The　bike　sharing　systems　are　fast　increasing　as　a　public　transport　mode　in　urban　short　t　ri　ps,　and　have　been　devel　oped　i　n　many　maj　or　ci　t　i　es　around　t　he　worl　d.　A　maj　or　challenge　in　the　study　of　bike　sharing　systems　is　that　some　large-scale　and　complex　queueing　networks　have　to　be　applied　through　multi-dimensional　Markov　processes,　while　their　discussion　always　suffers　a　common　difficulty:　State　space　explosion.　For　this　reason,　this　paper　provides　a　mean-field　computational　method　to　study　such　a　large-scale　bike　sharing　system.　Our　mean-field　computation　is　established　in　the　following　three　steps:　Firstly,　a　multi-dimensional　Markov　process　is　set　up　for　expressing　the　states　of　the　bike　sharing　system,　and　the　empirical　measure　process　of　the　multi-dimensional　Markov　process　is　given　to　partly　overcome　the　difficulty　of　state　space　explosion.　Based　on　this,　the　mean-field　equations　are　derived　by　means　of　a　virtual　time-inhomogeneous　M(t)　/　M(t)　/　1　/　K　queue　whose　arrival　and　service　rates　are　determined　by　using　some　mean-field　computation.　Secondly,　the　martingale　limit　is　employed　to　investigate　the　limiting　behavior　of　the　empirical　measure　process,　the　fixed　point　is　proved　to　be　unique　so　that　it　can　be　computed　by　means　of　a　nonlinear　birth-death　process,　the　asymptotic　independence　of　this　system　is　discussed,　and　specifically,　these　lead　to　numerical　computation　for　the　steady-state　probability　of　the　problematic　(empty　or　full)　stations.　Finally,　some　numerical　examples　are　given　for　valuable　observation　on　how　the　steady-state　probability　of　the　problematic　stations　depends　on　some　crucial　parameters　of　the　bike　sharing　system.　©　PU　2018.