The　travel　time　prediction　of　vehicles　is　an　important　part　of　intelligent　expressways.　It　can　not　only　provide　the　vehicle　distribution　trend　of　each　section　for　the　expressway　management　department　to　assist　the　fine　management　of　the　expressway,　but　it　can　also　provide　owners　with　dynamic　and　accurate　travel　time　prediction　services　to　assist　the　owners　to　formulate　more　reasonable　travel　plans.　However,　there　are　still　some　problems　in　the　current　travel　time　prediction　research　(e.g.,　different　types　of　vehicles　are　not　processed　separately,　the　proximity　of　the　road　network　is　not　considered,　and　the　capture　of　important　information　in　the　spatial-temporal　perspective　is　not　considered　in　depth).　In　this　paper,　we　propose　a　Multi-View　Travel　Time　Prediction　(MVPPT)　model.　First,　the　travel　times　of　different　types　of　vehicles　of　each　section　in　the　expressway　are　analyzed,　and　the　main　differences　in　the　travel　times　of　different　types　of　vehicles　are　obtained.　Second,　multiple　travel　time　features　are　constructed,　which　include　a　novel　spatial　proximity　feature.　On　this　basis,　we　use　CNN　to　capture　the　spatial　correlation　and　the　spatial　attention　mechanism　to　capture　key　information,　the　BiLSTM　to　capture　the　time　correlation　of　time　series,　and　the　time　attention　mechanism　capture　key　time　information.　Experiments　on　large-scale　real　traffic　data　demonstrate　the　effectiveness　of　our　proposal　over　state-of-the-art　methods.