Accurate　travel　time　prediction　(TTP)　is　a　significant　aspect　in　the　intelligent　transportation　system　(ITS).　Travel　times　of　certain　road　segments　explicitly　reflect　the　traffic　conditions　of　those　sections.　Effective　TTP　of　road　segments　is　instrumental　in　route　planning,　traffic　control,　and　traffic　management.　However,　the　accuracy　of　TTP　is　greatly　affected　by　the　intricate　topological　structure　of　traffic　network　and　the　dynamics　of　traffic　flow　over　time.　This　paper　develops　a　TTP　method　based　on　the　spatial-feature-based　hierarchical　clustering　(SFHC)　and　deep　multi-input　gated　recurrent　unit　(DMGRU).　The　proposed　two-stage　method　is　capable　of　capturing　the　spatial-temporal　features　of　traffic　network.　Specifically,　the　SFHC　divides　the　road　segments　into　several　clusters　having　similar　traffic　features,　and　then　the　clustered　data　is　fed　into　the　DMGRU　for　TTP.　Our　experiments　conducted　on　the　practical　dataset　demonstrate　that　the　designed　prediction　method　can　achieve　the　mean　absolute　percentage　error　(MAPE)　of　3.3109%　and　mean　absolute　error　(MAE)　of　2.5658,　which　outperform　various　combinations　of　baseline　clustering　algorithms　and　prediction　models.