城市空中交通领域关键技术创新与挑战
收稿日期: 2024-05-10
修回日期: 2024-05-13
录用日期: 2024-05-23
网络出版日期: 2024-06-03
基金资助
国家重点实验室开放研究基金(w222394)
Key technological innovations and challenges in urban air mobility
Received date: 2024-05-10
Revised date: 2024-05-13
Accepted date: 2024-05-23
Online published: 2024-06-03
Supported by
Open Research Fund Program of State Key Laboratory(w222394)
低空经济作为战略性新兴产业,是产业发展的新赛道,经济增长的新引擎。城市空中交通(UAM)是低空经济的重要组成部分。为了解城市空中交通领域的关键技术研究趋势和发展动态,识别研究的热点主题,揭示城市空中交通领域的知识结构和研究范畴,技术演进路径和未来应用前景,以获得当前城市空中交通研究的深入洞见,对Web of Science核心合集(WoSCC)数据库检索和提取的数据进行文献计量分析。结果表明:城市空中交通研究从技术(如避撞技术、高精度定位、垂直起降巡航能力,动态空域划分等)延伸到运营(如城市空中交通运营、无人机物流航线网络,超局部天气预报)以及低噪声着陆条件和消费者接受度,形成了一个全面的框架,目的是实现安全、高效、环保的城市空中交通系统,这些关键技术面临着解决城市低空复杂性的独特挑战,并且在城市空中交通发展过程中,应加强对空中交通管理和环境影响的重视。
余莎莎 , 陈星雨 . 城市空中交通领域关键技术创新与挑战[J]. 航空学报, 2024 , 45(S1) : 730657 -730657 . DOI: 10.7527/S1000-6893.2024.30657
As a strategic emerging industry, low-altitude economy is a new track for industrial development and a new engine for economic growth. Urban Air Mobility (UAM) is an important part of low-altitude economy. To gain insights into the key technological research trends and development dynamics within the UAM sector, identify hot topics of research, and unveil the knowledge structure and research scope in Urban Air Mobility. This includes exploring the technological evolution pathways and future application prospects to acquire a profound understanding of the current state of Urban Air Mobility research. A bibliometric analysis was conducted on data retrieved and extracted from the Web of Science Core Collection (WoSCC) database. From the result, UAM research forms a comprehensive framework from technology (e.g., collision avoidance, high-precision positioning, plus-cruise vertical take-off, dynamic airspace sectorization, etc.) to operations (e.g., urban air mobility operations, unmanned aerial vehicle logistics route network, hyper-local weather prediction) low-noise landing conditions and consumer intention. The aim is to achieve a safe, efficient, and environmentally friendly urban air traffic system, key technologies that face the unique challenge of addressing the complexity of low altitudes in cities, and the importance of air traffic management and environmental impact should be strengthened during the development of the urban air mobility.
