Research on drone hub-rider collaborative delivery model under urban instant delivery conditions
Article Text (Baidu Translation)
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摘要: 针对城市即时配送中传统骑手直送模式的运力瓶颈以及无人机直接配送在高密度城市环境中面临的挑战，提出了无人机枢纽-骑手联合配送模式；采用了“骑手前端取货-无人机枢纽间运输-骑手末端配送”的三段式流程，并衍生出5种拓展模式；基于Logit模型构建了包含经济性和时效性双重属性的分担率模型，推导了新模式与既有模式效用均衡时的临界运距计算公式，评估了竞争力边界；采用蒙特卡洛模拟、基于旅行商问题的数值试验及排队论模型对枢纽间距离期望值、顺路系数及枢纽成本等关键参数进行估算；构建了符合中国城市即时配送特征的基准场景，并通过敏感性分析探究了路网条件、枢纽位置等因素对配送模式竞争力的影响。结果表明：基准场景下联合配送模式的临界运距为3.88 km，处于骑手直送模式主要服务范围内；当枢纽与供需点距离位于2.57~4.32 km且配送距离在5.17~9.49 km时，因无人机续航限制需中转，导致优势区间不连续分布；5种拓展模式的临界运距介于1.57~3.88 km，呈现差异化竞争特征，其中，需求端自提模式临界运距最低（1.57 km），适用于用户自提场景，而端到端直配和全程自主配送模式次低，但均需供给端自配枢纽，且全程自主配送模式需较高自动化水平；在多订单场景下，联合配送模式对时间敏感型货物在更广泛的距离区间内保持竞争优势。所提方法为解决无人机在城市配送中的适应性问题提供了新思路，可为无人机配送网络规划、枢纽选址布局和低空经济政策制定提供定量分析工具。Abstract: To address the capacity bottlenecks of the traditional direct delivery model by riders in urban instant delivery and the challenges faced by direct drone delivery in high-density urban environments, a drone hub-rider collaborative delivery model was proposed. A three-stage process of "rider front-end pickup, drone inter-hub transportation, and rider terminal delivery" was adopted, and five extended models were derived. Based on the Logit model, a modal split model containing the dual attributes of economy and timeliness was constructed; the calculation formulas for the critical delivery distance when the utilities of the new model and existing models reached equilibrium were derived, and the competitiveness boundaries were evaluated. Monte Carlo simulation, numerical experiments based on the traveling salesman problem, and a queueing theory model were adopted to estimate key parameters such as the expected value of inter-hub distance, the enroute coefficient, and hub costs. A baseline scenario conforming to the characteristics of urban instant delivery in China was constructed, and the impacts of factors such as road network conditions and hub locations on the competitiveness of the delivery model were explored through sensitivity analysis. The results indicate that the critical delivery distance of the collaborative delivery model under the baseline scenario is 3.88 km, which is within the main service range of the direct rider delivery model. When the distance between the hub and supply/demand points is 2.57–4.32 km, and the delivery distance is 5.17–9.49 km, transfers are required due to the endurance limit of the drone, which leads to a discontinuous distribution of the advantage intervals.The critical delivery distances of the five extended models range from 1.57 to 3.88 km, presenting differentiated competitive characteristics.The demand-side self-pickup mode has the lowest critical delivery distance (1.57 km), which is suitable for customer self-pickup scenarios; the end-to-end direct delivery and fully autonomous delivery modes have the second lowest critical delivery distances, but both require supply-side self-equipped hubs, with the fully autonomous delivery mode additionally demanding a higher level of automation. In multi-order scenarios, the collaborative delivery model maintains a competitive advantage over a wider distance range for time-sensitive goods. The proposed method provides a new idea for solving the adaptability problem of drones in urban delivery and can provide a quantitative analysis tool for drone delivery network planning, hub location layout, and low-altitude economic policy formulation.
