Crowdsourced data for bicycling research and practice. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Nelson, Trisalyn; Ferster, Colin; Laberee, Karen; Fuller, Daniel; Winters, Meghan
• Is Part Of: Transport reviews. Volume 41:Issue 1(2021)
• Page Start: 97
• Page End: 114
• Publication Date: 2021-01-02
• Subjects: Crowdsourced -- bicycling -- exposure -- safety -- infrastructure -- attitudes; Transportation -- Periodicals; Transportation engineering -- Periodicals; 380.5
• DOI: 10.1080/01441647.2020.1806943
• Languages: English
• ISSNs: 0144-1647

Abstract

ABSTRACT : Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.