Prediction　of　bus　arrival　time　is　an　important　part　of　intelligent　transportation　systems.　Accurate　prediction　can　help　passengers　make　travel　plans　and　improve　travel　efficiency.　Given　the　nonlinearity,　randomness,　and　complexity　of　bus　arrival　time,　this　paper　proposes　the　use　of　a　wavelet　neural　network　(WNN)　model　with　an　improved　particle　swarm　optimization　algorithm　(IPSO)　that　replaces　the　gradient　descent　method.　The　proposed　IPSO-WNN　model　overcomes　the　limitations　of　the　gradient-based　WNN　which　can　easily　produce　local　optimum　solutions　and　stop　the　training　process　and　thus　improves　prediction　accuracy.　Application　of　the　model　is　illustrated　using　operational　data　of　an　actual　bus　line.　The　results　show　that　the　proposed　model　is　capable　of　accurately　predicting　bus　arrival　time,　where　the　root-mean　square　error　and　the　maximum　relative　error　were　reduced　by　42%　and　49%,　respectively.