Collaborative location method for drone vertiport and truck parking point in urban logistics
Article Text (Baidu Translation)
-
摘要: 为提升卡车与无人机协同配送（Synchronized Truck-drone Delivery，STDD）模式的运营效率与服务质量，针对STDD模式下的设施布局难题，提出了无人机起降点与卡车停靠点协同选址方法。首先，基于地理信息数据，采用栅格法对三维城市空间进行离散化建模，通过整合障碍物分布、噪声影响、步行可达性等指标，精细量化分析栅格单元环境特征；然后，考虑物流配送距离、用户需求分布等因素，建立多目标无人机起降点选址模型，进一步结合起降点服务关系、无人机性能等条件，构建多目标卡车停靠点服务分配模型；最后，采用模糊C均值聚类算法对用户需求点进行空间聚合，兼顾飞行安全、噪声影响与取件效率，从帕累托前沿中选取相对最优的起降点布局方案，基于城市公共停车场分布数据，融合模糊C均值聚类与多目标多元宇宙优化算法，得到停靠点与起降点之间的最佳服务匹配关系。结果表明：随着单个起降点服务的建筑数量增长，起降点规模呈减少趋势，居民平均步行距离呈增长趋势；融合选址优化策略的聚类算法生成的无人机起降点平均环境得分为0.682，相比融合就近选址策略的聚类算法平均提升了49.2%；起降点数量与被启用的停靠点数量成正相关，与最近邻停靠点间距成负相关；与传统多目标多元宇宙优化算法相比，该算法将被启用的停靠点数量减少了33%，服务不均衡度下降了28.6%，最近邻间距均值提高了22.01%。所提方法实现了无人机起降点与卡车停靠点的选址优化布局，为智慧城市低空物流网络建设提供了技术支撑。Abstract: To enhance the operational efficiency and service quality of the synchronized truck-drone delivery (STDD) mode and to address the facility layout problem under the STDD mode, a collaborative location method for drone vertiports and truck parking points was proposed. First, based on geographic information data, the three-dimensional urban space was discretized using the grid method. By integrating indicators such as obstacle distribution, noise impact, and pedestrian accessibility, the environmental characteristics of grid units were quantitatively analyzed. Then, by considering factors such as logistics delivery distance and user demand distribution, a multi-objective drone vertiport location model was established. Furthermore, combining conditions such as vertiport service relationships and drone performance, a multi-objective truck parking point service allocation model was constructed. Finally, the fuzzy C-means clustering algorithm was employed to spatially aggregate user demand points. By considering flight safety, noise impact, and pickup efficiency, a relatively optimal vertiport layout scheme was selected from the Pareto front. Based on the distribution data of urban public parking lots, the fuzzy C-means clustering and multi-objective multi-verse optimization algorithms were integrated to obtain the optimal service matching relationship between truck parking points and vertiports. The results show that with the increase in the number of buildings served by a single vertiport, the scale of vertiports shows a decreasing trend, while the average walking distance of residents shows an increasing trend. Notably, the clustering algorithm integrated with the location optimization strategy yields an average environmental score of 0.682 for drone vertiports, representing a 49.2% improvement compared to the clustering algorithm integrated with the proximity-based location strategy. Furthermore, the number of vertiports presents a positive correlation with the number of enabled truck parking points and a negative correlation with the nearest-neighbor parking point spacing. Compared with the traditional multi-objective multi-verse optimization algorithm, the proposed algorithm reduces the number of enabled parking points by 33%, decreases the service imbalance degree by 28.6%, and increases the mean value of nearest-neighbor spacing by 22.01%. The proposed method realizes the optimized location layout of drone vertiports and truck parking points, which can provide technical support for the construction of low-altitude logistics networks in smart cities.
