Bus　arrival　time　prediction　intends　to　improve　the　level　of　the　services　provided　by　transportation　agencies.　Intuitively,　many　stochastic　factors　affect　the　predictability　of　the　arrival　time,　e.g.,　weather　and　local　events.　Moreover,　the　arrival　time　prediction　for　a　current　station　is　closely　correlated　with　that　of　multiple　passed　stations.　Motivated　by　the　observations　above,　this　paper　proposes　to　exploit　the　long-range　dependencies　among　the　multiple　time　steps　for　bus　arrival　prediction　via　recurrent　neural　network　(RNN).　Concretely,　RNN　with　long　short-term　memory　block　is　used　to　＂correct＂　the　prediction　for　a　station　by　the　correlated　multiple　passed　stations.　During　the　correlation　among　multiple　stations,　one-hot　coding　is　introduced　to　fuse　heterogeneous　information　into　a　unified　vector　space.　Therefore,　the　proposed　framework　leverages　the　dynamic　measurements　(i.e.,　historical　trajectory　data)　and　the　static　observations　(i.e.,　statistics　of　the　infrastructure)　for　bus　arrival　time　prediction.　In　order　to　fairly　compare　with　the　state-of-the-art　methods,　to　the　best　of　our　knowledge,　we　have　released　the　largest　data　set　for　this　task.　The　experimental　results　demonstrate　the　superior　performances　of　our　approach　on　this　data　set.