In　recent　years,　intelligent　transportation　system　(ITS)　techniques　have　been　widely　exploited　to　enhance　the　quality　of　public　services.　As　one　of　the　worldwide　leaders　in　recycling,　Taiwan　adopts　the　waste　collection　and　disposal　policy　named　＂trash　doesn＇t　touch　the　ground＂,　which　requires　the　public　to　deliver　garbage　directly　to　the　collection　points　for　awaiting　garbage　collection.　This　study　develops　a　travel　time　prediction　system　based　on　data　clustering　for　providing　real-time　information　on　the　arrival　time　of　waste　collection　vehicle　(WCV).　The　developed　system　consists　of　mobile　devices　(MDs),　on-board　units　(OBUs),　a　fleet　management　server　(FMS),　and　a　data　analysis　server　(DAS).　A　travel　time　prediction　model　utilizing　the　adaptive-based　clustering　technique　coupled　with　a　data　feature　selection　procedure　is　devised　and　embedded　in　the　DAS.　While　receiving　inquiries　from　users＇　MDs　and　relevant　data　from　WCVs＇　OBUs　through　the　FMS,　the　DAS　performs　the　devised　model　to　yield　the　predicted　arrival　time　of　WCV.　Our　experiment　result　demonstrates　that　the　proposed　prediction　model　achieves　an　accuracy　rate　of　75.0%　and　outperforms　the　reference　linear　regression　method　and　neural　network　technique,　the　accuracy　rates　of　which　are　14.7%　and　27.6%,　respectively.　The　developed　system　is　effective　as　well　as　efficient　and　has　gone　online.