting, not everyday local trips.
  
  

• Tends to under‑represent short, linked journeys (which are more commonly undertaken by women).
  
  

• Poor at capturing rapid behaviour change.
  
  

The Gaps: What’s Still Missing

Despite the breadth of available data, several critical gaps remain — particularly for cities aiming to design inclusive, people‑centred cycling networks.

🚫 Real Journey Experience

Most datasets cannot show how journeys feel — where riders brake sharply, swerve, encounter poor surfaces, or experience repeated delays.

🚫 Under‑Reporting of Safety Issues

Near misses and single‑bike incidents are widely under‑reported, despite making up a significant proportion of safety‑related events. These rarely appear in police or hospital data, leaving major blind spots.

🚫 Network‑Wide Behaviour

Without end‑to‑end journey data, it is difficult to understand avoidance behaviour, route confidence, or how cyclists move through the network as a whole.

🚫 Multi‑Modal Understanding

Cycling and micromobility networks increasingly include bikes, e‑bikes, cargo bikes, and e‑scooters — yet most datasets remain siloed by mode or operator.

🚫 Representative Data

Perhaps most critically, many cycling datasets are not representative:

• Fitness platforms skew male and sport‑oriented.
  
  

• Shared fleets reflect where services are deployed.
  
  

• Surveys often miss quieter, less confident riders.
  
  

This means the experiences of women, commuters, older riders, and local trip‑makers are frequently under‑represented — even though these groups are central to growing everyday cycling.

There Is No Silver Bullet — But There Is a Better Approach

No single dataset can answer every question. Effective cycling and micromobility planning requires layered insight, with a clear understanding of the strengths and weaknesses of each source.

What has been missing is a scalable way to capture real, representative journey experience across modes — and turn that data into something planners can actually use.

How See.Sense Fills the Data Gap

Standardised Data Across All Bikes and Micromobility

See.Sense provides a consistent, standardised approach to collecting anonymised sensor data across:

• Personal bikes
  
  

• E‑bikes
  
  

• Cargo bikes
  
  

• Shared fleet vehicles
  
  

• E‑scooters and other micromobility forms
  
  

This allows cities to compare experience and behaviour across modes, rather than analysing each in isolation.

Intentional, Representative Deployments

A key advantage of See.Sense is the ability to deploy devices intentionally, rather than relying solely on self‑selecting users.

This enables cities to:

• Target specific groups such as women, commuters, or cargo bike users.
  
  

• Focus on under‑represented neighbourhoods or corridors.
  
  

• Reduce sampling bias common in crowdsourced or app‑based datasets.
  
  

The result is more inclusive, more representative insight — supporting infrastructure decisions that work for everyone, not just the most confident riders.

Rich, Continuous Journey Data

See.Sense captures data throughout real journeys, including:

• Braking and swerving — indicators of stress or conflict.
  
  

• Surface roughness and vibration — highlighting comfort and maintenance issues.
  
  

• Speed, dwell, and delay patterns — revealing congestion and inefficiency.
  
  

• Route choice and avoidance — showing where people choose not to ride.
  
  

This provides a network‑wide view that static infrastructure cannot.

Turning Data into Insight with AI‑Powered Dashboards

Collecting data is only part of the challenge. Making sense of it is where value is created.

See.Sense’s AI‑powered dashboards are designed to help cities:

• Identify patterns and hotspots quickly.
  
  

• Compare behaviour across modes and user groups.
  
  

• Measure change before and after interventions.
  
  

• Translate complex datasets into clear, actionable insight.
  
  

The focus is on supporting everyday decision‑making — not overwhelming teams with raw data.

Complementing Existing Data Sources

See.Sense does not replace counters, surveys, or operator data. Instead, it adds the missing experiential and representative layer, helping cities understand not just:

How many people are cycling
but
who is cycling, how their journeys feel, and where investment will have the greatest impact.

Building Better Cycling Networks Starts with Better Data

If cycling and micromobility are to fulfil their potential, they need to be planned with the same rigour as other transport modes — using data that is continuous, representative, and meaningful.

At See.Sense, we’re helping cities move beyond partial snapshots toward a richer understanding of everyday journeys — enabling infrastructure that is safer, more inclusive, and more effective.

Learn More

Find out how See.Sense can support your cycling and micromobility strategy:

📨 Get in touch: sales@seesense.cc
🌍 Explore our data solutions: https://seesense.cc/pages/data
📖 Read our insights and case studies: https://seesense.cc/blogs/insights