In　this　paper,　we　investigate　a　new　stochastic　lane　reservation　problem　under　uncertain　road　travel　times.　The　problem　needs　to　determine　an　optimal　location　of　reserved　lanes　in　a　transportation　network　and　design　reserved　lanes-based　routes　for　special　time-crucial　transport　tasks　under　the　condition　that　the　travel　time　is　uncertain,　but　partial　information,　i.e.,　mean　and　covariance　matrix　are　known.　For　the　problem,　we　develop　a　service-oriented　distributionally　robust　optimization　model.　The　objective　is　to　maximize　the　service　satisfaction,　which　is　measured　by　the　probability　of　completing　the　tasks　on　time.　To　solve　it,　the　widely　used　sample　average　approximation　(SAA)　method　is　first　adapted.　However,　the　SAA　method　is　time-consuming　to　the　NP-hardness　of　the　problem.　Thus,　by　analyzing　the　characteristics　of　the　problem,　we　propose　a　new　method　based　on　approximate　mixed　integer　second-order　cone　programming　(MI-SOCP).　The　efficiency　and　effectiveness　of　the　proposed　method　are　verified　by　the　results　of　a　real　case　as　compared　with　the　SAA　method.　©　2022　IEEE.