The　effectiveness　of　adaptive　traffic　signal　control　highly　relies　on　accurate　and　accountable　identification　of　dynamic　arrival　turning　movement　demand　on　approaches　and　other　traffic　flow　parameters　measuring　traffic　states.　Emerging　connected　vehicle　(CV)　and/or　autonomous　vehicle　(AV)-generated　mobility　data　can　be　potentially　used　as　a　new　data　source　in　support　of　the　adaptive　signal　control.　In　the　long-run,　the　CV/AV-generated　data　source　could　gradually　substitute　traditional　inductive　loop　data　as　the　maturity　levels　of　the　relevant　data　process　techniques　are　progressively　increasing.　However,　use　of　the　CV/AV-generated　data　is　still　yet　mature　due　to　lack　of　the　data　process　mechanism　and　models　to　integrate　the　data　into　the　adaptive　traffic　signal　control　system.　It　is　hence　an　imperative　need　to　develop　the　mechanism　for　processing　the　CV/AV-generated　data　source　in　order　to　facilitate　improving　the　efficiency　of　the　adaptive　traffic　signal　control　schemes.　This　paper　presents　a　developed　methodological　framework　along　with　associated　data　models　which　can　be　used　to　configure　an　intelligent　CV/AV　data　fusion　in　support　of　adaptive　signal　control　operations.　A　proof-of-concept　study　has　been　conducted　to　test　the　developed　models　via　comparison　of　the　CV/AV-data-driven　scenario　and　the　traditional-detection-data-supported　scenarios.　The　paper　presents　the　modeling　framework　along　with　performance　analysis　of　the　testing　study,　which　demonstrates　positive　outcomes　in　terms　of　reduced　queue　length　and　throughput,　as　well　as　benefit-cost　ratios.　©　2025　Periodical　Offices　of　Chang’an　University