  Bike-sharing systems have been regarded as a critical component of solutions
towards the transition to greener and more sustainable transportation, with the
benefits of reducing carbon emissions, improving public health, and mitigating
congestion by replacing short-distance motorized trips. Due to better
accessibility and usage flexibility, newly emergent dockless sharing bikes have
become quite popular and are reviving the fashion of cycling in cities. Urban
congestion is simultaneously influenced by heterogeneous saptio-temporal travel
demands, topology and spatial characteristics of road networks, and the
interplay between travel modes. In this paper, by considering aforementioned
factors, we discover a robust sublinear scaling relation between the level of
congestion for vehicles and the detour ratio weighted by biking traffic, which
is intriguing given the fact that congestion and detour ratio is linearly
independent. Such a scaling relation implies a strong interplay between vehicle
traffic and cycling activities and can be applied in predictions for congestion
or aggregated to more sophisticated traffic models. In addition,
biking-traffic-weighted detour ratio can be applied to detect inefficient
routes, which would help alleviate urban congestion, make better urban
planning, and improve transportation efficiency and equity in cities.
