Crowdsourced cycling data applications to estimate noise pollution exposure during urban cycling.

This research demonstrates a methodology to integrate freely available datasets to understand the relationship between road traffic noise and cycling experiences in a medium sized city. An illustrative example of the methodology was drawn from data for Dublin, Ireland. We aggregate local environmental data with 81,403 Strava cycle trips, contextualised by feedback from 335 cyclists to estimate exposure levels and infer impacts on experiences and behaviours. Results demonstrate that cyclists recognise that they are subjected to increased noise levels and experience negative psychophysical consequences as a result, but they tend to downplay the impact of noise as merely a minor annoyance. Noise also impacts behaviour, most noticeably through temporal and spatial detours. Geospatial mapping was used to visualise the relationship between noise pollution and cycling activity. Estimating traffic noise levels across two cycle routes, direct vs popular detour, revealed a +10 dB(A) increase in exposure for a saving of approximately 4 min on the direct route compared to the detour. Spatial inequities in exposure levels may have serious health consequences for cyclists in a city such as Dublin. The methodology is demonstrated as suitable for policy level interventions and planning purposes.