发展城市空中交通(Urban Air Mobility，UAM)迫切需要在充分考虑安全约束的基础上，以更低的运营成本服务更多的旅客。对此，基于电动垂直起降飞行器(electric Vertical Takeoff and Landing，eVTOL)性能，以最大旅客周转量和最小总成本为目标函数，增设最小安全间隔、最大乘客等待时间等约束条件，建立面向动态需求的eVTOL运输调度模型，并提出一种联合成本与调度的粒子群优化算法(Joint Optimization of Cost and Schedule-Particle Swarm Optimization，JOCS-PSO)，求解最优调度方案。根据京津冀地区6个垂直起降点的旅客需求预测数据，进行异构eVTOL机队的规模与调度分配，调度结果表明使用170架EHang 216-S 型和177 架Geely Aerofugla AE200 X01 型eVTOL，在eVTOL 15年的使用周期内，共可周转124,076,676人次，人均成本为277.53元/人，在实现成本效益与服务效率平衡的基础上有显著的时间优势；此外，讨论了安全间隔对旅客周转量和成本的影响，计算结果指出若将安全间隔由1 min增加至6 min时，人均服务成本将增加4.1%，总服务人次将减少11.7%。
The development of Urban Air Mobility (UAM) urgently requires balancing cost-effectiveness and service efficiency by serving more passengers at lower operational costs while fully considering safety constraints. To address this, based on the performance of Electric Vertical Takeoff and Landing (eVTOL) vehicles, this study proposes a real-time demand-oriented eVTOL transport scheduling optimization model. The model uses total service population and total cost as objective functions, incorporating con-straints such as minimum safety intervals and maximum passenger waiting time. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm is constructed to solve the optimal scheduling scheme. Using real-world data from the Beijing-Tianjin-Hebei region, the demand conditions between six vertiports are calculated. By deploying 170 EHang 216-S and 177 Geely Aerofugla AE200 X01 eVTOLs, a total of 124,076,676 passengers can be served over the 15-year lifecycle of the eVTOLs, with a per capita cost of 277.53 CNY and an average travel time saving of approximately 1.5 hours per passenger. When the safety interval is increased from 1 minute to 6 minutes, the per capita service cost increases by 4.1%, and the total number of passengers served decreases by 11.7%. The results demonstrate that the proposed scheme offers significant time-saving advantages while maintaining controllable costs.
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