It　is　widely　recognized　that　human-driven　vehicles　(HVs)　and　connected　and　autonomous　vehicles　(CAVs)　are　expected　to　coexist　and　share　the　urban　traffic　infrastructure　in　the　transportation　network　for　a　long　time.　To　fully　utilizes　CAVs＇　potential　to　reduce　congestion　in　the　transitional　period,　this　study　proposes　and　addresses　the　intersection　design　and　signal　setting　problem　in　the　transportation　network　with　mixed　HVs　and　CAVs.　Due　to　the　difference　in　terms　of　communication　technology　and　autonomous　driving　technology　for　HVs　and　CAVs,　three　types　of　intersections　have　been　proposed　to　amplify　the　efficiency-improvement　benefit　from　CAVs　by　separating　CAVs　from　HVs　in　a　temporal　or　local-spatial　dimension:　the　conventional　signalized　intersection,　the　novel　signalized　intersection　with　a　dedicated　CAV　phase　and　dedicated　CAV　approaches,　and　the　intelligent　signal-free　intersection.　The　problem　is　to　determine　the　spatial　layout　of　different　types　of　intersections　in　the　transportation　network,　the　cycle　time,　and　green　time　duration　for　each　phase　of　signalized　intersections　that　minimize　the　total　travel　cost,　in　which　the　route　choice　behavior　of　heterogeneous　travelers　has　been　respected　based　on　the　user　equilibrium　principle.　A　mixed-integer　nonlinear　programming　model　is　developed　to　formulate　the　proposed　intersection　design　and　signal　setting　problem　based　on　the　link-node　modeling　method,　in　which　the　path　enumeration　is　avoided.　Then,　by　employing　various　linearization　techniques　(e.g.,　disjunctive　constraints,　logarithmic　transformation,　piecewise　linearization　with　logarithmic-sized　binary　variables　and　constraints,　outer-approximation　technique),　the　proposed　model　can　be　further　transformed　into　a　relaxed　sub-problem　in　the　form　of　mixed-integer　linear　programming.　A　globally　optimal　solution　algorithm　embedding　with　solving　a　sequence　of　relaxed　sub-problems　and　nonlinear　mixed　complementarity　problems　is　proposed　to　converge　to　a　global　optimum.　The　results　of　numerical　experiments　illustrate　that　the　proposed　methodology　can　significantly　improve　the　performance　of　the　whole　network.　Moreover,　it　consistently　outperforms　the　optimization　model　considering　only　conventional　signalized　intersections　under　various　CAV　market　penetration　rates.