Distributed Vision Sensing of Small Uncrewed Aircraft Systems in Urban Traffic Corridors
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Chester Dolph↗
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Cyber Resiliency and the Implementation of a Host-Based Intrusion Detection System in an Urban Air Mobility Environment
With the growth in urban air mobility systems and the increasing reliance on interconnected technologies, ensuring the security of these complex infrastructures has become critical. As cities evolve into smart urban centers, the vulnerability to cyber threats escalates, possibly endangering citizens’ safety and the efficiency of transportation networks. In response to these challenges, this paper presents a study on implementing a Host-Based Intrusion Detection System (HIDS) tailored explicitly to urban mobility environments’ unique demands. This study explores the design, development, and deployment of the proposed HIDS, focusing on its adaptability to monitor the hybrid nature of the urban mobility environment. Leveraging machine learning algorithms and anomaly detection techniques, the HIDS is equipped to continuously monitor and analyze the behavior of individual host systems, vehicles, and devices, thereby providing a proactive approach to threat detection. Implementing a HIDS is a pivotal strategy for enhancing cyber resiliency, as it gives an organization granular visibility into internal system activities, enables rapid detection and response to anomalous behavior and cyber threats, and fortifies the organization’s overall cybersecurity posture. In conclusion, this paper highlights the significance of host-based intrusion detection in urban mobility environments and underscores the necessity of tailored security solutions to safeguard against emerging cyber threats.
UAM↗
Cyber Resiliency and the Implementation of a Host-Based Intrusion Detection System in an Urban Air Mobility Environment
With the growth in Urban Air Mobility systems and the increasing reliance on interconnected technologies, ensuring the security of these complex components has become critical. As cities evolve into smart urban centers, the vulnerability to cyber threats escalates, possibly endangering citizens safety and the efficiency of transportation networks.In response to these challenges, this paper presents a study on the need for cyber resilient techniques within future air traffic environments. It will pay specific attention to the implementation of a Host-Based Intrusion Detection System (HIDS) utilizing Atomic OSSEC software, tailored specifically to a NASA simulation of an UrbanAirMobility environments’ unique demands. Further, this study seeks to outline the rational for NASA’s recommendation for a HIDS in such environments. It explores the design, development, and deployment of the proposed HIDS, focusing on its adaptability to monitor the hybrid nature of the Urban Air Mobility environment. Leveraging machine learning algorithms and anomaly detection techniques, the HIDS is equipped to continuously monitor and analyze the behavior of individual host systems, vehicles, and devices, thereby providing a proactive approach to threat detection. Implementing a HIDS is a pivotal strategy for enhancing cyber resiliency, as it gives an organization granular visibility into internal system activities, enables rapid detection and response to anomalous behavior and cyber threats, and fortifies the organizations overall cybersecurity posture. Finally, this study aims to provide recommendations and include learned takeaways that the Urban Air Mobility industry should consider. In brief, this paper highlights the significance of host-based intrusion detection in UrbanAirMobility environments and underscores the necessity of tailored security solutions to safeguard against emerging cyber threats.
UAM↗
Urban Air Mobility System Testbed Using CAVE Virtual Reality Environment
Urban Air Mobility (UAM) refers to a system of air passenger and small cargo transportation within an urban area. The UAM framework also includes other urban Unmanned Aerial Systems (UAS) services that will be supported by a mix of onboard, ground, piloted, and autonomous operations. Over the past few years UAM research has gained wide interest from companies and federal agencies as an on-demand innovative transportation option that can help reduce traffic congestion and pollution as well as increase mobility in metropolitan areas. The concepts of UAM/UAS operation in the National Airspace System (NAS) remains an active area of research to ensure safe and efficient operations. With new developments in smart vehicle design and infrastructure for air traffic management, there is a need for methods to integrate and test various components of the UAM framework. In this work, we report on the development of a virtual reality (VR) testbed using the Cave Automatic Virtual Environment (CAVE) technology for human-automation teaming and airspace operation research of UAM. Using a four-wall projection system with motion capture, the CAVE provides an immersive virtual environment with real-time full body tracking capability. We created a virtual environment consisting of San Francisco city and a vertical take-off-and-landing passenger aircraft that can fly between a downtown location and the San Francisco International Airport. The aircraft can be operated autonomously or manually by a single pilot who maneuvers the aircraft using a flight control joystick. The interior of the aircraft includes a virtual cockpit display with vehicle heading, location, and speed information. The system can record simulation events and flight data for post-processing. The system parameters are customizable for different flight scenarios; hence, the CAVE VR testbed provides a flexible method for development and evaluation of UAM framework.
