| 307 | 0 | 70 |
| 下载次数 | 被引频次 | 阅读次数 |
【背景】随着低空空域开放与无人机系统发展，城市低空中“有人机-无人机”混合运行逐渐成为常态，但安全与容量平衡仍面临挑战。【目标】在混合运行场景下建立统一的微观与宏观联合建模框架，揭示异构平台下微观行为差异对交通流安全与效率的影响规律，以支撑低空空域容量评估与运行优化。【方法】构建结构化静态安全间隔模型，引入刺激-响应机制建立微观速度控制模型，并利用马尔科夫链方法实现微观行为向宏观状态的映射，生成三参数交通流基本图。【结果】与同构系统相比，混合运行的临界密度右移且在高密度出现低速稳定平台，容量边界得到拓展；当初始间距为1.0～1.3倍的静态阈值、无人机巡航速度约为有人机的110%、响应时延小于1.5 s时，系统可在密度10～13架/km区间内实现容量与稳定性的最优平衡。【结论】微观-宏观耦合框架能够在保证安全的前提下优化低空空域容量利用，揭示异构飞行器在动态协同下的安全-效率权衡机制。【应用】研究成果为低空空域容量预测、运行调度与策略优化提供量化建模工具。
Abstract：[Background] With the opening of low-altitude airspace and the rapid development of unmanned aircraft systems， mixed operations of “manned aircraft–unmanned aircraft” in urban low-altitude environments are becoming increasingly common; however， the balance between safety and capacity still faces challenges. [Objective] To establish a unified micro–macro coupled modeling framework for mixed operations， revealing how micro-level behavioral differences among heterogeneous platforms affect traffic flow safety and efficiency， thereby supporting low-altitude airspace capacity assessment and operational optimization. [Method] A structured static safety-separation model is constructed; a micro-level speed-control model is developed by introducing a stimulus–response mechanism; and a Markov chain method is used to map micro-level behaviors to macro-level states， generating a three-parameter fundamental diagram of traffic flow. [Result] The results show that， compared with homogeneous systems， the critical density of mixed operations shifts rightward and a low-speed stable plateau emerges at high densities， leading to an expanded capacity boundary. When the initial spacing is 1.0–1.3 times the static threshold， the UAV cruising speed is about 110% of that of manned aircraft， and the response delay is less than 1.5 s， the system achieves an optimal balance between capacity and stability within the density range of 10–13 aircraft/km. [Conclusion] The micro–macro coupled framework can optimize low-altitude airspace capacity utilization while ensuring safety， and it reveals the safety–efficiency trade-off mechanism of heterogeneous aircraft under dynamic coordination. [Application] The findings provide quantitative modeling tools for low-altitude airspace capacity prediction， operational scheduling， and strategy optimization.
[1] BAO D， YAN Y， LI Y， et al. The future of last-mile delivery: lifecycle environmental and economic impacts of drone-truck parallel systems[J]. Drones， 2025， 9(1): 54.
[2] 王俊潼， 包丹文， 周佳怡， 等. 低空空域规划研究现状与展望[J]. 航空学报， 2025， 46(11): 82-107. WANG Juntong， BAO Danwen， ZHOU Jiayi， et al. Low-altitude airspace planning: a review and prospect[J]. Acta Aeronautica et Astronautica Sinica， 2025， 46(11): 82-107.
[3] 范文博， 肖杰， 吴艺楷， 等. 低空飞行器运行优化决策方法综述与展望[J]. 交通运输工程与信息学报， 2025， 23(3): 37-59. FAN Wenbo， XIAO Jie， WU Yikai， et al. A review of optimization methods for low-altitude aircraft operations[J]. Journal of Transportation Engineering and Information， 2025， 23(3): 37-59.
[4] 包丹文， 尚静萱， 韦达， 等. 漏斗形空域结构下无人机双目标起降联合调度模型[J]. 交通运输工程与信息学报， 2025， 23(4): 36-49. BAO Danwen， SHANG Jingxuan， WEI Da， et al. Joint scheduling model for dual-target takeoff and landing of unmanned aerial vehicles under a funnel-shaped airspace structure[J]. Journal of Transportation Engineering and Information， 2025， 23(4): 36-49.
[5] DENHAM C L， CUMMINGS W G， SMITH J C. Theoretical and simulated capacity of urban air mobility airspace characteristics[C]//AIAA SCITECH 2023 Forum. National Harbor & Online: AIAA， 2023: AIAA2023-0546.
[6] ALVAREZ L E， JONES J C， BRYAN A， et al. Demand and capacity modeling for advanced air mobility[C]//AIAA AVIATION 2021 FORUM. VIRTUAL EVENT. AIAA， 2021: AIAA2021-2381.
[7] SU Y， XU Y， INALHAN G. A risk-based UAM airspace capacity assessment method using Monte Carlo simulation[C]//2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC). Barcelona: IEEE， 2023: 1-10.
[8] ZHANG J， LI Z， LUO X， et al. Study of urban unmanned aerial vehicle separation in free flight based on track prediction[J]. Applied Sciences， 2024， 14(13): 5712.
[9] CELDRAN MARTINEZ V， SHIN H S， TSOURDOS A. Risk assessment for sUAS in urban environments: a comprehensive analysis， modelling and validation for safe operations[C]//AIAA SCITECH 2024 Forum. Orlando: AIAA， 2024: AIAA2024-0232.
