Sponsor: U.S. Department of Transportation, Pacific Southwest Region 9 UTC
PI: Chun-Hsing Ho
Co-PIs: Brendan J. Russo and Steven R. Gehrke
This research project developed an instrumented bike with a sensor logger, a video device (e.g., GoPro), a mobile app, and a cloud server/website to detect real-time quality of cycling infrastructure systems (bike trails, sidewalks, pedestrian pathways, etc.). The instrumented bike is capable of immediately sharing the information with cyclists (road users) and governments/authorities (road managers) such that (1) cyclists (road users) will be aware of upcoming potential hazards prior to cycling and be able to adjust their cycling route accordingly and (2) governments (road managers) will be able to more effectively prioritize their maintenance needs. A computing algorithm using the sliding window method was developed in support of the instrumented bike’s development. Demonstrated in field testing, the sliding window computing algorithm was capable of analyzing vibration patterns and identifying potential hazards (potholes, bumps, uneven surface, cracks, etc.) through multiple cyclists. This research project introduced an instrumented bike to the cycling community and agencies with a goal to provide “smart wheels” for day-to-day cycling operations, improve bike efficiency, safety, and mobility, promote cycling activities, and reduce emissions.