This　study　investigates　lane-changing　strategy　optimization　for　connected　and　autonomous　vehicles　(CAVs)　in　heterogeneous　traffic　systems　to　enhance　traffic　efficiency　and　orderliness,　addressing　challenges　posed　by　heterogeneous　traffic　flows　and　variable　CAV　penetration　rates.　An　enhanced　Nagel-Schreckenberg　(NaSch)　model　integrated　adaptive　cruise　control　(ACC)　and　cooperative　adaptive　cruise　control　(CACC)　frameworks　to　simulate　heterogeneous　traffic.　Relative　entropy　(Kullback–Leibler　divergence)　served　as　a　novel　metric　for　quantifying　traffic　orderliness.　Two　innovative　lane-changing　strategies—Conservative　Aggregation　Strategy　(CSA)　and　Radical　Aggregation　Strategy　(RDA)—were　proposed,　alongside　a　Lane　Type　Aggregation　(LTA)　approach　for　dedicated　CAV　lanes.　CSA　increased　road　capacity　by　12.6%,　while　RDA　achieved　a　14.0%　improvement　under　moderate　CAV　penetration　(20–40%).　However,　excessive　aggregation　at　high　penetrations　(60–80%)　degraded　efficiency,　highlighting　the　importance　of　strategy　calibration.　Optimal　CACC　platoon　size　was　determined　as　four　vehicles　for　CSA,　balancing　stability　and　flow　dynamics.　LTA　implementation　reduced　relative　entropy　and　achieved　a　critical　full-load　density　of　27　veh/km,　optimizing　lane　utilization.　Strategic　lane　aggregation　and　dedicated　CAV　lanes　significantly　enhance　traffic　order　and　efficiency,　with　penetration　rate-specific　recommendations:　CSA/RDA　for　moderate　adoption　and　LTA　for　higher　CAV　densities.　Findings　offer　theoretical　and　practical　guidance　for　deploying　adaptive　traffic　management　systems　and　CAV-exclusive　infrastructure.　©　The　Author(s),　under　exclusive　licence　to　Intelligent　Transportation　Systems　Japan　2025.