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Related Concept Videos

  1. Home
  3. Crowdsourced Cycling Data Applications To Estimate Noise Pollution Exposure During Urban Cycling.
  1. Home
  3. Crowdsourced Cycling Data Applications To Estimate Noise Pollution Exposure During Urban Cycling.

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

Rebecca Wogan1, John Kennedy1

  • 1Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, University of Dublin, D02 PN40 Dublin, Ireland.

Heliyon
|March 27, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Cyclists experience negative impacts from road traffic noise, altering their routes. This study presents a method to map noise exposure and its effects on urban cycling behavior.

Keywords:
Cyclist behaviorsCyclist experiencesEnvironmental health dataNoise pollution exposureStrava

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Area of Science:

  • Environmental Science
  • Urban Planning
  • Transportation Studies

Background:

  • Road traffic noise is a significant urban pollutant.
  • Understanding its impact on non-motorized transport is crucial for sustainable cities.
  • Cycling experiences are influenced by environmental factors, including noise.

Purpose of the Study:

  • To develop and demonstrate a methodology for integrating open datasets to analyze road traffic noise and cycling experiences.
  • To quantify noise exposure and its effects on cyclist behavior and well-being in a medium-sized city.
  • To inform urban planning and policy interventions for healthier urban environments.

Main Methods:

  • Integration of freely available environmental data with 81,403 Strava cycle trips.
  • Collection of cyclist feedback from 335 participants to contextualize trip data.
  • Geospatial mapping to visualize noise pollution and cycling activity relationships.
  • Estimation of traffic noise levels for direct versus detour routes.

    • Main Results:

    • Cyclists recognize increased noise exposure and associated negative psychophysical consequences, though often perceive it as minor.
    • Noise pollution influences cycling behavior, leading to temporal and spatial detours.
    • A +10 dB(A) noise exposure increase was observed on a direct route compared to a popular detour, saving approximately 4 minutes.
    • Significant spatial inequities in noise exposure levels were identified.

    Conclusions:

    • The demonstrated methodology effectively integrates diverse datasets to assess traffic noise impacts on urban cycling.
    • Noise pollution significantly affects cyclist behavior and well-being, with potential serious health consequences due to spatial inequities.
    • The methodology is suitable for policy-making and urban planning to mitigate noise impacts and improve cycling safety and experience.