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图 9 无人机固定成本、骑手单位运输成本敏感性分析
Figure 9. Sensitivity analysis of drone fixed cost and rider unit transportation cost
图 12 枢纽距离-配送距离与联合配送模式分担率关系
Figure 12. Relationship between hub distance-delivery distance and share rate of DHRCDM
图 13 无人机每天订单量对联合配送模式分担率的影响
Figure 13. Impact of drone daily order volume on DHRCDM share rate
图 15 末端直飞模式、端到端直配模式临界运距
Figure 15. Critical delivery distance of last-mile direct flight mode and end-to-end direct delivery mode
图 16 全程自主配送模式临界运距分析
Figure 16. Critical delivery distance analysis of fully autonomous delivery mode
图 17 多订单场景下的多模式优势配送区间
Figure 17. Advantageous delivery distance intervals of multiple modes in multi-order scenarios
图 19 多订单场景下时间敏感型货物的多模式优势区间
Figure 19. Advantageous delivery intervals of multiple modes for time-sensitive goods in multi-order scenarios
图 20 多订单场景下成本敏感型货物的多模式优势区间
Figure 20. Advantageous delivery intervals of multiple modes for cost-sensitive goods in multi-order scenarios
表 1 基于蒙特卡洛模拟的顺路系数拟合
Table 1. Enroute coefficient fitting based on Monte Carlo simulation
L σ 最佳拟合模型 R2 RMSE L σ 最佳拟合模型 R2 RMSE 3 000 0.1 二次模型 0.999 8 0.001 4 7 000 0.1 二次模型 0.999 7 0.001 9 3 000 0.2 二次模型 0.998 2 0.011 4 7 000 0.2 二次模型 0.998 1 0.011 6 3 000 0.4 二次模型 0.998 0 0.021 2 7 000 0.4 二次模型 0.997 9 0.021 9 3 000 0.6 二次模型 0.999 3 0.016 3 7 000 0.6 二次模型 0.999 3 0.016 5 5 000 0.1 二次模型 0.999 8 0.001 8 10 000 0.1 二次模型 0.999 8 0.001 7 5 000 0.2 二次模型 0.998 1 0.010 6 10 000 0.2 二次模型 0.998 2 0.011 2 5 000 0.4 二次模型 0.998 0 0.021 4 10 000 0.4 二次模型 0.997 8 0.022 2 5 000 0.6 二次模型 0.999 3 0.016 7 10 000 0.6 二次模型 0.999 3 0.016 1 
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表 2 直接引用参数取值
Table 2. Parameter values from direct citation
参数 取值 参数 取值 参数 取值 $ {v}_{\mathrm{r}} $[26]/（km·h-1） 25 $ {C}_{\mathrm{e}-\mathrm{p}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}} $[26] /（元·KWh-1） 0.66 $ {F}_{\mathrm{d}\mathrm{r}\mathrm{o}\mathrm{n}\mathrm{e}} $ [50] /（元·天-1） 46.3 $ {v}_{\mathrm{f}} $[26] /（km·h-1） 57.6 $ \tau $[51] 0.15 $ {A}_{1} $[49] /m2 0.224 $ {q}_{1} $[49] /kg 7 $ {q}_{2} $[49] /kg 10 $ \psi $[49] 0.7 $ {A}_{2} $[49] /m2 0.015 $ {A}_{3} $[49] /m2 0.092 9 $ {\partial }_{1} $[49] 1.49 $ {\partial }_{2} $[49] 1 $ {\partial }_{3} $[49] 2.2 $ g $[49] /（m·s-2） 9.8 $ D $[49] /m 0.432 $ y $[49] /个 8 $ {q}_{\mathrm{t}} $[26] /kg 1 $ \rho $[49] /（kg·m-3） 1.225 $ {Q}_{\mathrm{t}} $[26] /kg 5
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表 3 综合推导参数取值
Table 3. Parameter values from comprehensive derivation
参数 取值 参数 取值 参数 取值 $ {T}_{1}^{\text{'}} $/min 10 $ {T}_{3}^{\text{'}} $/min 5 $ {T}_{5}^{\text{'}} $/min 3 $ {T}_{7}^{\text{'}} $/min 3 $ {L}_{\mathrm{d}} $/km 8 $ {\alpha }_{\mathrm{f}} $ 1.1 $ \alpha {}_{\mathrm{r}} $ 1.5 $ {C}_{\mathrm{p}\mathrm{i}\mathrm{c}\mathrm{k}\mathrm{u}\mathrm{p}} $/（元·单-1） 1.3 $ {C}_{\mathrm{d}\mathrm{e}\mathrm{l}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{y}} $/（元·单-1） 1.3 $ {C}_{\mathrm{u}-\mathrm{r}} $/（元·km-1） 1.2 $ {C}_{\mathrm{u}\mathrm{n}\mathrm{i}\mathrm{t}-\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}} $/（元·m2 ·年-1） 600 $ {C}_{\mathrm{o}} $/（元·次-1） 0.5
