In　a　prolonged　transitional　period,　the　urban　transportation　infrastructure　is　expected　to　accommodate　two　different　types　of　vehicles.　One　type　of　vehicle　is　the　human-driven　vehicle　(HV)　and　the　other　is　the　connected　and　autonomous　vehicle　(CAV).　Nevertheless,　the　conflict　between　HVs　and　CAVs　in　the　mixed　traffic　scenario　significantly　impedes　the　efficiency-　improvement　benefit　of　implementing　emerging　CAV　technologies.　To　harness　the　full　potential　of　CAVs　in　enhancing　traffic　efficiency　and　network　performance,　multi-type　lanes　(i.e.,　regular　lanes,　dedicated　CAV　lanes,　and　CAV/toll　lanes)　and　multi-type　intersections　(i.e.,　conventional　signalized　intersections,　novel　signalized　intersections　with　an　exclusive　phase　and　exclusive　approaches,　and　smart　signal-free　intersections)　are　proposed　to　efficiently　manage　the　heterogeneous　traffic　flow　on　roads　and　at　intersections,　respectively.　From　the　perspective　of　traffic　planners,　in　this　research,　the　integrated　planning　problem　of　multi-type　lanes　and　intersections　(IPPLI　for　short)　in　the　mixed　transportation　network　is　suggested　and　tackled,　where　the　route　selection　behavior　and　the　cross-group　externalities　of　heterogeneous　travelers　are　considered　according　to　the　user　equilibrium　principle.　It　aims　to　minimize　the　overall　travel　cost　by　making　decisions　on　the　spatial　layout　of　multi-type　lanes　and　intersections　in　the　network,　the　toll　level　of　CAV/toll　lanes,　the　number　of　exclusive　approaches　at　novel　signalized　intersections,　time　intervals　of　the　cycle　and　green　signal　for　each　phase　at　both　conventional　and　novel　signalized　intersections.　Then,　the　IPPLI　is　formulated　as　a　mixed-integer　nonlinear　programming　model　based　on　the　link-node　modeling　method　without　time-consuming　path　enumeration　and　memory-consuming　path　storage.　As　a　mathematical　problem　with　complementarity　constraints,　it　is　solved　by　an　improved　evolutionary　algorithm-based　approach,　which　consists　of　two　modules　cooperating　with　each　other.　After　introducing　the　concept　of　the　accessibility　of　HVs,　a　heuristic　technique　is　proposed　to　accelerate　algorithm　convergence　by　continuously　repairing　unreasonable　solutions.　Finally,　experiments　are　performed　on　two　distinct　networks　to　showcase　the　properties　of　the　problem　and　assess　the　effectiveness　of　the　proposed　model.　Experimental　results　show　that　the　proposed　model　consistently　performs　outstandingly　across　a　range　of　CAV　penetration　rates.　Our　model　achieves　maximum　improvements　of　25.71%　and　4.84%　in　reducing　travel　costs　compared　to　models　that　only　plan　multi-type　lanes　and　multi-type　intersections,　respectively.　Additionally,　the　improved　evolutionary　algorithm-based　approach　reduces　the　convergence　time　by　20.51%　and　26.81%　compared　to　the　classical