The issuance of consumption coupons during the epidemic period to stimulate the economy must take full account of the level of probabilistic consumption and inventory optimization. In this paper, an improved minimum-cost maximum-flow model is constructed to dynamically adjust the inventory capacity of node enterprises with the change of probabilistic consumption level, and three scenarios are simulated by numerical assumptions. The results show that: (1) The model can better solve the problem of consumption coupons, probabilistic consumption and inventory optimization; (2) Consumer welfare remains unchanged, the largest number of government consumption coupons is issued, and the number of enterprise inventories reaches the lowest; (3) Enterprise inventories are minimized with different decisions on consumer probability consumption, and the government’s issuance of consumption coupons and the satisfaction of consumer demand have reached a dynamic balance. Corresponding suggestions are put forward, hoping to better help the government to implement the consumption coupons policy to stimulate the economy.

Aiming at the challenges of peak passenger congestion, user behavior heterogeneity and insufficient network synergy faced by public transportation systems in urbanization, this study proposed the Dynamic Incentive Strategy-Heterogeneous Response Synergy Model (DIS-HARM). The model integrated reinforcement learning, user heterogeneity modeling and small-world network synergy mechanism, adjusted the carbon credit intensity in real time by dynamic incentive generator, quantified the diminishing marginal utility effect of incentives for high-income groups by combining elastic user identifiers, and designed weather attenuation coefficients to optimize the spread of social influence. Simulation results showed that DIS-HARM significantly improves system efficiency and fairness: the peak hour passenger flow reduction rate reaches 72.2% (2.5% higher than the static strategy), the average peak hourly cost is reduced by 3.125%, and 36.5% of the incentive resources are tilted to the low-income group (83.1% coverage rate) at the same time. The model provided a theoretical tool for dynamic pricing and differentiated incentive strategies for urban transportation management, helping to achieve the dual goals of green travel and social equity.