This paper studies the impact of the decrease in government subsidies on the selection of the cooperation model of vehicle manufacturers’ in the new energy vehicle supply chain, and uses the mathematical modeling method to establish MA (cooperation between vehicle manufacturers and battery suppliers with better battery life), MB (cooperation between vehicle manufacturers and battery suppliers with similar battery life) MAB (vehicle manufacturer cooperates with both) and N (does not cooperate with both) four cooperation models. The results show that vehicle manufacturers and battery suppliers A and B have cooperation motives, but whether vehicle manufacturers prefer to cooperate with battery supplier A or battery supplier B is related to the decrease in subsidies. In addition, it studies the impact of the decrease in subsidies on the sales price, market demand and supply chain profit of new energy vehicles.

This study investigates the optimization of taxi-pooling operations using the M/M/1/m queuing model, aiming to enhance efficiency and revenue for taxi service platforms. Traditional taxi operations face challenges during peak periods, including inefficiency and increased operational costs. Taxi-pooling, by accommodating multiple passengers with similar travel demands, offers a solution that reduces travel costs, operational expenses, and urban congestion. The study develops an optimization model to balance operational costs and passenger waiting times, identifying the utilization rate of taxis as a critical factor in platform revenue. By modeling the taxi-pooling service as a queuing system, we derive mathematical expressions for passenger waiting times and platform revenue under varying conditions. Our findings highlight the importance of optimal vehicle investment strategies and pricing decisions to maximize revenue. The study provides theoretical support for improving taxi-pooling platforms’ efficiency and competitiveness, contributing to better urban transportation solutions.

The unbalanced nature of passenger demand and the uncertainty of delay at intersections are considered critical factors in the efficiency of public transportation operations. Considering the controllable characteristics of the bus rapid transit system (BRT), an integrated optimization model that includes multi-mode transit scheduling and signal timing is proposed. The robust optimization model copes with the fluctuations in passenger demand and bus operation. A non-linear programming is developed to minimize the total passenger travel time and optimize the transit departure time, dwelling strategies at stations, and signal timings at intersections. The constraints on the signal control, degree of saturation, transit scheduling, and passenger waiting time at stations are intended to reflect real traffic conditions. A case study and extensive sensitivity analyses were conducted to evaluate the performance of the proposed model. The results show that the proposed integrated model can adjust the BRT dwelling strategies based on different passenger demands and coordinate transit departure time and signal timing adjustment to mitigate the passenger delay. Furthermore, experimental results demonstrate improvements of 5.3% and 8.1% in the mean and maximum values of passenger travel time, respectively, using the proposed model, compared to those obtained using the conventional operation strategy.