Travel　time　prediction　is　a　fundamental　part　of　traffic　analysis.　Meanwhile　it　affected　by　spatial　correlations,　temporal　dependencies,　external　conditions　(e.g.　weather,　meta　data,　traffic　conditions).　In　this　paper,　we　propose　a　deep　learning　framework　that　integrates　CNN　and　Bi-LSTM　to　learn　spatial-temporal　feature　representations　of　travel　time　prediction.　The　short-term　(5　minutes　interval)　historical　traffic　data　which　fully　utilize　to　capture　the　patterns　and　trend　of　the　travel　time.　Our　paper　sorted　the　feature　into　two　categories:　time-varying　attributes,　non-time-varying　attributes.　The　proposed　models　called　MV-FCL　were　evaluated　on　a　network　in　the　City　of　Zhangzhou,　China.　The　results　demonstrate　that　the　proposed　MV-FCL　model　outperform　state-of-art　baselines.　©　2022　SPIE.