Most　existing　lane　reservation　studies　usually　consider　a　static　transportation　network　with　assuming　constant　link　travel　times.　However,　in　reality　the　link　travel　times　are　highly　uncertain　due　to　various　factors　such　as　weather,　accidents,　road　maintenance,　intersections,　etc.　Moreover,　the　precise　link　travel　time　probability　distribution　is　usually　difficult　to　be　obtained.　This　paper　studies　a　new　stochastic　bus　lane　reservation　problem　with　partial　link　travel　time　information,　i.e.,　only　the　mean　and　covariance　matrix　are　known.　The　objective　is　to　maximize　the　bus　service　level　measured　by　the　probability　of　the　event　that　all　lines　are　jointly　scheduled　on　time.　For　the　problem,　we　formulate　a　service-oriented　distributionally　robust　optimization　model.　Its　complexity　is　shown　to　be　NP-hard.　To　solve　the　problem,　a　sample　average　approximation　(SAA)-based　method　is　first　adapted.　Since　the　SAA-based　approach　is　computational　expensive,　a　new　approximated　mixed　integer　second-order　cone　programming　(MI-SOCP)-based　approach　is　developed.　Computational　results　on　a　real-life　case　show　that　the　proposed　MI-SOCP-based　approach　can　efficiently　obtain　satisfactory　solutions　of　high　quality.　Besides,　our　results　indicate　that　the　proposed　model　and　algorithm　can　provide　better　solutions　with　higher　service　level,　as　compared　with　general　stochastic　models　with　known　distributions　and　without　considering　service　levels.