1 | 沈映春. 低空经济: “飞” 出新赛道[J]. 人民论坛, 2024(8): 74-79. |
SHEN Y C. Low-altitude economy: “flying” out of the new track[J]. People’s Tribune, 2024(8): 74-79 (in Chinese). | |
2 | 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. |
LI C L, QU W Q, LI Y D, 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 (in Chinese). | |
3 | ?ZTüRK O, KOCAMAN R, KANBACH D K. How to design bibliometric research: An overview and a framework proposal[J]. Review of Managerial Science, 2024: 1-29. |
4 | RAJENDRAN S, SRINIVAS S. Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities[J]. Transportation Research Part E: Logistics and Transportation Review, 2020, 143: 102090. |
5 | RAJENDRAN S, ZACK J. Insights on strategic air taxi network infrastructure locations using an iterative constrained clustering approach[J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 128: 470-505. |
6 | RAJENDRAN S, SHULMAN J. Study of emerging air taxi network operation using discrete-event systems simulation approach[J]. Journal of Air Transport Management, 2020, 87: 101857. |
7 | RAJENDRAN S. Real-time dispatching of air taxis in metropolitan cities using a hybrid simulation goal programming algorithm[J]. Expert Systems with Applications, 2021, 178: 115056. |
8 | 张洪海, 邹依原, 张启钱, 等. 未来城市空中交通管理研究综述[J]. 航空学报, 2021, 42(7): 024638. |
ZHANG H H, ZOU Y Y, ZHANG Q Q, et al. Future urban air mobility management: Review[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(7): 024638 (in Chinese). | |
9 | ZHANG H H, FEI Y H, LI J Y, et al. Method of vertiport capacity assessment based on queuing theory of unmanned aerial vehicles[J]. Sustainability, 2022, 15(1): 709. |
10 | ZHANG Q Q, HUANG X, ZHANG H H, et al. Research on logistics path optimization for a two-stage collaborative delivery system using vehicles and UAVs[J]. Sustainability, 2023, 15(17): 13235. |
11 | YI J, ZHANG H H, WANG F, et al. An operational capacity assessment method for an urban low-altitude unmanned aerial vehicle logistics route network[J]. Drones, 2023, 7(9): 582. |
12 | YI J, ZHANG H H, LI S, et al. Logistics UAV air route network capacity evaluation method based on traffic flow allocation[J]. IEEE Access, 2023, 11: 63701-63713. |
13 | YANG X X, WEI P. Scalable multi-agent computational guidance with separation assurance for autonomous urban air mobility[J]. Journal of Guidance, Control, and Dynamics, 2020, 43(8): 1473-1486. |
14 | YANG X X, WEI P. Autonomous free flight operations in urban air mobility with computational guidance and collision avoidance[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(9): 5962-5975. |
15 | 李安醍, 李诚龙, 武丁杰, 等. 结合跳点引导的无人机随机搜索避撞决策方法[J]. 航空学报, 2020, 41(8): 323726. |
LI A T, LI C L, WU D J, et al. Collision avoidance decision method for UAVs in random search combined with jump point guidance[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(8): 323726 (in Chinese). | |
16 | YWET N L, MAW A A, NGUYEN T A, et al. YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility [J]. Aerospace, 2024, 11(3): 31. |
17 | BERTRAM J, WEI P, ZAMBRENO J. A fast Markov decision process-based algorithm for collision avoidance in urban air mobility[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 15420-15433. |
18 | WU P C, XIE J F, LIU Y C, et al. Risk-bounded and fairness-aware path planning for urban air mobility operations under uncertainty[J]. Aerospace Science and Technology, 2022, 127: 107738. |
19 | HU J M, YANG X X, WANG W C, et al. Obstacle avoidance for UAS in continuous action space using deep reinforcement learning[J]. IEEE Access, 2022, 10: 90623-90634. |
20 | ?REG Z, SHIN H S, TSOURDOS A. Analysis of the traffic conflict situation for speed probability distributions[J]. The Aeronautical Journal, 2023, 127(1314): 1380-1434. |
21 | JOVER J, BERMúDEZ A, CASADO R. Priority-aware conflict resolution for U-space[J]. Electronics, 2022, 11(8): 1225. |