Marayong, Panadda↗
NASA Experimental Program to Stimulate Competitive Research: South Carolina
The use of an appropriate relationship model is critical for reliable prediction of future urban growth. Identification of proper variables and mathematic functions and determination of the weights or coefficients are the key tasks for building such a model. Although the conventional logistic regression model is appropriate for handing land use problems, it appears insufficient to address the issue of interdependency of the predictor variables. This study used an alternative approach to simulation and modeling urban growth using artificial neural networks. It developed an operational neural network model trained using a robust backpropagation method. The model was applied in the Myrtle Beach region of South Carolina, and tested with both global datasets and areal datasets to examine the strength of both regional models and areal models. The results indicate that the neural network model not only has many theoretic advantages over other conventional mathematic models in representing the complex urban systems, but also is practically superior to the logistic model in its capability to predict urban growth with better - accuracy and less variation. The neural network model is particularly effective in terms of successfully identifying urban patterns in the rural areas where the logistic model often falls short. It was also found from the area-based tests that there are significant intra-regional differentiations in urban growth with different rules and rates. This suggests that the global modeling approach, or one model for the entire region, may not be adequate for simulation of a urban growth at the regional scale. Future research should develop methods for identification and subdivision of these areas and use a set of area-based models to address the issues of multi-centered, intra- regionally differentiated urban growth.
Sutton, Michael A.↗
Future fields of control application
Application of control theory to production engineering, transportation systems, urban development, biological oceanography, mass communications, and socio-economic problems
Source record↗
Predicting near-saturated hydraulic conductivity in urban soils
Pedotransfer functions (PTFs) provide point predictions of soil hydraulic properties from more readily measured soil characteristics, yet uncertainties and biases in measurement methods, sampling distributions, and boundary conditions can limit accuracy when estimating near-saturated hydraulic conductivity (K(n)). These limitations may be particularly problematic in understudied urban landscapes that often contain altered hydraulic properties. To better treat deficiencies in PTF performance, we addressed three objectives, which were to: 1) develop PTFs to predict urban K(n), 2) assess bulk density and coarse fragments as explanatory variables; and 3) evaluate the predictive capability of these PTFs by comparing their output to measured hydraulic conductivity values from three other studies of urban soil hydraulics. We used artificial neural networks (ANN) and random forest (RF) approaches to predict urban K(n), with the training dataset including 307 tension infiltrometer tests and other measurements drawn from urban soil assessments in 11 U.S. cities. The PTFs utilized a hierarchy of inputs, starting with percentage sand, silt, clay, and then adding percentage coarse fragments and bulk density. The ANN models performed similar to the RF models, and all models exhibited similar or better predictive performance as models results collected from published articles. The inclusion of bulk density or coarse fragments did not improve accuracy over soil texture alone. Possible reasons for this result include low correlation between K(n) and bulk density and the exclusion of large voids during flow measurements with tension infiltrometers. The models have been made available as an open-source software package to encourage adoption by users working in urban systems.
Jinshi Jian↗
Digital Flight: A New Cooperative Operating Mode to Complement VFR and IFR
Digital Flight is a proposed new operating mode for all airspace users, complementing and adding to the existing operating modes of visual and instrument flight rules (VFR and IFR) and providing for cooperative integration in controlled airspace. Under new regulations (Digital Flight Rules, DFR) that set requirements for its sustained use, qualified operators employ Digital Flight to enhance their airspace access and operational flexibility in all visibility conditions and eventually all airspace classes without requiring segregation from incumbent operations. Enabled by connected digital information and technologies, Digital Flight operators employ cooperative practices and self-separation to ensure flight path safety in lieu of visual procedures or receiving separation services from Air Traffic Control. Its distributed structure and automated functions enable traffic densities and operational tempos not achievable with the existing operating modes. Various ongoing and emerging industry initiatives for enabling new entrants (e.g., unmanned aircraft systems, urban air taxis) and enhanced use of underserved airspace (e.g., ultra-high altitude), are pursuing various alternative operating modes with significant similarities, creating a unique and time-limited opportunity for harmonization and convergence. Digital Flight is proposed to serve that harmonizing role, creating a common new paradigm for airspace operations. Such convergence would not only bring together these emerging market segments but will also bolster the existing operators with access to this new operating mode and its advantages. Digital Flight provides an opportunity to focus regulatory development and benefit the aviation industry. This paper describes Digital Flight in sufficient detail to initiate community engagement and deliberation on a common new operating mode. It describes the essential elements, principal capabilities, and operational integration of DFR in shared airspace. It describes the value proposition from multiple perspectives and presents initial thoughts on the path forward.
Digital flight↗
Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning
The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.
Ruixuan Han↗
Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning
The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.