-
图 1 运行环境综合评估技术路线
Figure 1. Technical route for comprehensive assessment of the operating environment
图 2 无人机起降点与卡车停靠点运行关系
Figure 2. Drone vertiport and truck parking point operation relationship
图 5 卡车停靠点服务分配算法框架
Figure 5. Algorithm framework of truck parking point service allocation
图 6 不同集群总数下的编码与解码方式
Figure 6. Encoding and decoding modes for different total cluster counts
图 9 起降点分级选址结果与服务对象
Figure 9. Graded location results and service objects of vertiports
图 13 卡车停靠点服务分配目标函数
Figure 13. Objective functions of truck parking point service allocation
表 1 仿真参数
Table 1. Simulation parameters
参数 取值 参数 取值 影响半径$ {r}_{\mathrm{n}\mathrm{o}\mathrm{i}} $/m 500 地面反射修正项$ {s}_{\mathrm{r}\mathrm{e}\mathrm{f}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}} $/dB 11 物流无人机的声功率级$ {s}_{\mathrm{u}\mathrm{a}\mathrm{v}} $/dB 120 飞行续航里程$ {r}_{\mathrm{u}\mathrm{a}\mathrm{v}} $/m 3 000 空气吸收系数$ {\alpha }_{\mathrm{a}\mathrm{i}\mathrm{r}} $ 0.1 飞行续航裕度$ \mathrm{\Delta }{r}_{\mathrm{u}\mathrm{a}\mathrm{v}} $/m 100 最低悬停高度$ {h}_{\mathrm{u}} $/m 50 最近邻停靠点间距下限$ {r}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{{'}} $/m 500 居民期望步行时间$ {T}_{\mathrm{p}} $/s 300 服务起降点容量上限$ {s}_{\mathrm{m}\mathrm{a}\mathrm{x}}^{{'}} $ 50 居民步行速度$ {v}_{\mathrm{p}} $/（m·s-1） 1.2 备选停靠点总数$ \left|Z\right| $ 47 服务建筑物数量上限$ {s}_{\mathrm{m}\mathrm{a}\mathrm{x}} $ 85 一组宇宙总数$ {N}_{\mathrm{e}} $ 20 最近邻起降点间距下限$ {r}_{\mathrm{m}\mathrm{i}\mathrm{n}} $/m 100 最大迭代次数$ {I}_{\mathrm{m}\mathrm{a}\mathrm{x}} $ 1 000 
下载: 导出CSV
表 2 无人机起降点等级划分结果
Table 2. Classification results of drone vertiports
等级 服务建筑数量范围 平均服务建筑数量 起降点个数 一级起降点 51~85 62.4 14 二级起降点 21~50 31.7 52 三级起降点 1~20 11.6 100
下载: 导出CSV
表 3 算法对比结果
Table 3. Algorithm comparison results
方法 FCM-MOMVO MOMVO MOSA NSGA-Ⅱ 最近邻间距均值$ {D}_{\mathrm{a}\mathrm{v}\mathrm{g}}^{{'}} $/m 864.26* 708.30 612.50 562.03 服务不均衡度$ {S}_{\mathrm{s}\mathrm{t}\mathrm{d}} $ 2.45* 3.43 4.31 3.37 平均服务距离$ {C}_{\mathrm{a}\mathrm{v}\mathrm{g}}^{{'}} $/m 452.60 438.92 378.15 355.36* 被启用停靠点数量$ {N}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}} $ 10* 15 17 20 注：*表示对应目标函数值最优的解。
下载: 导出CSV
-
[1] LI S, ZHANG H H, YI J, et al. A bi-level planning approach of logistics unmanned aerial vehicle route network[J]. Aerospace Science and Technology, 2023, 141: 1 08572. [2] MENG Z Y, ZHOU Y T, LI E Y, et al. Environmental and economic impacts of drone-assisted truck delivery under the carbon market price [J]. Journal of Cleaner Production, 2023, 401: 136758. doi: 10.1016/j.jclepro.2023.136758 [3] SALAMA M, SRINIVAS S. Joint optimization of customer location clustering and drone-based routing for last-mile deliveries[J]. Transportation Research Part C: Emerging Technologies, 2020, 114: 620-642. doi: 10.1016/j.trc.2020.01.019 [4] 张林静, 丁溢. 基于卡车-无人机协同配送的双目标应急物资运送优化[J]. 系统管理学报, 2025, 34(1): 81-95.ZHANG Lin-jing, DING Yi. Optimizing bi-objective emergency supply transportation based on synchronized truck-drone delivery[J]. Journal of Systems & Management, 2025, 34(1): 81-95. [5] ZHANG R W, DOU L H, XIN B, et al. A review on the truck and drone cooperative delivery problem[J]. Unmanned Systems, 2024, 12(5): 823-847. doi: 10.1142/S2301385024300014 [6] 宋瑞, 边疆, 何世伟, 等. 