With　the　advancement　of　communication　and　autonomous　driving　technologies,　a　mixed　traffic　flow　comprising　human-driven　vehicles　(HVs),　connected　human-driven　vehicles　(CHVs),　and　connected　autonomous　vehicles　(CAVs)　is　emerging.　In　this　paper,　we　propose　a　generalized　car-following　model　for　mixed　traffic　flow,　which　considers　both　the　human　drivers’　characteristics　(i.e.,　perception　ability　of　distance,　acceleration,　and　speed,　trust　level　in　connected　vehicle　information,　and　driving　style)　and　information　from　multiple　leading　connected　vehicles　(CVs).　Through　numerical　experiments,　we　analyze　the　influences　of　mixed　traffic　flow　composition　schemes,　communication　distance,　and　human　drivers’　characteristics　on　mixed　traffic　flow.　The　results　show　that　the　proposed　model　can　effectively　capture　the　car-following　behavior　of　different　types　of　vehicles　in　mixed　traffic　flow.　The　contribution　of　CHVs　to　mixed　traffic　flow　stability　is　significantly　less　than　that　of　CAVs　due　to　the　involvement　of　human　drivers’　characteristics.　Human　drivers’　characteristics　significantly　influence　average　fuel　consumption　(FC)　within　mixed　traffic　flow.　Specifically,　inaccurate　perception　of　distance　by　human　drivers　can　lead　to　an　increase　in　the　average　FC,　while　a　higher　level　of　trust　in　connected　information　leads　to　lower　average　FC.　Furthermore,　the　results　reveal　that　the　communication　distance　between　CVs　plays　a　pivotal　role　in　the　stability　of　mixed　traffic　flow.　©　2023　Elsevier　B.V.