This research, oriented toward strengthening the social responsibility of mobility platforms, regulating driver workload, and ensuring economic revenue, proposes a sustainable service operation mode for intelligent mobility platforms led by reservation services and supplemented by instant services. A two-stage 0-1 mixed integer programming model is constructed. The stage I customizes service routes for reservation passengers based on driver workload, while stage II achieves on-demand matching considering passenger absenteeism. Given the different complexities of the two models, stage I is solved using a column generation-based heuristic algorithm (CG-BH) and a hybrid meta-heuristic algorithm based on adaptive large neighborhood search and simulated annealing (ALNS-SA), while stage II is solved using Gurobi. Numerical experiments verify the rationality of the two-stage model and highlights the superiority of the ALNS-SA algorithm in balancing solution quality and computational efficiency. Furthermore, the study quantifies the impact of the number of drivers and empty vehicle duration penalties on experimental results, demonstrating that the proposed method can reasonably regulate driver workload and match them with personalized service routes within their workload capacity. It also reveals that the revenue generated by serving high-quality instant passengers is higher than the losses incurred by defaulting passengers. This research provides management insights for the development of sustainable service operation mode for intelligent mobility platforms.