22 | ALDAO E, GONZáLEZ-DE SANTOS L, GONZáLEZ-JORGE H. LiDAR based detect and avoid system for UAV navigation in UAM corridors[J]. Drones, 2022, 6(8): 185. |
23 | CLARKE S G, HWANG S, THAPLIYAL O, et al. Distributed denial-of-service resilient control for urban air mobility applications[J]. Journal of Aerospace Information Systems, 2023, 20(12): 873-889. |
24 | ?REG Z, SHIN H S, TSOURDOS A. On the underlying dynamics of traffic conflicts related to stochastic behaviour[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2023, 237(5): 1078-1093. |
25 | BRITTAIN M W, YANG X X, WEI P. Autonomous separation assurance with deep multi-agent reinforcement learning[J]. Journal of Aerospace Information Systems, 2021, 18(12): 890-905. |
26 | WANG Z Y, DELAHAYE D, FARGES J L, et al. A quasi-dynamic air traffic assignment model for mitigating air traffic complexity and congestion for high-density UAM operations[J]. Transportation Research Part C: Emerging Technologies, 2023, 154: 104279. |
27 | HAN R X, LI H X, APAZA R, et al. Deep reinforcement learning assisted spectrum management in cellular based urban air mobility[J]. IEEE Wireless Communications, 2022, 29(6): 14-21. |
28 | TANG H L, ZHANG Y, MOHMOODIAN V, et al. Automated flight planning of high-density urban air mobility[J]. Transportation Research Part C: Emerging Technologies, 2021, 131: 103324. |
29 | KLEINBEKMAN I C, MITICI M, WEI P. Rolling-horizon electric vertical takeoff and landing arrival scheduling for on-demand urban air mobility[J]. Journal of Aerospace Information Systems, 2020, 17(3): 150-159. |
30 | YAVAS V, YAVA? TEZ ?. Consumer intention over upcoming utopia: Urban air mobility[J]. Journal of Air Transport Management, 2023, 107: 102336. |
31 | LEE C J, BAE B, LEE Y L, et al. Societal acceptance of urban air mobility based on the technology adoption framework[J]. Technological Forecasting and Social Change, 2023, 196: 122807. |
32 | ARIZA-MONTES A, QUAN W, RADIC A, et al. Understanding the behavioral intention to use urban air autonomous vehicles[J]. Technological Forecasting and Social Change, 2023, 191: 122483. |
33 | KIM J J, KIM S S, HAILU T B, et al. Impacts of UAM on tourism: The roles of innovative characteristics, motivated consumer innovativeness, attitude, problem awareness, and cultural differences[J]. Asia Pacific Journal of Tourism Research, 2023, 28(12): 1452-1472. |
34 | KIM J J, RADIC A, ARIZA-MONTES A, et al. Cars are ready to fly: Urban air mobility and psychological process of sustainable travel mode choices[J]. The International Journal of Aerospace Psychology, 2024: 1-21. |
35 | KALAKOU S, MARQUES C, PRAZERES D, et al. Citizens’ attitudes towards technological innovations: The case of urban air mobility[J]. Technological Forecasting and Social Change, 2023, 187: 122200. |
36 | HE X Y, HE F, LI L S, et al. A route network planning method for urban air delivery[J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 166: 102872. |
37 | ZHANG H H, TIAN T, FENG O G, et al. Research on public air route network planning of urban low-altitude logistics unmanned aerial vehicles[J]. Sustainability, 2023, 15(15): 12021. |
38 | BIJJAHALLI S, SABATINI R, GARDI A. GNSS performance modelling and augmentation for urban air mobility[J]. Sensors, 2019, 19(19): 4209. |
39 | SRINIVAS S, WELKER S, HERSCHFELT A, et al. Cramér-Rao lower bounds on 3D position and orientation estimation in distributed ranging systems[J]. Applied Sciences, 2023, 13(3): 2008. |
40 | ARIANTE G, PONTE S, PAPA U, et al. Ground control system for UAS safe landing area determination (SLAD) in urban air mobility operations[J]. Sensors, 2022, 22(9): 3226. |
41 | WANG S Z, ZHAN X Q, ZHAI Y W, et al. Enhancing navigation integrity for urban air mobility with redundant inertial sensors[J]. Aerospace Science and Technology, 2022, 126: 107631. |
