A review of Urban Air Mobility-enabled Intelligent Transportation Systems: Mechanisms, applications and challenges

Wang, Leilei; Deng, Xiaoheng; Gui, Jinsong; Jiang, Ping; Zeng, Feng; Wan, Shaohua

doi:10.1016/j.sysarc.2023.102902

Cited by 30 publications

(7 citation statements)

References 36 publications

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“…The expansion of UAM into cities introduces a multitude of stakeholders, including multiple operators and service providers, and the establishment of numerous vertiports across the urban landscape. While this distributed operational network brings about various advantages, such as improved accessibility and flexibility, it also presents significant challenges in terms of managing safety [9,15,44]. Ground transportation services are expected to be part of the UAM mobility scenario.…”

Section: Urban Air Mobility Concept Of Operationsmentioning

confidence: 99%

“…While other studies have addressed operational barriers for UAM operations [13] and challenges to UAM safety management [9,15,44], no work has been conducted to address organizational perspectives to foster a just culture in UAM. The results in Section 4 consider the UAM ecosystem perspectives, including operators, service providers, physical infrastructure, authority, and the public.…”

Section: Urban Air Mobility Concept Of Operationsmentioning

confidence: 99%

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Exploring Safety Culture in Urban Air Mobility: System of Systems Perspectives Using Enterprise Architecture

Hoffmann,

Nishimura,

Gomes

2024

Systems

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Urban Air Mobility (UAM) is an emerging industry marked by technological advancements, new operational contexts, and regulatory frameworks. This article examines how to improve safety management in UAM operations by adopting a just culture approach from a system of systems perspective. Acknowledging the critical role of front-line workers, especially in the early stage with piloted vehicles, the ecosystem-level approach comprehends multiple providers, operators, and services. Employing an enterprise architecture methodology, we address the challenge of fostering a learning-oriented environment amidst diverse organizational perspectives and stakeholders’ interests. This study identifies key capabilities, functions, and resource exchanges within and across organizations by strategically leveraging architectural views and systemic visualizations. A unified safety committee is discussed and recommended to facilitate consensus among stakeholders, including regulatory bodies, thus paving the way for industry-wide improvements. Findings contribute to evolving safety protocols in UAM operations and serve as a blueprint for integrating cutting-edge methodologies to drive systemic enhancements.

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Section: Urban Air Mobility Concept Of Operationsmentioning

confidence: 99%

Section: Urban Air Mobility Concept Of Operationsmentioning

confidence: 99%

Exploring Safety Culture in Urban Air Mobility: System of Systems Perspectives Using Enterprise Architecture

Hoffmann,

Nishimura,

Gomes

2024

Systems

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“…The literature has overwhelmingly covered the various motivations for AAM deployments. The top motivation is to escape the negative effects of ground traffic congestion by more efficiently utilizing the third dimension of travel [6]. Other motivations for AAM deployments include reducing the risk of environmental damage and economic issues caused by existing ground transportation systems [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Integrating Electric Vertical Takeoff and Landing Aircraft into Public Airspace: A Scenario Study

Bridgelall,

White,

Tolliver

2023

Future Transportation

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The successful deployment of Advanced Air Mobility (AAM) requires the safe integration of electric vertical takeoff and landing (eVTOL) aircraft in non-segregated airspace. However, a lack of practical deployment demonstrations can slow the regulatory progress. This study presents a practical deployment framework for the integration of eVTOL aircraft into public airspace, in accordance with the Federal Aviation Administration (FAA) special airworthiness criteria. This study focused on a corporate use case to provide a comprehensive scenario for enhancing intra-company mobility. The scenario supports early implementation and demonstration goals by removing reliance on air traffic management systems or air traffic controller separation services. The practical deployment framework included facility selection, route planning, terrain mapping, collision avoidance, aircraft selection, infrastructure requirements, vertiport placement, electric chargers, ground handling, operational staff, and cost estimates. Planners can use the framework as a template to inform the implementation of real-world AAM services using eVTOL aircraft.

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“…At present, the capture of original traffic data in most cities in the world mainly comes from two channels, namely, floating car GPS data and road sensor data [26]. Therefore, in this paper, we try to process the original traffic data through different methods to form a graph network and related traffic feature data and then use the graph neural network machine learning algorithm to predict.…”

Section: Introductionmentioning

confidence: 99%

Traffic prediction for diverse edge IoT data using graph network

Shen,

Zhang,

Geng

et al. 2024

J Cloud Comp

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More researchers are proposing artificial intelligence algorithms for Internet of Things (IoT) devices and applying them to themes such as smart cities and smart transportation. In recent years, relevant research has mainly focused on data processing and algorithm modeling, and most have shown good prediction results. However, many algorithmic models often adjust parameters for the corresponding datasets, so the robustness of the models is weak. When different types of data face other model parameters, the prediction performance often varies a lot. Thus, this work starts from the perspective of data processing and algorithm models. Taking traffic data as an example, we first propose a new data processing method that processes traffic data with different attributes and characteristics into a dataset that is more common for most models. Then we will compare different types of datasets from the perspective of multiple model parameters, and further analyze the precautions and changing trends of different traffic data in machine learning. Finally, different types of data and ranges of model parameters are explored, together with possible reasons for fluctuations in forecast results when data parameters change.

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A review of Urban Air Mobility-enabled Intelligent Transportation Systems: Mechanisms, applications and challenges

Cited by 30 publications

References 36 publications

Exploring Safety Culture in Urban Air Mobility: System of Systems Perspectives Using Enterprise Architecture

Exploring Safety Culture in Urban Air Mobility: System of Systems Perspectives Using Enterprise Architecture

Integrating Electric Vertical Takeoff and Landing Aircraft into Public Airspace: A Scenario Study

Traffic prediction for diverse edge IoT data using graph network

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