Open AccessBike‐sharing systems with a dual selection mechanism and a dynamic double‐threshold repositioning policy
Author(s) -
Fan RuiNa,
Ma FanQi
Publication year - 2021
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/itr2.12056
Subject(s) - dual (grammatical number) , selection (genetic algorithm) , mechanism (biology) , computer science , measure (data warehouse) , computability , set (abstract data type) , mathematical optimization , artificial intelligence , data mining , algorithm , mathematics , art , philosophy , literature , epistemology , programming language
Abstract Bike‐sharing systems have gained considerable attention with the tide of sharing economy. This paper proposes a dual selection mechanism and a dynamic double‐threshold repositioning policy to reduce problematic stations. The selection mechanism is a preventive self‐balancing measure, which is carried out before problematic stations appear. To prove the effectiveness of this selection mechanism, a system of differential equations is set up, the steady‐state probabilities of the bike‐sharing system are computed, and numerical experiments to verify the applicability and computability of the computational method are provided. The method providd in this study contributes to improving the efficiency of the bike‐sharing by self‐balancing measure.