Real-time　traffic　status　information　provides　good　references　for　urban　traffic　control　and　management.　Travel　time　is　easy　to　understand　and　widely　employed　in　representing　traffic　status.　With　significantly　improved　positioning　accuracy　and　coverage,　trajectory　data　collected　from　GPS-equipped　probe　vehicles　have　great　potential　for　traffic　state　recognition.　This　paper　presents　a　machine　learning　enabled　travel　time　estimation　method　based　on　the　GPS-equipped　probe　vehicles　data.　This　research　considers　the　spatial-temporal　relevancy　while　solving　the　travel　time　allocation　problem:　the　travel　time　of　target　segment　might　be　associated　with　its　previous　travel　times　and/or　the　traffic　states　of　nearby　relevant　segments.　After　data　normalization　and　network　clustering,　an　artificial　neural　network　(ANN)　algorithm　considering　such　spatial-temporal　relevancy　was　conducted　to　infer　the　travel　time　distribution　among　the　traveled　segments　within　one　path.　Furthermore,　a　weighted　summation　of　the　travel　time　estimation　result　from　various　trajectories　was　calculated　to　better　represent　the　segment　travel　time　in　one　time　step.　The　proposed　method　was　evaluated　by　evaluating　the　estimation　results　with　automatic　vehicle　identification　obtained　ground　truth.　The　experimental　results　illustrated　that　by　utilizing　the　ANN　to　consider　the　spatial-temporal　relevancy,　the　proposed　method　is　effective　and　efficient　in　estimating　the　travel　time.