Cyclists as Intelligent Carriers of Space-Time Environmental Information: Crowd-Sourced Sensor Data for Local Air Quality Measurement and Mobility Analysis in the Netherlands

Kourtit K., Nijkamp P., Osth J., TÜRK U.

Journal of Urban Technology, vol.31, no.1, pp.73-91, 2024 (SSCI) identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.1080/10630732.2023.2177954
  • Journal Name: Journal of Urban Technology
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, IBZ Online, Aerospace Database, Applied Science & Technology Source, Avery, Computer & Applied Sciences, Educational research abstracts (ERA), Geobase, Public Affairs Index
  • Page Numbers: pp.73-91
  • Keywords: air quality, bicycle, crowd-sourced data, kernel density, mobility pattern, sensor, snifferbike, surface model, “bikeability” index
  • Abdullah Gül University Affiliated: Yes


In recent years, slow travel modes (walking, cycling) have gained much interest in the context of urban air quality management. This article presents the findings from a novel air quality measurement experiment in the Netherlands, by regarding cyclists as carriers and transmitters of real-world information on fine-grained air quality conditions. Using individual sensors on bicycles—connected to a GPS positioning system—online local pollution information originating from cyclists’ detailed spatial mobility patterns is obtained. Such air quality surface maps and cyclists’ mobility maps are then used to identify whether there are significant differences between the actual route choice and the cyclists’ shortest route choice, so as to identify the implications of poor air quality conditions for their mobility choices. Thus, the article seeks to present both a detailed pollution surface map and the complex space-time mobility patterns of cyclists in a region, on the basis of online quantitative data—at any point in time and space—from bicycle users in a given locality. In addition, the article estimates their response—in terms of route choice—to detailed air-quality information through the use of a novel geoscience-inspired analysis of space-time “big data.” The empirical test of our quantitative modeling approach was carried out for the Greater Utrecht area in the Netherlands. Our findings confirm that spatial concentration of air pollutants have great consequences for bike users’ route choice patterns, especially in the case of non-commuting trips. We also find that cyclists make longer trips on weekends and in the evenings, especially towards parks and natural amenities.