Connected　vehicles　(CVs)　have　demonstrated　significant　potential　for　addressing　traffic　issues.　This　paper　presents　a　mathematical　framework　for　stochastic　heterogeneous　traffic　flow,　integrating　regular　vehicles　(RVs)　and　CVs,　considering　stochasticity,　driver＇s　dynamic　time　headway　(DTH)　characteristics,　and　the　information　flow　topology　(IFT)　of　CVs.　We　develop　a　novel　car　-following　model　(CFM)　for　RVs,　accounting　for　both　driver＇s　stochasticity　and　DTH　characteristics.　Furthermore,　we　propose　a　dynamic　model　for　CVs　by　integrating　connected　assisted　driving　strategies　(CADS)　into　the　RVs＇　model,　which　includes　a　dynamic　information　flow　topology　(DIFT)　based　on　the　time　headway　(TH)　between　vehicles　within　the　communication　range.　We　derive　second　-order　exponential　stability　conditions　for　both　RVs　and　CVs　by　employing　the　Lyapunov　stochastic　stability　theory.　We　investigate　the　impact　of　driver　stochasticity,　DTH　characteristics,　and　CADS　on　heterogeneous　traffic　flow　characteristics　through　extensive　numerical　experiments.　Model　calibration　results　indicate　that,　in　comparison　to　the　state-of-the-art　model,　the　proposed　model　exhibits　superior　prediction　accuracy,　achieving　a　9.09%　improvement　at　the　group　-driver　level　and　a　10.47%　improvement　at　the　individual　-driver　level.　Theoretical　and　numerical　experimental　results　demonstrate　that　CVs　with　the　proposed　assisted　driving　strategy　effectively　mitigate　traffic　oscillations,　and　traffic　flow　stability　improves　as　the　CV　penetration　rate　increases.　Moreover,　CVs　can　efficiently　suppress　stochasticity　in　traffic　flow,　with　the　strength　of　traffic　fluctuations　decreasing　as　the　CV　penetration　rate　grows.　Different　CV　spatial　distributions　result　in　different　propagation　strengths　of　disturbances　in　traffic　flow,　adhering　to　a　specific　dual　Gaussian　distribution.　Under　various　conditions,　the　average　decline　rates　of　speed　fluctuations　and　energy　consumption　for　traffic　with　the　increasing　CV　penetration　rate　are　20%-50%　and　10%-30%,　respectively.