Effective　bus　travel　time　prediction　is　essential　in　transit　operation　system.　An　improved　support　vector　machine　(SVM)　is　applied　in　this　paper　to　predict　bus　travel　time　and　then　the　efficiency　of　the　improved　SVM　is　checked.　The　improved　SVM　is　the　combination　of　traditional　SVM,　Grubbs＇　test　method　and　an　adaptive　algorithm　for　bus　travel-time　prediction.　Since　error　data　exists　in　the　collected　data,　Grubbs＇　test　method　is　used　for　removing　outliers　from　input　data　before　applying　the　traditional　SVM　model.　Besides,　to　decrease　the　influence　of　the　historical　data　in　different　stages　on　the　forecast　result　of　the　traditional　SVM,　an　adaptive　algorithm　is　adopted　to　dynamically　decrease　the　forecast　error.　Finally,　the　proposed　approach　is　tested　with　the　data　of　No.　232　bus　route　in　Shenyang.　The　results　show　that　the　improved　SVM　has　good　prediction　accuracy　and　practicality.