Suggested Citation:
Qian, Xiaodong, Jason Moore, Deb Niemeier (2020)

Predicting Bicycle Pavement Ride Quality: Sensor-Based Statistical Model

Bicycle paths or even bicycle lanes have not emerged as key priorities in traditional pavement systems analysis. Most cities rely on route preferences (e.g., common school routes) or visual checks to prioritize pavement conditions on bicycle facilities. We used 31 bike path sections with a representative range of pavement surface conditions to collect acceleration data, GPS location data, bicycle steering angle, surface displacement data, and mean texture depth (MTD) data. We also recruited cyclists to complete a post-ride survey on ride quality. Using these data, we specified two ordered logit regression models to separately examine the relationships between bicycle ride quality and traditional pavement roughness measurement (or surface defect density on trajectories) while holding other parameters (e.g., bicycle accelerations and steering angle) constant. Our study shows that a surface defect index can replace the MTD test for bicycle facilities and can produce better performance in predicting ride quality, especially when pavement condition needs moderate repair to avoid becoming much worse. We also examine ride quality, specifically the vertical acceleration effect on ride experience, for different types of bicycles (e.g., a mountain bike with a suspension system versus a touring bike).