考虑超重超远客户的卡车无人机协同配送研究[J]. 中国公路学报, 2024, 37(3): 395-406.SONG Rui, BIAN Jiang, HE Shi-wei, et al. Truck-drone joint delivery with consideration given to customers with great demands and at great distances[J]. China Journal of Highway and Transport, 2024, 37(3): 395-406. [7] 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 [8] KARAK A, ABDELGHANY K. The hybrid vehicle-drone routing problem for pick-up and delivery services[J]. Transportation Research Part C: Emerging Technologies, 2019, 102: 427-449. doi: 10.1016/j.trc.2019.03.021 [9] LI Z L, LI S, LU J, et al. Air route network planning method of urban low-altitude logistics UAV with double-layer structure [J]. Drones, 2025, 9(3): 193. doi: 10.3390/drones9030193 [10] 刘光才, 马寅松. 城市物流无人机配送中心选址及任务分配研究[J]. 飞行力学, 2023, 41(3): 88-94.LIU Guang-cai, MA Yin-song. Research on location and task allocation of urban logistics UAV distribution center[J]. Flight Dynamics, 2023, 41(3): 88-94. [11] JEONG J, SO M, HWANG H Y. Selection of vertiports using K-means algorithm and noise analyses for urban air mobility (UAM) in the Seoul metropolitan area[J]. Applied Sciences, 2021, 11(12): 5729. doi: 10.3390/app11125729 [12] ESCRIBANO M J, KHALIFE C, SLIM J, et al. An integrated vertiport placement model considering vehicle sizing and queuing: A case study in London[J]. Journal of Air Transport Management, 2023, 113: 102486. doi: 10.1016/j.jairtraman.2023.102486 [13] 曹英英, 陈淮莉. 基于集群的卡车与无人机联合配送调度研究[J]. 计算机工程与应用, 2022, 58(11): 287-294.CAO Ying-ying, CHEN Huai-li. Research on truck and drone joint distribution scheduling based on cluster[J]. Computer Engineering and Applications, 2022, 58(11): 287-294. [14] VOLAKAKIS V, MAHMASSANI H S. Vertiport infrastructure location optimization for equitable access to urban air mobility[J]. Infrastructures, 2024, 9(12): 239. doi: 10.3390/infrastructures9120239 [15] 冯棣坤, 张洪海, 华明壮, 等. 面向城市低空物流的多层异质起降场点网络协同规划[J]. 交通运输工程学报, 2026, 26(2): 110-124. doi: 10.19818/j.cnki.1671-1637.2026.086 FENG Di-kun, ZHANG Hong-hai, HUA Ming-zhuang, et al. Multi-layer heterogeneous take-off and landing site network collaborative planning for urban low-altitude logistics[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 110-124. doi: 10.19818/j.cnki.1671-1637.2026.086 [16] PETIT V, RIBEIRO M. Multi-objective vertiport location optimization for a middle-mile package delivery framework: Case study in the South Holland Region[J]. Journal of Air Transport Management, 2025, 125: 102757. doi: 10.1016/j.jairtraman.2025.102757 [17] KUO R J, LU S H, LAI P Y, et al. Vehicle routing problem with drones considering time windows[J]. Expert Systems with Applications, 2022, 191: 116264. doi: 10.1016/j.eswa.2021.116264 [18] 龚英, 涂熳熳, 周愉峰. 卡车-无人机协同的洪灾应急选址-路径鲁棒优化问题[J]. 