［Purposes ］ With the continuous advancement of connected and autonomous driving technologies，mixed traffic flow involving both connected and autonomous vehicle （CAV） and human -driven vehicle （HDV） will persist for an extended period.However，there are significant differences in driving characteristics and route choice behaviors between CAV and HDV，and actual traffic demand always changes randomly.Therefore，in order to accurately predict network equilibrium flow patterns，this paper conducted research on mixed traffic assignment considering stochastic demand in a human -machine hybrid driving environment.［Methods］ Firstly，this paper analyzed the distributions of route flows and link flows for CAV and HDV under the assumption that travel demand follows a lognormal distribution.Based on the Bureau of Public Roads （BPR） type link impedance function，the route travel time under lognormal demand was derived.Secondly，by assuming that CAV and HDV chose their routes based on actual travel time budget and perceived travel time budget，respectively，this paper considered global demand elasticity and constructed a reliability -based user equilibrium and reliability -based stochastic user equilibrium （RUE -RSUE） mixed traffic assignment variational inequality model in the human -machine hybrid driving environment.Moreover，it designed a dual -loop algorithm of successive averages to solve it.Finally，the Nguyen -Dupuis network was used to verify the model and algorithm constructed in this paper，and the influence of CAV ’s market penetration，the travel time reliability demand，and the coefficient of variation of the traffic demand on the network equilibrium flow pattern was analyzed.［Findings］ The numerical results show that the dual -loop algorithm of successive averages designed in this paper can effectively solve the RUE -RSUE mixed traffic assignment model considering global demand elasticity.The travel time budget decreases with the increase in of CAV’s market penetration and increases with the increase in the coefficient of variation of the traffic demand and travel time reliability，but it is greatly affected by CAV ’s market penetration and traffic demand fluctuations.Traffic demand decreases with the increase in travel time reliability demand，and the increase in CAV ’s market penetration rate will increase the traffic demand under the same sensitivity，but the impact of travel time reliability sensitivity on traffic demand is more significant.［Conclusions ］ The proposed RUE -RSUE mixed traffic assignment model captures the effects of the traffic demand randomness on route choices for CAV and HDV，providing a basis for decision -making in the planning and management of intelligent connected transportation systems.