[10] 李善梅， 纪亚宏， 王超， 等. 考虑加减速行为不对称的终端区空中交通流跟驰模型[J]. 交通运输系统工程与信息， 2024， 24(6): 286-297. LI Shanmei， JI Yahong， WANG Chao， et al. An aircraft-following model for air traffic flow in terminal airspace considering asymmetric acceleration and deceleration behavior[J]. Journal of Transportation Systems Engineering and Information Technology， 2024， 24(6): 286-297.
[11] 王莉莉， 赵云飞， 郭微萌. 基于飞联网运行的航空器多速度差跟驰模型[J]. 北京航空航天大学学报， 2025， 51(6): 1873-1881. WANG Lili， ZHAO Yunfei， GUO Weimeng. Aircraft multi-velocity difference car-following model based on flight networking operation[J]. Journal of Beijing University of Aeronautics and Astronautics， 2025， 51(6): 1873-1881.
[12] PAN Y A， GUO J， CHEN Y， et al. A fundamental diagram based hybrid framework for traffic flow estimation and prediction by combining a Markovian model with deep learning[J]. Expert Systems with Applications， 2024， 238: 122219.
[13] ZHANG X， SUN J， SUN J. On the stochastic fundamental diagram: a general micro-macroscopic traffic flow modeling framework[J]. Communications in Transportation Research， 2025， 5: 100163.
[14] BESENCZI R， BáTFAI N， JESZENSZKY P， et al. Large-scale simulation of traffic flow using Markov model[J]. PLoS One， 2021， 16(2): e0246062.
[15] LI C， LONG T， HU Y， et al. Dynamic MPC obstacle avoidance based tube sparse A* path planning method for UAV swarm under communication delays[M]//Advances in Guidance， Navigation and Control. Singapore: Springer Nature Singapore， 2023: 3295-3305.
[16] 贺正冰. 微观交通模型: 智能网联化转型与通用驾驶人模型框架[J]. 交通运输工程与信息学报， 2022， 20(2): 1-13. HE Zhengbing. Microscopic traffic models: transformation in connected environ-ment and generalized driver model[J]. Journal of Transportation Engineering and Information， 2022， 20(2): 1-13.
[17] 张健， 黎宗孝， 罗鑫悦， 等. 冲突探测与解脱机制下城市物流无人机自由飞行间隔研究[J]. 科学技术与工程， 2024， 24(30): 13229-13235. ZHANG Jian， LI Zongxiao， LUO Xinyue， et al. Urban logistics unmanned aerial vehicle free flight separation considering conflict detection and resolution mechanism[J]. Science Technology and Engineering， 2024， 24(30): 13229-13235.
[18] HOOGENDOORN S P， DAAMEN W， KNOOP V L， et al. Macroscopic Fundamental Diagram for pedestrian networks: theory and applications[J]. Transportation Research Procedia， 2017， 23: 480-496.
[19] LIU L， XU M， WANG Z， et al. Delay-informed intelligent formation control for UAV-assisted IoT application[J]. Sensors， 2023， 23(13): 6190.
[20] 林亨， 方华健， 吴冬雁. 基于动态安全距离的 CACC 车辆混合交通流模型[J]. 北京交通大学学报， 2022， 46(6): 36-42. LIN Heng， FANG Huajian， WU Dongyan. CACC vehicle mixed traffic flow model based on dynamic safety distance[J]. Journal of Beijing Jiaotong University， 2022， 46(6): 36-42.
[21] ZHUANG X， ZHANG J， TIAN J， et al. Variable time headway spacing strategy for connected vehicles platoon based on sliding mode control[J]. Physica A: Statistical Mechanics and Its Applications， 2024， 637: 129588.
[22] LI X， YOU Z， MA X， et al. Effect of autonomous vehicles on car-following behavior of human drivers: Analysis based on structural equation models[J]. Physica A: Statistical Mechanics and Its Applications， 2024， 633: 129360.
[23] ZHANG C， WANG W， SUN L. Calibrating car-following models via Bayesian dynamic regression[J]. Transportation Research Part C: Emerging Technologies， 2024， 168: 104719.
[24] ZHONG G， DU S， ZHANG H， et al. Demarcation method of safety separations for sUAV based on collision risk estimation[J]. Reliability Engineering & System Safety， 2024， 242: 109738.
[25] XIE H， JI X， YIN J， et al. Standardization of safety separation for multi-category unmanned aerial vehicles in low-altitude airspace operations[J]. Applied Sciences， 2025， 15(13): 7501.
[26] HADDAD J， MIRKIN B， ASSOR K. Traffic flow modeling and feedback control for future low-altitude air city transport: an MFD-based approach[J]. Transportation Research Part C: Emerging Technologies， 2021， 133: 103380.
[27] 韩鹏， 周斌， 张恩宇. 终端区多场景有人机/无人机空中碰撞风险研究[J]. 西华大学学报(自然科学版)， 2022， 41(2): 8-11， 50.
[28] LI R J， SUN S Y， WU Y X， et al. Fundamental diagram of mixed traffic flow considering time lags， platooning intensity， and the degradation of connected automated vehicles[J]. Physica A: Statistical Mechanics and Its Applications， 2023， 627: 129130.
基本信息:
DOI：10.19961/j.cnki.1672-4747.2025.12.002
中图分类号:V35
引用信息:
[1]刘子聪,包丹文,仇逸芙.有人机-无人机混合低空交通建模：微观-宏观耦合框架[J].交通运输工程与信息学报().DOI:10.19961/j.cnki.1672-4747.2025.12.002.
2026-01-23
2026-01-23
2026-01-23