Multi-source low-altitude risk quantification and route generation in urban environments

doi:10.11947/j.AGCS.2026.20250371

Abstract

Abstract:

The development and utilization of low-altitude airspace are gradually becoming a strategic pillar for advancing the low-altitude economy. However, the massive influx of low-altitude aircraft poses unprecedented safety challenges over urban areas. How to scientifically, comprehensively, and accurately quantify the risk distribution in complex urban low-altitude environments, and subsequently generate safe and efficient air routes, has become a critical scientific problem. Existing research often overlooks the multi-dimensional coupled risks between urban low-altitude and ground space, and air route generation algorithms are typically confined to local search, making it difficult to achieve accurate risk quantification and globally optimal route generation. To this end, this paper proposes a low-altitude risk quantification method and an intelligent route generation model constrained primarily by ground risk. Firstly, by fusing three types of data—population density, building density, and land use type—we propose a low-altitude risk factor quantification and combined weighting method to construct a high-medium-low multi-level low-altitude risk distribution map. Secondly, an intelligent air route generation model based on adaptive particle swarm optimization (APSO-AR) is proposed. By incorporating an adaptive air route node construction, a risk perception sampling mechanism, and an interval penalty mechanism, the model achieves global risk perception and optimal route generation for low-altitude aircraft within complex urban scenes. Finally, air route generation experiments were conducted using Wuhan city as the research area. The results demonstrate that, compared to ant colony optimization (ACO), genetic algorithm (GA), risk A^* algorithm, and the traditional particle swarm optimization (PSO), the air routes generated by APSO-AR show an average reduction of 12.0% in route length and an average reduction of 30.7% in risk level, while the average code runtime is reduced by 77.4%. The model simultaneously ensures route optimality, risk minimization, and global rationality. This paper provides a new technical path for “risk quantification—route generation”, providing methodological support for urban low-altitude risk map construction and route planning.

Key words: risk quantification, route generation, particle swarm optimization algorithm, low-altitude economy

CLC Number:

P237

Qinghua Tan, Heng Qi, Hongyu Shi, Luliang Tang, Zihan Kan, Hong Yang, Lele Sun, Yafei Liu, Zhengxiong Gu. Multi-source low-altitude risk quantification and route generation in urban environments[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(7): 1306-1320.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://xb.chinasmp.com/EN/10.11947/j.AGCS.2026.20250371

