This presentation is delivered by See.Sense, leading micro-mobility technology and data SME, based in County Down, that is on a mission to make cycling better. They will discuss how better cycling data can help planners and cities rapidly improve conditions for cycling, thereby enabling more people to cycle.

The presentation will provide an overview of challenges currently faced by local authorities gathering cycling data. It will then provide a short overview of the technology developed by See.Sense, describing how their patented sensor and AI-fusion technology, is embedded in their cycling technology products, providing GNSS/sensor telemetry capabilities that make riding safer, and cities smarter. Data gathered includes location/time, swerving/braking, road surface conditions. Data is gathered either by embedding their tech in bike share or e-scooter schemes, or through their award-winning intelligent and connected bike lights on private citizens/ bikes that are used by more than 100,000 cyclists worldwide.

The second part of the presentation will focus on applications for the granular sensor data See.Sense gathers, namely:

• Data helping to design optimal cycling networks: Popular routes/speeds, dwell time and congestion, pedestrian conflict areas.
• Safety analysis: Swerving and braking data collates with stats 19 collision data but also highlights where near miss incidents are happening providing a leading indicator on risky areas for cyclists. See.Sense will share results from their study with RoSPA in Birmingham.
• Road surface conditions: a mapping of road surface bumpiness that builds an understanding of where there is need to make repairs to manage comfort and safety for cyclists. See.Sense will share results from their project with Smart Dublin and AECOM on this.
• Evidence cycle infrastructure performance: Data is able to provide before and after comparisons not only in terms of popularity, but also for safety and comfort of the rider.
• Understanding diverse needs: anonymised profile data including age and gender allows analysis of differences in experiences.
• Furthermore, the platform collects user survey data that can provide detailed understanding of user perceptions, experiences and requests.

Lastly, the presentation will conclude by exploring the possibility of combining See.Sense data with other data sets from both cycling, e-scooters, cargo bikes, as well as car data, to elicit powerful insights into mobility in cities.