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表 4 专家论证参数取值
Table 4. Parameter values from expert consultation
参数 取值 参数 取值 $ \phi \left({n}_{\mathrm{z}}\right) $ 1.2 $ \sigma $ 0.2 $ {\gamma }_{k} $ 0.5 $ {d}_{ik} $/km 0.5 $ {Q}_{\mathrm{d}\mathrm{r}\mathrm{o}\mathrm{n}\mathrm{e}} $/单 24 $ {W}_{\mathrm{t}} $ /min 1 $ {d}_{{k}^{\text{'}}j} $/km 1
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表 5 联合配送模式体系对比分析
Table 5. Comparative analysis of DHRCDM and its extensions
模式类型 对比基准模型成本/距离参数变化 对比基准模型时间参数变化 续航约束 最小临界运距/km 模型特点 新增实施难度 无人机枢纽模式（基准） $ {L}_{\mathrm{d}} $ 3.88 基于枢纽建设完成协同配送 供给端直达模式 $ {L}_{\mathrm{d}} $ 3.88 供给点至枢纽点收益由商家获取 商家的参与意愿和操作能力 需求端自提模式 $ -{C}_{{k}^{\text{'}}j, \mathrm{r}}^{}-{C}_{\mathrm{d}\mathrm{e}\mathrm{l}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{y}} $ $ -{T}_{8}-{T}_{3} $ $ {L}_{\mathrm{d}} $ 1.57 允许用户在货物运抵目标枢纽后自行取货 用户自提意愿；枢纽存储能力 末端直飞模式 $ {d}_{{k}^{\text{'}}j}=0 $ $ {T}_{3}=0 $ $ \frac{{L}_{\mathrm{d}}}{2} $ 2.73 骑手将货物送至枢纽后，无人机可从枢纽直飞需求点完成末端配送 临时泊位选址建设；货物交接标准化；动态航路规划 端到端直配 $ {d}_{ik}={d}_{{k}^{\text{'}}j}=0 $ $ {T}_{3}=0 $ $ \frac{{L}_{\mathrm{d}}}{2} $ 2.13 供需两端具备无人机起降设施，且起飞点为无人机枢纽 临时泊位选址建设；货物交接标准化；动态航路规划；供给端基础设施及订单规模要求；供给端管理要求 全程自主配送模式 $ \begin{array}{l}-{C}_{ik, \mathrm{r}}^{}-{C}_{{k}^{\text{'}}j, \mathrm{r}}^{}+\ {C}_{ik, \mathrm{f}}^{}+{C}_{{k}^{\text{'}}j, \mathrm{f}}^{}\end{array} $ $ \begin{array}{l}\mathrm{m}\mathrm{a}\mathrm{x}\left\{{T}_{1}^{\text{'}}, {T}_{5}^{\text{'}}+\right.\ \frac{{d}_{ik}{\alpha }_{\mathrm{f}}}{{v}_{\mathrm{f}}}+{T}_{7}^{\text{'}}\}-\end{array} $
$ {T}_{1}^{\text{'}}-{T}_{4}-{T}_{8} $$ {L}_{\mathrm{d}} $ 2.14 无人机从枢纽出发完成取货和送货全流程，实现配送链路的完全自动化 高自动化水平要求；监管框架要求动态航路规划；取送端临时泊位选址建设
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[1] 国务院办公厅. 国务院办公厅关于促进即时配送行业高质量发展的指导意见: 国办发〔2024〕3号[EB/OL]. (2024-01-27). https://www.gov.cn/zhengce/content/202409/content_6974606.htm .General Office of the State Council. Guiding opinions on promoting the high-quality development of the instant delivery industry: Guo Ban Fa [2024] No. 3[EB/OL]. (2024-01-27).https://www.gov.cn/zhengce/content/202409/content_6974606.htm .[2] Frost & Sullivan. 2024 China instant delivery industry trends white paper[R]. Shanghai: Frost & Sullivan, 2024. [3] XUE G Q, WANG Z, WANG G. Optimization of rider scheduling for a food delivery service in O2O business [J]. Journal of Advanced Transportation, 2021, 2021: 5515909. [4] CHIANG W C, LI Y Y, SHANG J, et al. Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization [J]. Applied Energy, 2019, 242: 1164-1175. doi: 10.1016/j.apenergy.2019.03.117 [5] AGATZ N, BOUMAN P, SCHMIDT M. Optimization approaches for the traveling salesman problem with drone [J]. Transportation Science, 2018, 52(4): 965-981. doi: 10.1287/trsc.2017.0791 [6] DUKKANCI O, KARA B Y, BEKTAŞ T. Minimizing energy and cost in range-limited drone deliveries with speed optimization [J]. Transportation Research Part C: Emerging Technologies, 2021, 125: 102985. doi: 10.1016/j.trc.2021.102985 [7] JAZAIRY A, PERSSON E, BRHO M, et al. Drones in last-mile delivery: A systematic literature review from a logistics management perspective[J]. The International Journal of Logistics Management, 2025, 36(7): 1-62. [8] 周明阳. 无人机送货前景可期[N/OL]. 新华网, (2024-06-05). https://www.news.cn/tech/20240605/1fb877357a0d49d19094f5432b986697/c.html .ZHOU Ming-yang. Prospects for drone delivery[N/OL]. Xinhuanet, (2024-06-05).https://www.news.cn/tech/20240605/1fb877357a0d49d19094f5432b986697/c.html .[9] ZHANG J, CAMPBELL J F, SWEENEY D C, et al. Energy consumption models for delivery drones: A comparison and assessment[J]. Transportation Research Part D: Transport and Environment, 2021, 90: 102668. doi: 10.1016/j.trd.2020.102668 [10] CHUNG S H, SAH B, LEE J. Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions[J]. Computers & Operations Research, 2020, 123: 105004. [11] PINTO R, LAGORIO A. Point-to-point drone-based delivery network design with intermediate charging stations [J]. Transportation Research Part C: Emerging Technologies, 2022, 135: 103506. doi: 10.1016/j.trc.2021.103506 [12] ZHU T K, BOYLES S D, UNNIKRISHNAN A. Two-stage robust facility location problem with drones [J]. Transportation Research Part C: Emerging Technologies, 2022, 137: 103563. doi: 10.1016/j.trc.2022.103563 [13] PARK Y, LEE S, SUNG I, et al. Facility location-allocation problem for emergency medical service with unmanned aerial vehicle[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(2): 1465-1479. [14] 任新惠, 王柳, 王佳雪. 基于分区优化的无人机全自动机场选址研究[J]. 运筹与管理, 2023, 32(6): 20-26.REN Xin-hui, WANG Liu, WANG Jia-xue. Automatic vertiport location of unmanned aerial vehicle based on partition optimization[J]. Operations Research and Management Science, 2023, 32(6): 20-26. [15] BRUNI M E, KHODAPARASTI S, BRUNI M E, et al. A variable neighborhood descent matheuristic for the drone routing problem with beehives sharing[J]. Sustainability, 2022, 14(16): 9978. doi: 10.3390/su14169978 [16] 张春晓, 郭通, 李宇萌. 城市低空立体物流网络双种群协同优化方法[J]. 