42 | NEGRU S A, GERAGERSIAN P, PETRUNIN I, et al. Resilient multi-sensor UAV navigation with a hybrid federated fusion architecture[J]. Sensors, 2024, 24(3): 981. |
43 | YU H G, HERSCHFELT A, WU S Y, et al. Communications and high-precision positioning (CHP2): Hardware architecture, implementation, and validation[J]. Sensors, 2023, 23(3): 1343. |
44 | LEE D, YEE K. Novel electric propulsion system analysis method for electric vertical takeoff and landing aircraft conceptual design[J]. Journal of Aircraft, 2024, 61(2): 375-391. |
45 | 刘文学, 侯聪, 杨亚联, 等. 面向城市空中交通的电动飞行汽车关键性能指标分析[J]. 机械工程学报, 2024: 1-19. |
Liu WX, Hou C, Yang YL, et al. Analysis of key performance indexes of electric flying vehicles for urban air traffic[J]. Chinese Journal of Mechanical Engineering, 2024:1-19. (in Chinese). | |
46 | BHANDARI R, AMAN MISHRA A, CHAKRABORTY I. Optimization of lift-plus-cruise vertical take-off and landing aircraft with electrified propulsion[J]. Journal of Aircraft, 2024, 61(2): 392-414. |
47 | BORETTI A. Advantages of plug-in hybrid electric vertical take-off and landing aircraft with hydrogen energy storage[J]. International Journal of Hydrogen Energy, 2024, 55: 339-346. |
48 | LU Z D, HONG H C, HOLZAPFEL F. Multi-phase vertical take-off and landing trajectory optimization with feasible initial guesses[J]. Aerospace, 2023, 11(1): 39. |
49 | GERDES I, TEMME A, SCHULTZ M. Dynamic airspace sectorisation for flight-centric operations[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 460-480. |
50 | 陈志杰, 汤锦辉, 王冲, 等. 人工智能赋能空域系统,提升空域分层治理能力 [J]. 航空学报, 2021, 42(4): 525018. |
CHEN Z J, TANG J H, WANG C, et al. Artificial intelligence enables airspace system to improve hierarchical governance capability of airspace [J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 7-15 (in Chinese). | |
51 | MURCA M C R. Identification and prediction of urban airspace availability for emerging air mobility operations[J]. Transportation Research Part C: Emerging Technologies, 2021, 131: 103274. |
52 | DAI W, PANG B Z, LOW K H. Conflict-free four-dimensional path planning for urban air mobility considering airspace occupancy[J]. Aerospace Science and Technology, 2021, 119: 107154. |
53 | NITHYA D S, QUARANTA G, MUSCARELLO V, et al. Review of wind flow modelling in urban environments to support the development of urban air mobility[J]. Drones, 2024, 8(4): 147. |
54 | WEI Q S, NILSSON G, COOGAN S. Safe schedule verification for urban air mobility networks with node closures[J]. IEEE Transactions on Control of Network Systems, 2024, 11(2): 855-866. |
55 | REICHE C, COHEN A P, FERNANDO C. An initial assessment of the potential weather barriers of urban air mobility[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(9): 6018-6027. |
56 | ADKINS K A, BECKER W, AYYALASOMAYAJULA S, et al. Hyper-local weather predictions with the enhanced general urban area microclimate predictions tool[J]. Drones, 2023, 7(7): 428. |
57 | CHO H, KO J, JEONG J, et al. Numerical investigation of low-noise landing conditions for multirotor urban air mobility vehicle[J]. Journal of Aircraft, 2024, 61(3): 733-744. |
58 | POGGI C, BERNARDINI G, GENNARETTI M. A minimum objective function trim procedure for VTOLs noise reduction[J]. Aerospace Science and Technology, 2024, 147: 109004. |
59 | KIM Y, LEE S. Deep learning based prediction of urban air mobility noise propagation in urban environment[J]. The Journal of the Acoustical Society of America, 2024, 155(1): 171-187. |
60 | 余莎莎, 陈艺君, 张学军. 城市低空场景下无人机运行对地风险量化评估 [J]. 北京航空航天大学学报, 2024: 1-14. |
Yu SS, Chen YJ, ZHANG XJ. Quantitative assessment of ground risk of UAV operation in urban low-altitude scenarios[J]. Journal of Beijing University of Aeronautics and Astronautics, 2024: 1-14. |
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