Diffusion behavior in a docked bike-sharing system

This paper examines bike diffusion behavior in a docked bike-sharing system in Chicago. The analysis is based on an analogy between the movement of shared bikes and the transmission of certain information on internet or the spreading of epidemics among humans By mining a bike trip data set collected in the city, we find that (1) the distribution of bike trip distance peaks between 0.8 and 2 km, and beyond 6.3 km, it follows a strong power law; (2) the diffusion intensity of a community is affected positively by the number of incoming bike trips and rebalancing actions, and negatively by the percentage of inner-community trips. The effect of the rebalancing actions is roughly twice as strong as that of the incoming bike trips; (3) both the diffusion range of a bike and the number of rebalancing actions it receives are strong predictors of its use. Reaching one more community will produce about 14 more trips and an additional rebalancing action contributes about 8.6; and (4) even the most active bikes could only reach about 75% of all communities in Chicago. The last finding helps identify a cluster of communities poorly connected with the rest of the city by bike travel. Interestingly, these isolated communities are strongly correlated with the areas of the city that have high concentration of African American population, low-income households and homicide crimes.