Hub-and-spoke UAM network design model considering service quality
Article Text (Baidu Translation)
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摘要: 随着城市低空经济的快速发展，科学布局垂直起降设施并构建高效运行的城市空中交通（UAM）网络成为UAM发展的关键和核心。根据容量和功能差异将垂直起降设施分为起降场和起降点，构建了考虑容量限制的非严格多分配两级轴辐式UAM网络；以垂直起降设施建设成本和网络运输成本最小、总出行时间最小、平均服务质量惩罚分数最小为目标，构建了多目标优化模型；以第三代非支配遗传算法为主优化框架，嵌入了变邻域搜索算法协同优化垂直起降设施选址决策及网络连接方案；以北京市地面交通出行数据为例，预测UAM潜在出行需求并生成垂直起降设施候选点集，在此基础上验证模型有效性，并进行了参数灵敏度试验。研究结果表明：相比传统两级网络结构，两级轴辐式网络能够将总成本降低0.9%，将总出行时间降低12.1%，将单位需求出行时间平均缩短4.12 min，同时提供更优的服务质量；垂直起降设施建设数量对各目标函数值和网络拥挤度有显著影响，当建设3个起降场、8个起降点时，UAM网络可达到各目标函数间的较好平衡。由此，该模型能够在提高经济效益和运输效率的同时，提高UAM网络服务质量，从而为构建轴辐式UAM网络提供科学的规划决策支持。Abstract: With the rapid development of the urban low-altitude economy, scientific layout of vertical take-off and landing (VTOL) facilities and the construction of an efficiently operated urban air mobility (UAM) network constitute the key and core of UAM development. Differentiating VTOL facilities by capacity and function into vertiports and vertistops, this study established a non-strict multi-allocation two-level hub-and-spoke UAM network that incorporates capacity constraints. A multi-objective optimization model was established with the objectives of minimizing VTOL facility construction cost and network transportation cost, minimizing total travel time, and minimizing the average service quality penalty score. The model was solved using a hybrid framework based on the non-dominated sorting genetic algorithm Ⅲ, enhanced by an embedded variable neighborhood search to co-optimize the location decisions for VTOL facilities and the network allocation scheme. Ground traffic travel data of Beijing was taken as a case study. UAM travel demand was predicted and a candidate VTOL facility set was generated. Based on this, model validity was verified and parameter sensitivity experiments were conducted. Research results show that, compared to traditional two-level network structures, the two-level hub-and-spoke network reduces total cost by 0.9%, reduces total travel time by 12.1%, shortens the average travel time per unit demand by 4.12 minutes, and delivers superior service quality. The number of VTOL facilities significantly affects objective function values and network congestion. An optimal trade-off among all objectives is achieved when deploying 3 vertiports and 8 vertistops. The proposed model thus enhances both economic benefits and transportation efficiency while improving UAM service quality, offering scientific decision support for the planning of hub-and-spoke UAM networks.
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图 10 不同垂直起降设施数量下的方案对比
Figure 10. Comparison of solutions under different numbers of VTOL facilities
表 1 时间、距离、成本系数预测结果
Table 1. Prediction results of time, distance, and cost coefficients
参数 取值 标准差 P值 95%置信区间 $ {\mu }_{1} $ -3.32×10-4 1.90×10-5 0.00 [-3.55×10-4，
-3.09×10-4]$ {\mu }_{2} $ 2.32×10-5 3.29×10-6 0.00 [1.70×10-5，
2.98×10-5]$ {\mu }_{3} $ -1.04×10-2 5.37×10-4 0.00 [-1.14×10-2，
-9.30×10-3]
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表 2 两种模型最优方案目标值对比
Table 2. Comparison of objective values for optimal solutions of two models
模型 总成本/万元 总出行时间/102 h 平均惩罚分数 传统模型 2 187.98 442.237 6 9.76 本文模型 2 168.53 388.793 5 9.72
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表 3 不同垂直起降设施数量下的方案对比
Table 3. Comparison of solutions under different numbers of VTOL facilities
r1 r2 网络运输成本/万元 总出行时间/102 h 平均惩罚分数 平均中转次数 平均拥挤度 1 9 304.017 6 426.515 6 6.858 30 0.206 633 1.222 356 2 8 263.556 9 419.870 7 8.730 50 0.354 592 1.160 539 3 7 249.192 7 409.251 5 10.168 57 0.558 673 0.699 592 4 6 224.219 3 394.603 4 12.507 50 0.545 918 0.561 554 3 8 268.526 5 388.793 5 9.724 90 0.477 000 0.639 834 3 9 293.009 1 385.102 0 8.411 00 0.410 700 0.545 482 3 10 309.701 4 378.471 9 9.469 90 0.390 300 0.529 980 3 11 331.791 0 375.004 7 8.840 20 0.382 700 0.445 388 3 12 295.345 6 356.323 4 8.758 50 0.290 800 0.352 296
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