航空学报, 2025, 46(11): 287-306.ZHANG Chun-xiao, GUO Tong, LI Yu-meng. Dual-population coevolutionary optimization for multi-layer urban air logistics network [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 287-306. [17] MURRAY C C, CHU A G. The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery[J]. Transportation Research Part C: Emerging Technologies, 2015, 54: 86-109. doi: 10.1016/j.trc.2015.03.005 [18] ZANG X N, JIANG L, LIANG C Y, et al. Optimization approaches for the urban delivery problem with trucks and drones [J]. Swarm and Evolutionary Computation, 2022, 75: 101147. doi: 10.1016/j.swevo.2022.101147 [19] LI Y S, ZHANG G Z, PANG Z B, et al. Continuum approximation models for joint delivery systems using trucks and drones [J]. Enterprise Information Systems, 2020, 14(4): 406-435. doi: 10.1080/17517575.2018.1536928 [20] 马飞, 张洁, 孙少龙, 等. 基于TBL视角的"卡车+无人机"协同配送多目标路径优化[J/OL]. 系统工程理论与实践, 2025, http://kns.cnki.net/kcms/detail/11.2267.N.20250313.1919.024.html .MA Fei, ZHANG Jie, SUN Shao-long, et al. Multi-objective Path Optimization for "Truck + Drone" Collaborative Delivery Based on TBL Perspective[J/OL]. Systems Engineering Theory & Practice, 2025,http://kns.cnki.net/kcms/detail/11.2267.N.20250313.1919.024.html .[21] JEONG H Y, SONG B D, LEE S. Truck-drone hybrid delivery routing: Payload-energy dependency and No-Fly zones [J]. International Journal of Production Economics, 2019, 214: 220-233. doi: 10.1016/j.ijpe.2019.01.010 [22] MENG S S, GUO X P, LI D, et al. The multi-visit drone routing problem for pickup and delivery services [J]. Transportation Research Part E: Logistics and Transportation Review, 2023, 169: 102990 doi: 10.1016/j.tre.2022.102990 [23] LIU M Q, LI Y L, WANG X W. Joint optimization of truck-drone routing for last-mile deliveries in urban areas [J]. Transportmetrica A: Transport Science, 2024, 22: 1-27. [24] LUO Q Z, WU G H, JI B, et al. Hybrid multi-objective optimization approach with Pareto local search for collaborative truck-drone routing problems considering flexible time windows [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 13011-13025. doi: 10.1109/TITS.2021.3119080 [25] CHEN X W, ULMER M W, THOMAS B W. Deep Q-learning for same-day delivery with vehicles and drones [J]. European Journal of Operational Research, 2022, 298(3): 939-952. doi: 10.1016/j.ejor.2021.06.021 [26] 赵强柱, 卢福强, 王雷震, 等. 无人机骑手联合外卖配送路径优化问题研究[J]. 计算机工程与应用, 2022, 58(11): 269-278.ZHAO Qiang-zhu, LU Fu-qiang, WANG Lei-zhen, et al. Research on drones and riders joint take-out delivery routing problem [J]. Computer Engineering and Applications, 2022, 58(11): 269-278. [27] 卢福强, 蒋润雪, 毕华玲, 等. 动态订单下无人机辅助骑手外卖配送路径优化研究[J]. 中国管理科学, 2024, 34(2): 79-88.LU Fu-qiang, JIANG Run-xue, BI Hua-ling, et al. Research on route optimization of drone-assisted rider food delivery under dynamic orders[J]. Chinese Journal of Management Science, 2024, 34(2): 79-88. [28] ZHOU M, ZHAO L D, KONG N, et al. Understanding consumers' behavior to adopt self-service parcel services for last-mile delivery[J]. Journal of Retailing and Consumer Services, 2020, 52: 101911. doi: 10.1016/j.jretconser.2019.101911 [29] BOYSEN N, FEDTKE S, SCHWERDFEGER S. Last-mile delivery concepts: A survey from an operational research perspective [J]. OR Spectrum, 2021, 43(1): 1-58. doi: 10.1007/s00291-020-00607-8 [30] 国务院, 中央军委. 