Abstract
With the rapid development of sharing bicycles, unreasonable dispatching methods are likely to cause a series of issues, such as resource waste and traffic congestion in the city. In this paper, a new dynamic scheduling method is proposed, named Tri-G, so as to solve the above problems. First of all, the whole visualization information of bike stations was built based on a Spatio-Temporal Graph (STG), then Gaussian Mixture Mode (GMM) was used to group individual stations into clusters according to their geographical locations and transition patterns, and the Gradient Boosting Regression Tree (GBRT) algorithm was adopted to predict the number of bikes inflow/outflow at each station in real time. This paper used New York’s bicycle commute data to build global STG visualization information to evaluate Tri-G. Finally, it is concluded that Tri-G is superior to the methods in control groups, which can be applied to various geographical scenarios. In addition, this paper also discovered some human mobility patterns as well as some rules, which are helpful for governments to improve urban planning.
Highlights
Bike-sharing refers to the provision of short-term bicycle rental services in unattended urban locations
The Gradient Boosting Regression Tree (GBRT) will be compared with two common methods, HA and ARMA
Based on GC and BC, with the RMLSE measurement method, it is found that the errors of prediction accuracy decreased by 1.48% on average compared with GC and decreased by 0.1% compared with BC
Summary
Bike-sharing refers to the provision of short-term bicycle rental services in unattended urban locations. To rent a bicycle in China, one uses a mobile app to scan the QR code on the bike, and any of the millions of bikes scattered on the sidewalks can be used by the users. China’s billion-dollar bike-sharing revolution has already transformed the look and feel of cities around the country, with more than 100 million apps downloaded and billions of rides taken on many millions of bikes. In Beijing, there are 700,000 shared bikes and 11 million registered users, which is nearly half the capital’s population. Compared with New York, New York’s Citi Bike has 10,000 bikes and 236,000 subscribers, which is the largest operation in the United States [1]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