计算机工程与应用, 2025, 61(14): 307-321.GONG Ying, TU Man-man, ZHOU Yu-feng. Truck-drone collaborated location-routing robust optimization problem for flood emergency response[J]. Computer Engineering and Applications, 2025, 61(14): 307-321. [19] SHAVARANI S M, GOLABI M, IZBIRAK G. A capacitated biobjective location problem with uniformly distributed demands in the UAV-supported delivery operation[J]. International Transactions in Operational Research, 2021, 28(6): 3220-3243. doi: 10.1111/itor.12735 [20] CHAUHAN D, UNNIKRISHNAN A, FIGLIOZZI M. Maximum coverage capacitated facility location problem with range constrained drones[J]. Transportation Research Part C: Emerging Technologies, 2019, 99: 1-18. [21] RAVE A, FONTAINE P, KUHN H. Drone location and vehicle fleet planning with trucks and aerial drones[J]. European Journal of Operational Research, 2023, 308(1): 113-130. doi: 10.1016/j.ejor.2022.10.015 [22] 李章萍, 贺亚蒙. 基于GA-SA组合算法的山区复杂环境无人机起降点选址[J]. 科学技术与工程, 2024, 24(2): 850-857.LI Zhang-ping, HE Ya-meng. Site selection of unmanned aerial vehicle take-off and landing points in mountainous complex environment based on GA-SA combination algorithm[J]. Science Technology and Engineering, 2024, 24(2): 850-857. [23] 陈丹, 汤程, 谢宇, 等. 面向城市低空物流配送的无人机实时航迹双层规划[J]. 航空学报, 2025, 46(16): 229-247.CHEN Dan, TANG Cheng, XIE Yu, et al. Real time dual layer path planning of unmanned aerial vehicles for urban low altitude logistics distribution[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(16): 229-247. [24] LEVINSON D, MARION B, OWEN A, et al. The city is flatter: Changing patterns of job and labor access[J]. Cities, 2017, 60: 124-138. doi: 10.1016/j.cities.2016.08.002 [25] RIVERA-GONZALEZ C, AHMED U, ROORDA M J. The evolving landscape of urban logistics: A study of the Greater Golden Horseshoe[J]. Journal of Transport Geography, 2025, 122: 1 04055. [26] DUDEK T, KAŚKOSZ K. Optimizing drone logistics in complex urban industrial infrastructure[J]. Transportation Research Part D: Transport and Environment, 2025, 140: 104610. doi: 10.1016/j.trd.2025.104610 [27] 吴迪, 石帅杰, 张雅婷, 等. 不确定需求下基于云仓储的物流网络节点选择[J]. 交通运输工程学报, 2025, 25(2): 189-203. doi: 10.19818/j.cnki.1671-1637.2025.02.012WU Di, SHI Shuai-jie, ZHANG Ya-ting, et al. Selection of logistics network nodes based on cloud warehousing under uncertain demand[J]. Journal of Traffic and Transportation Engineering, 2025, 25(2): 189-203. doi: 10.19818/j.cnki.1671-1637.2025.02.012 [28] GUPTA R, MUTTOO S K, PAL S K. Fuzzy C-means clustering and particle swarm optimization based scheme for common service center location allocation[J]. Applied Intelligence, 2017, 47(3): 624-643. doi: 10.1007/s10489-017-0917-0 [29] KUMAR S, PANAGANT N, TEJANI G G, et al. A two-archive multi-objective multi-verse optimizer for truss design [J]. Knowledge-Based Systems, 2023, 270: 110529. doi: 10.1016/j.knosys.2023.110529 [30] OTAIR M, ALHMOUD A, JIA H, et al. Optimized task scheduling in cloud computing using improved multi-verse optimizer[J]. Cluster Computing, 2022, 25(6): 4221-4232. doi: 10.1007/s10586-022-03650-y [31] MIRJALILI S, JANGIR P, MIRJALILI S Z, et al. Optimization of problems with multiple objectives using the multi-verse optimization algorithm[J]. Knowledge-based Systems, 2017, 134: 50-71. doi: 10.1016/j.knosys.2017.07.018 [32] ACHARYA S, GANESAN S, KUMAR D V, et al. A multi-objective multi-verse optimization algorithm for dynamic load dispatch problems[J]. Knowledge-based Systems, 2021, 231: 107411. doi: 10.1016/j.knosys.2021.107411 -
图(15) / 表(3)
计量
- 文章访问数: 24
- HTML全文浏览量: 23
- PDF下载量: 7
- 被引次数: 0
