Lane　reservation　optimization　is　important　in　intelligent　transportation　systems.　Most　existing　studies　are　carried　out　under　deterministic　road　conditions　by　assuming　constant　road　travel　time.　However,　road　conditions　vary　due　to　various　factors,　resulting　in　uncertain　road　travel　time.　This　work　addresses　a　new　reliability-based　lane　reservation　and　route　design　problem　by　considering　uncertain　road　travel　time　with　its　known　mean　and　covariance　matrix.　It　aims　to　decide　which　road　segments　in　a　network　should　implement　reserved　lanes　and　to　design　routes　for　special　time-crucial　transportation　tasks.　The　objective　is　to　maximize　transportation　service　reliability　(i.e.,　the　probability　of　completing　the　special　tasks　on　time).　For　this　problem,　a　novel　distributionally　robust　optimization　model　is　first　established.　To　solve　it,　this　work　proposes　i)　an　adapted　sample　average　approximation-based　approach　and　ii)　a　two-stage　hierarchical　heuristic　algorithm　based　on　second-order　cone　programming.　Experimental　results　on　an　illustrative　example　and　a　real-world　case　demonstrate　that　the　latter　is　more　effective　and　efficient　than　the　former.　In　addition,　we　conduct　a　series　of　parameter　sensitivity　analysis　experiments　to　reveal　the　factors　affecting　lane　reservation　and　provide　optimal　solutions　given　different　parameter　settings.