http://xb.chinasmp.com/EN/Y2026/V55/I7/1306

Figures/Tables 15

Fig. 1

Tab. 1

Tab. 2

Tab. 3

Fig. 2

Tab. 4

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Tab. 5

Fig. 7

Tab. 6

Fig. 8

Fig. 9

References 47

[1]	唐炉亮, 齐恒, 施泓羽, 等. 低空航路地图：支撑低空经济规模化飞行安全的公共基础设施[J]. 科学通报, 2026, 71(13): 2919-2932.
	Tang Luliang, Qi Heng, Shi Hongyu, et al. Low-altitude sky-lane map: a new infrastructure of the high-density flying safety for low-altitude economy[J]. Science Bulletin, 2026, 71(13): 2919-2932.
[2]	李晓华. 我国低空消费的发展现状与前景[J]. 人民论坛, 2026(2): 74-78.
	Li Xiaohua. Current status and prospects of low-altitude consumption in China[J]. People's Tribune, 2026(2): 74-78.
[3]	廖小罕, 屈文秋, 徐晨晨, 等. 城市空中交通及其新型基础设施低空公共航路研究综述[J]. 航空学报, 2023, 44(24): 1-29.
	Liao Xiaohan, Qu Wenqiu, Xu Chenchen, et al. A review of urban air mobility and its new infrastructure low-altitude public routes[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(24): 1-29.
[4]	晏磊, 廖小罕, 周成虎, 等. 中国无人机遥感技术突破与产业发展综述[J]. 地球信息科学学报, 2019, 21(4): 475-495.
	Yan Lei, Liao Xiaohan, Zhou Chenghu, et al. The impact of UAV remote sensing technology on the industrial development of China: a review[J]. Journal of Geo-Information Science, 2019, 21(4): 475-495.
[5]	贾永楠. 低空空域无人系统交通管理方案初探[J]. 航空学报, 2025, 46(11): 114-140.
	Jia Yongnan. A scheme for unmanned aerial system traffic management in low-altitude airspace[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 114-140.
[6]	全权, 李刚, 柏艺琴, 等. 低空无人机交通管理概览与建议[J]. 航空学报, 2020, 41(1): 023238.
	Quan Quan, Li Gang, Bai Yiqin, et al. Low altitude UAV traffic management: an introductory overview and proposal[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(1): 023238.
[7]	徐晨晨, 叶虎平, 岳焕印, 等. 城镇化区域无人机低空航路网迭代构建的理论体系与技术路径[J]. 地理学报, 2020, 75(5): 917-930.
	Xu Chenchen, Ye Huping, Yue Huanyin, et al. Iterative construction of UAV low-altitude air route network in an urbanized region: theoretical system and technical roadmap[J]. Acta Geographica Sinica, 2020, 75(5): 917-930.
[8]	张洪海, 李姗, 夷珈, 等. 城市低空航路规划研究综述[J]. 南京航空航天大学学报, 2021, 53(6): 827-838.
	Zhang Honghai, Li Shan, Yi Jia, et al. Review on urban low-altitude air route planning[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2021, 53(6): 827-838.
[9]	廖小罕, 徐晨晨, 叶虎平. 低空经济发展与低空路网基础设施建设的效益和挑战[J]. 中国科学院院刊, 2024, 39(11): 1966-1981.
	Liao Xiaohan, Xu Chenchen, Ye Huping. Benefits and challenges of constructing low-altitude air route network infrastructure for developing low-altitude economy[J]. Bulletin of Chinese Academy of Sciences, 2024, 39(11): 1966-1981.
[10]	La Cour-Harbo A. Quantifying risk of ground impact fatalities for small unmanned aircraft[J]. Journal of Intelligent & Robotic Systems, 2019, 93(1): 367-384.
[11]	郑秀梅, 李智恒. 城市空中交通感知风险识别与评价：基于在线评论数据[J]. 安全与环境学报, 2025, 25(8): 3143-3150.
	Zheng Xiumei, Li Zhiheng. Identification and assessment of perceived risks in UAM based on online review data[J]. Journal of Safety and Environment, 2025, 25(8): 3143-3150.
[12]	Sunil E, Ellerbroek J, Hoekstra J, et al. Analysis of airspace structure and capacity for decentralized separation using fast-time simulations[J]. Journal of Guidance, Control, and Dynamics, 2017, 40(1): 38-51.
[13]	He Xinyu, Li Lishuai, Mo Yanfang, et al. Air corridor planning for urban drone delivery: complexity analysis and comparison via multi-commodity network flow and graph search[J]. Transportation Research Part E: Logistics and Transportation Review, 2025, 193: 103859.
[14]	Primatesta S, Rizzo A, La Cour-Harbo A. Ground risk map for unmanned aircraft in urban environments[J]. Journal of Intelligent & Robotic Systems, 2020, 97(3): 489-509.
[15]	胡莘婷, 戴福青. 基于城区行人安全的无人机运行风险评估[J]. 中国安全科学学报, 2020, 30(8): 137-142.
	Hu Xinting, Dai Fuqing. Risk assessment model for UAV operation considering safety of ground pedestrians in urban areas[J]. China Safety Science Journal, 2020, 30(8): 137-142.
[16]	王文涛, 甘旭升, 吴亚荣, 等. 考虑不确定性的低空无人机运行风险评估方法[J]. 现代防御技术, 2022, 50(5): 14-21.
	Wang Wentao, Gan Xusheng, Wu Yarong, et al. Low-altitude UAV operation risk assessment method considering the uncertainty[J]. Modern Defense Technology, 2022, 50(5): 14-21.
[17]	Zhang Honghai, Zou Yiyuan, Wang Zhongye, et al. Assessment model of safety situation in low-altitude airspace based on AGA-FCM[C]//Proceedings of 4th International Conference on Electromechanical Control Technology and Transportation (ICECTT). Guilin: IEEE, 2019: 10-17.
[18]	Zhao Zhenyu, Zhang Lanfang, Zhou Ruida, et al. A flight risk field model for advanced low-altitude transportation system using field theory[J]. Transportation Research Part A: Policy and Practice, 2024, 190: 104268.
[19]	Levitt I, Phojanamongkolkij N, Horn A, et al. UAM airspace research roadmap-Rev.2.0[R/OL]. (2023-03-01) [2025-04-09]. https://ntrs.nasa.gov/citations/20230002647.
[20]	McFadyen A, Bruggemann T. Unmanned air traffic network design concepts[C]//Proceedings of 2017 IEEE International Conference on Intelligent Transportation Systems. Yokohama: IEEE, 2017: 1-7.
[21]	Fu Yangguang, Ding Mingyue, Zhou Chengping, et al. Route planning for unmanned aerial vehicle (UAV) on the sea using hybrid differential evolution and quantum-behaved particle swarm optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013, 43(6): 1451-1465.
[22]	Gharibi M, Boutaba R, Waslander S L. Internet of drones[J]. IEEE Access, 2016, 4: 1148-1162.
