To　establish　a　suitable　pure　electric　bus　arrival　time　prediction　model,　this　paper　takes　pure　electric　bus　as　the　research　object.　Based　on　the　analysis　of　the　influencing　factors　of　the　arrival　time　of　the　pure　electric　bus,　the　BP　neural　network　arrival　time　prediction　model　optimized　by　the　firefly　algorithm　(FA-BP　prediction　model)　is　established　by　selecting　vehicle　type,　SOC　value,　battery　age,　and　time　as　input　conditions.　The　model　is　trained　and　tested　by　using　bus　operation　data.　The　root　mean　square　error　of　the　Kalman　filter　model　is　0.351,　of　the　BP　neural　network　model　is　0.059,　and　of　the　FA-BP　prediction　model　is　0.04.　The　results　show　that　the　model　in　this　paper　effectively　improves　the　prediction　accuracy　and　has　good　reliability　and　feasibility.　It　can　provide　some　theoretical　references　for　pure　electric　bus　operation　and　managers　and　provide　some　basis　for　improving　bus　reliability.　　©　2023　Yuanwen　Lai　et　al.