无人驾驶航空器飞行管理暂行条例: 国务院令第761号[R]. 北京: 中国法制出版社, 2023.State Council, Central Military Commission. Interim regulations on the management of unmanned aerial vehicle flights: Order No. 761[R]. Beijing: China Legal Publishing House, 2023. [31] 廖小罕, 徐晨晨, 叶虎平, 等. 无人机应用发展关键基础设施与低空公共航路网规划[J]. 中国科学院院刊, 2022, 37(7): 977-988.LIAO Xiao-han, XU Chen-chen, YE Hu-ping, et al. Critical infrastructures for developing UAVs' applications and low-altitude public air-route network planning [J]. Bulletin of Chinese Academy of Sciences, 2022, 37(7): 977-988. [32] 李诚龙, 屈文秋, 李彦冬, 等. 面向eVTOL航空器的城市空中运输交通管理综述[J]. 交通运输工程学报, 2020, 20(4): 35-54. doi: 10.19818/j.cnki.1671-1637.2020.04.003LI Cheng-long, QU Wen-qiu, LI Yan-dong, et al. Overview of traffic management of urban air mobility (UAM)with eVTOL aircraft [J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 35-54. doi: 10.19818/j.cnki.1671-1637.2020.04.003 [33] 张洪海, 夷珈, 李姗, 等. 低空空域容量评估研究综述[J]. 交通运输工程学报, 2023, 23(6): 78-93. doi: 10.19818/j.cnki.1671-1637.2023.06.003Zhang Hong-hai, Yi Jia, Li Shan, et al. Review of low-altitude airspace capacity assessment research [J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 78-93. doi: 10.19818/j.cnki.1671-1637.2023.06.003 [34] SCHMIDT S, SARACENI A. Consumer acceptance of drone-based technology for last mile delivery [J]. Research in Transportation Economics, 2024, 103: 101404. doi: 10.1016/j.retrec.2023.101404 [35] 孙宗胜, 帅斌, 许旻昊. 低碳背景下快捷货物各运输方式间临界运距研究[J]. 交通运输系统工程与信息, 2023, 23(6): 11-21.SUN Zong-sheng, SHUAI Bin, XU Min-hao. Critical transportation distance analysis for express goods transportation modes considering low carbon emissions[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 11-21. [36] XU X D, YANG H C, JEONG K, et al. Teaching freight mode choice models new tricks using interpretable machine learning methods[J]. Frontiers in Future Transportation, 2024, 5: 1339273. doi: 10.3389/ffutr.2024.1339273 [37] MASOUMI H, MEHRIAR M, NOSAL-HOY K, et al. Logit and probit models explaining mode choice and frequency of public transit ridership among university students in Krakow, Poland [J]. Urban Science, 2024, 8(3): 113. doi: 10.3390/urbansci8030113 [38] JANSSON J O. Transport System Optimization and Pricing [M]. Chichester : Wiley, 1984. [39] MINKEN H, JOHANSEN B G. A logistics cost function with explicit transport costs [J]. Economics of Transportation, 2019, 19: 100116. doi: 10.1016/j.ecotra.2019.04.001 [40] PENG Y, MO Z Y, LIU S. Passenger's routes planning in stochastic common-lines' multi-modal transportation network through integrating genetic algorithm and Monte Carlo simulation [J]. Archives of Transport, 2021, 59(3): 73-92. doi: 10.5604/01.3001.0015.0123 [41] BEARDWOOD J, HALTON J H, HAMMERSLEY J M. The shortest path through many