[23]	张海阔, 孟秀云. 基于改进粒子群算法的无人机航迹规划[J]. 飞行力学, 2024, 42(2): 29-35.
	Zhang Haikuo, Meng Xiuyun. UAV route planning based on improved particle swarm optimization algorithm[J]. Flight Dynamics, 2024, 42(2): 29-35.
[24]	Zhao Pengfei. Optimization of LSTM ship trajectory prediction based on hybrid genetic algorithm[J]. Journal of Geodesy and Geoinformation Science, 2024, 7(3): 89-102.
[25]	徐晨晨, 廖小罕, 岳焕印, 等. 基于改进蚁群算法的无人机低空公共航路构建方法[J]. 地球信息科学学报, 2019, 21(4): 570-579.
	Xu Chenchen, Liao Xiaohan, Yue Huanyin, et al. Construction of a UAV low-altitude public air route based on an improved ant colony algorithm[J]. Journal of Geo-Information Science, 2019, 21(4): 570-579.
[26]	李士途, 姜灵芝. 基于A^*算法的无人机智能避障路径及实时航路规划研究[J]. 自动化与仪器仪表, 2024(12): 67-71.
	Li Shitu, Jiang Lingzhi. Intelligent obstacle avoidance path and real-time navigation route planning of UAV based on A^* algorithm[J]. Automation & Instrumentation, 2024(12): 67-71.
[27]	Rudnick-Cohen E, Herrmann J W, Azarm S. Risk-based path planning optimization methods for unmanned aerial vehicles over inhabited Areas1[J]. Journal of Computing and Information Science in Engineering, 2016, 16(2): 021004.
[28]	张宏宏, 甘旭升, 李双峰, 等. 复杂低空环境下考虑区域风险评估的无人机航路规划[J]. 仪器仪表学报, 2021, 42(1): 257-266.
	Zhang Honghong, Gan Xusheng, Li Shuangfeng, et al. UAV route planning considering regional risk assessment under complex low altitude environment[J]. Chinese Journal of Scientific Instrument, 2021, 42(1): 257-266.
[29]	Primatesta S, Guglieri G, Rizzo A. A risk-aware path planning strategy for UAVs in urban environments[J]. Journal of Intelligent & Robotic Systems, 2019, 95(2): 629-643.
[30]	张瑞鑫, 徐青, 吕峥, 等. 基于高斯混合回归与改进A^*算法的时间最优路径规划方法[J]. 测绘学报, 2025, 54(6): 1139-1151. DOI: . doi: 10.11947/j.AGCS.2025.20240478
	Zhang Ruixin, Xu Qing, Lü Zheng, et al. Time optimal path planning method based on Gaussian mixture regression and improved A^* algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(6): 1139-1151. DOI: . doi: 10.11947/j.AGCS.2025,20240478
[31]	Washington A, Clothier R A, Silva J. A review of unmanned aircraft system ground risk models[J]. Progress in Aerospace Sciences, 2017, 95: 24-44.
[32]	Dobson J E, Bright E A, Coleman P R, et al. LandScan: a global population database for estimating populations at risk[J]. Photogrammetric Engineering and Remote Sensing, 66(7): 849-857.
[33]	Batista e Silva F, Freire S, Schiavina M, et al. Uncovering temporal changes in Europe's population density patterns using a data fusion approach[J]. Nature Communications, 2020, 11: 4631.
[34]	Deville P, Linard C, Martin S, et al. Dynamic population mapping using mobile phone data[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(45): 15888-15893.
[35]	GB 50137—2011城市用地分类与规划建设用地标准[S].
	GB 50137—2011 Code for classification of urban land use and planning standards of development land[S].
[36]	高玉琴, 王慧, 刘钺, 等. 基于空间信息格网的南京市洪水风险评估[J]. 水利水电科技进展, 2024, 44(6): 6-12.
	Gao Yuqin, Wang Hui, Liu Yue, et al. Flood disaster risk assessment in Nanjing city based on spatial information grids[J]. Advances in Science and Technology of Water Resources, 2024, 44(6): 6-12.
[37]	郭金玉, 张忠彬, 孙庆云. 层次分析法的研究与应用[J]. 中国安全科学学报, 2008, 18(5): 148-153.
	Guo Jinyu, Zhang Zhongbin, Sun Qingyun. Study and applications of analytic hierarchy process[J]. China Safety Science Journal, 2008, 18(5): 148-153.
[38]	Sinaga K P, Yang M S. Unsupervised K-means clustering algorithm[J]. IEEE Access, 2020, 8: 80716-80727.
[39]	冯茜, 李擎, 全威, 等. 多目标粒子群优化算法研究综述[J]. 工程科学学报, 2021, 43(6): 745-753.
	Feng Qian, Li Qing, Quan Wei, et al. Overview of multiobjective particle swarm optimization algorithm[J]. Chinese Journal of Engineering, 2021, 43(6): 745-753.
[40]	Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996, 26(1): 29-41.
[41]	范洪达, 叶文, 马向玲. 基于遗传算法的飞机低空突防航路规划[J]. 航空计算技术, 2003, 33(1): 13-17.
	Fan Hongda, Ye Wen, Ma Xiangling. Low altitude penetration route planning of the plane based on genetic algorithm[J]. Aeronautical Computer Technique, 2003, 33(1): 13-17.
[42]	Ramírez-Ochoa D D, Pérez-Domínguez L A, Martínez-Gómez E A, et al. PSO, a swarm intelligence-based evolutionary algorithm as a decision-making strategy: a review[J]. Symmetry, 2022, 14(3): 455.
[43]	武汉市人民政府门户网站. 市人民政府关于印发武汉市支持低空经济高质量发展若干措施的通知[EB/OL]. [2025-08-29]. https://www.wuhan.gov.cn/zwgk/xxgk/zfwj/gfxwj/202406/t20240607_2413516.shtml.
	Wuhan Municipal People's Government. Notice of the Wuhan Municipal People's Government on Issuing Several Measures to Support the High-quality Development of the Low-altitude Economy in Wuhan[EB/OL]. [2025-08-29]. https://www.wuhan.gov.cn/zwgk/xxgk/zfwj/gfxwj/202406/t20240607_2413516.shtml.
[44]	Chen Yuehong, Xu Congcong, Ge Yong, et al. A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data[J]. Earth System Science Data, 2024, 16(8): 3705-3718.
[45]	Liu Zeping, Tang Hong, Feng Lin, et al. CBRA: The first multi-annual (2016-2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with Super-resolution Segmentation from Sentinel-2 imagery[DS/OL]. [2025-08-28]. https://zenodo.org/records/7500612.
[46]	Aleti A, Moser I. A systematic literature review of adaptive parameter control methods for evolutionary algorithms[J]. ACM Computing Surveys, 2016, 49(3): 1-35.
[47]	Trelea I C. The particle swarm optimization algorithm: convergence analysis and parameter selection[J]. Information Processing Letters, 2003, 85(6): 317-325.

