Abstract
With the development of mobile communication and global positioning system navigation and positioning technology, analysis of user behavior on mobile Internet has become a hot topic in research area. Sign in sharing-bicycles’ app, find bicycle location, and selected has become a part of mobile Internet user’s daily life. Based on the data analysis of the spatial and temporal characteristics, find out sharing-bicycle’s user behavior obeys power-law distribution. In the time interval, user behavior of sharing-bicycle has strong intermittency and weak memory; the exponent of probability that K edge nodes is three by fitting the distance of sharing-bicycle’s data curve. It is verified that mobile Internet is long to scale-free networks. We conclude seven characteristics of user’s behavior of sharing-bicycle in mobile Internet application from experimental results. The analysis of sharing-bicycle’s behavior has become a complement and extension in human dynamics research field.
Highlights
In 2005, scholar Barabasi publish paper on Nature journal, he found human dynamic theory that the time intervals of human activities obey power-law distribution
Time interval of events shows non-Poisson statistical characteristics be long to human dynamic field
Zhou Tao summed up the latest research results that behavior interval obeys the power-law distribution approximately; the power exponent is between 1 and 3; it shows the feature of short bursts and long-term dormancy
Summary
In 2005, scholar Barabasi publish paper on Nature journal, he found human dynamic theory that the time intervals of human activities obey power-law distribution. Celebrity letter,[1] Online movie on demand,[2] Music channel acceptance,[3] content of GitHub, or Android release and response in forum.[4,5] In the above studies, time interval of events shows non-Poisson statistical characteristics be long to human dynamic field. Zhou Tao summed up the latest research results that behavior interval obeys the power-law distribution approximately; the power exponent is between 1 and 3; it shows the feature of short bursts and long-term dormancy. The behavior of human use of mobile Internet products exhibits the power-law distribution of non-Poisson characteristics. Gibrat’s law in Economics,[8,9] Taylor’s law in wave analysis,[10] and Fractal feature extraction method by analysis of time series[11] can describe the characteristics of user’s behavior
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