points[J]. Mathematical Proceedings of the Cambridge Philosophical Society, 1959, 55(4): 299-327. doi: 10.1017/S0305004100034095 [42] DAGANZO C F. The distance traveled to visit N points with a maximum of C stops per vehicle: An analytic model and an application [J]. Transportation Science, 1984, 18(4): 331-350. doi: 10.1287/trsc.18.4.331 [43] CHIEN T W. Operational estimators for the length of a traveling salesman tour[J]. Computers & Operations Research, 1992, 19(6): 469-478. [44] KWON O, GOLDEN B, WASIL E. Estimating the length of the optimal TSP tour: An empirical study using regression and neural networks [J]. Computers & Operations Research, 1995, 22(10): 1039-1046. [45] CHICCO D, WARRENS M J, JURMAN G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation [J]. PeerJ Computer Science, 2021, 7: e623. doi: 10.7717/peerj-cs.623 [46] CZACHÓRSKI T, NYCZ T, NYCZ M, et al. Traffic engineering: Erlang and engset models revisited with diffusion approximation [C]// Information Sciences and Systems 2014. Cham: Springer, 2014: 249-256. [47] COSMETATOS G P. Some approximate equilibrium results for the multi-server queue (M/G/r) [J]. Journal of the Operational Research Society, 1976, 27(3): 615-620. doi: 10.1057/jors.1976.120 [48] 杭州迅蚁网络科技有限公司. TR9S智能物流无人机[EB/OL]. (2025-04-13) [2025-09-22]. https://www.antwork.link/ .Hangzhou Antwork Network Technology Co., Ltd. TR9S intelligent logistics UAV[EB/OL]. (2025-04-13) [2025-09-22].https://www.antwork.link/ [49] STOLAROFF J K, SAMARAS C, O'NEILL E R, et al. Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery [J]. Nature Communications, 2018, 9: 409. doi: 10.1038/s41467-017-02411-5 [50] KONG R, YANG Y C. Meituan AI and robotics layout overview: Unmanned delivery, has the singularity arrived?[R]. Shanghai: TF Securities, 2025. [51] 艾瑞咨询. 中国即时配送行业研究报告[R]. 上海: 艾瑞咨询, 2024.InciResearch. China Instant Delivery Industry Research Report[R]. Shanghai: iResearch, 2024. [52] MOSHREF-JAVADI M, VAN C K P, MCCUNNEY B A, et al. Enabling same-day delivery using a drone resupply model with transshipment points[J]. Computational Management Science, 2023, 20(1): 22. doi: 10.1007/s10287-023-00453-3 [53] 魏子秋, 李孟晓. 考虑双重满意度的外卖配送路径优化[J]. 物流工程与管理, 2024, 46(1): 54-57.WEI Zi-qiu, LI Meng-xiao. Takeaway delivery route optimization considering double satisfaction[J]. Logistics Engineering and Management, 2024, 46(1): 54-57. [54] GIACOMIN D J, LEVINSON D M. Road network circuity in metropolitan areas [J]. Environment and Planning B, 2015, 42(6): 1040-1053. doi: 10.1068/b130131p [55] KIM S, KIM T, JEON J. Implementing dynamic distance-time conversion factors through real-time traffic data for enhancing urban mobility and service accessibility in South Korea [J]. PLoS One, 2025, 20(9): e0330266. doi: 10.1371/journal.pone.0330266 -
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