风险度	风险区域	风险类型	用地分类
0.85	建筑	硬性聚集风险	H-建设用地、R-居住用地、A-公共管理与公共服务用地、B-商业服务业设施用地、M-工业用地、W-物流仓储用地
0.50	道路	动态损失风险	S-道路与交通设施用地
0.25	植被	柔性缓冲风险	G-绿地与广场用地、E-非建设用地
0.10	水域	柔性缓冲风险	E-非建设用地（E1水域）

风险因子	特征向量	权重值/（%）	最大特征根	CR值
人口密度	1.471	49.048	3.054	0.027
建筑密度	0.593	19.762
土地覆盖	0.936	31.190

风险因子	AHP权重	熵值法权重	组合权重
人口	0.490	0.395	0.44
建筑	0.198	0.418	0.31
土地	0.312	0.188	0.25

风险级别	聚类中心	风险值下界	风险值上界
低风险	0.024 0	0.000 0	0.184 1
中风险	0.344 1	0.184 1	0.446 1
高风险	0.548 1	0.446 1	1.000 0

航路名称	起点		终点		APSO-AR生成航路情况
航路名称	像素坐标	地理坐标	像素坐标	地理坐标	航路综合代价	航路长度/m	风险度
航路a	（85，7）	30.546°N，114.298°E	（18，175）	30.519°N，114.382°E	96.94	9 767.02	0.084
航路b	（46，0）	30.528°N，114.294°E	（0，136）	30.508°N，114.363°E	81.00	7 336.45	0.186
航路c	（147，36）	30.576°N，114.312°E	（58，182）	30.533°N，114.389°E	82.00	8 647.15	0.044
航路d	（0，93）	30.508°N，114.341°E	（154，142）	30.577°N，114.367°E	85.44	8 379.25	0.109
航路e	（153，116）	30.576°N，114.354°E	（65，62）	30.536°N，114.325°E	60.92	5 786.55	0.139
航路f	（0，7）	30.508°N，114.297°E	（70，48）	30.539°N，114.319°E	42.65	4 070.69	0.134