Ruixuan Han↗
Urban Area Unmanned Aerial Systems Sensor Capabilities for Ensuring Ground Hazards Safety
Unmanned aerial systems (UASs) in urban areas can pose significant safety risks to dynamic ground objects (DGOs) such as people, pets, and bikes; especially for off-nominal emergency traverses and landings. This paper will examine a framework for evaluating the UAS safety benefits which can be achieved by classifying DGO hazards, modeling their behavior, and assigning collision costs. DGOs are assumed to be any ground objects which are either moving or capable of moving. Safety benefits will be assessed by analyzing metrics computed from UAS and DGO trajectories which take into account intent and uncertainties. This paper will establish the theoretical relationships mapping these trajectories and DGO classifications to safety levels. Sensor capabilities will be mapped to DGO trajectory uncertainties, so that safety can be directly estimated from the sensor specifications for a given UAS trajectory.
Bouyssounouse, Xavier↗
Separation Assurance in Urban Air Mobility Systems Using Shared Scheduling Protocols
Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significant risks. We propose a tactical separation approach leveraging shared scheduling protocols, originally designed for Ethernet networks and operating systems, to coordinate access to these intersections. Using a decentralized Markov decision process framework, the proposed approach enables aircraft to autonomously adjust their speed and timing as they navigate these critical areas, maintaining safe separation without a central controller. We evaluate the effectiveness of this approach in simulated UAM scenarios, demonstrating its ability to reduce separation violations to zero while acknowledging trade-offs in flight times as traffic density increases. Additionally, we explore the impact of non-compliant aircraft, showing that while shared scheduling protocols can no longer guarantee safe separation, they still provide significant improvements over systems without scheduling protocols.
Separation Assurance↗
Ground Risk Assessment Service Provider (GRASP) Development Effort as a Supplemental Data Service Provider (SDSP) for Urban Unmanned Aircraft System (UAS) Operations
NASA’s Unmanned Aircraft System (UAS) Traffic Management (UTM) project aims to enable the integration of new aviation paradigms such as Unmanned Aircraft Systems (UAS) while providing the necessary infrastructure for future concepts such as On-Demand Mobility (ODM) and Urban Air Mobility (UAM) operations in the National Airspace System (NAS). In order to do so, the UTM project has developed an architecture to allow communication among UAS operators, UAS Service Suppliers (USS), Air Navigation Service Providers (ANSP), and the public. As part of this framework, the Supplemental Data Service Providers (SDSP) are envisioned as model and/or data based services that disseminate essential or enhanced information to ensure safe operations within low-altitude airspace. These services include terrain and obstacle data, specialized weather data, surveillance, constraint information, risk monitoring, etc. This paper highlights the development efforts of a non-participant casualty risk assessment SDSP called Ground Risk Assessment Service Provider (GRASP) which assists operators with preflight planning. GRASP is based on the previously introduced UTM Risk Assessment Framework (URAF) and allows UAS operators to simulate and visualize potential non-participant casualty risks associated with their proposed flight. The risk assessment capability also allows operators to revise their flight plans if the casualty risks are determined to be above acceptable thresholds. GRASP is configured to account for future improvements including servicing airborne aircraft as part of NASA’s System-Wide Safety (SWS) project.
Ancel, Ersin↗
Separation Assurance in Urban Air Mobility Systems Using Shared Scheduling Protocols
Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significant risks. We propose a tactical separation approach leveraging shared scheduling protocols, originally designed for Ethernet networks and operating systems, to coordinate access to these intersections. Using a decentralized Markov decision process framework, the proposed approach enables aircraft to autonomously adjust their speed and timing as they navigate these critical areas, maintaining safe separation without a central controller. We evaluate the effectiveness of this approach in simulated UAM scenarios, demonstrating its ability to reduce separation violations to zero while acknowledging trade-offs in flight times as traffic density increases. Additionally, we explore the impact of non-compliant aircraft, showing that while shared scheduling protocols can no longer guarantee safe separation, they still provide significant improvements over systems without scheduling protocols.
system safety↗
Exploratory Analysis of the Airspace Throughput and Sensitivities of an Urban Air Mobility System
The use of small, vertical-takeoff and landing aircraft to provide efficient, high-speed, ondemand passenger transportation within a metropolitan area (e.g. intra-city transportation) is a topic of increasing interest and investment within the aerospace and transportation communities. Preliminary, mostly vehicle-level analysis suggests that passenger-carrying “Urban Air Mobility” has the potential to provide meaningful door-to-door trip time savings compared to identical trips taken solely by automobile, even for relatively short trips of a few tens of miles. Subsequent analysis has shown that if such trips can be conducted at costs competitive with ground transportation, the demand for such flight operations, not surprisingly, becomes unprecedented by historical airspace operations counts, raising fundamental questions regarding feasibility, practicality, capacity and basic system attributes such as separation criteria. In this paper, we conduct a preliminary assessment of vertipad requirements and en route separation minima relative to the feasibility of large-scale urban aviation operations. This analysis is acknowledged as being far from comprehensive and is intended to help define the initial boundaries of an airspace system compatible with enabling high-volume operations.
Goodrich, Kenneth H.↗
Urban studies - An opportunity for systems engineering
Systems engineering and urban transportation
Gibson, J. E.↗
Assessment of the impact of dipped guideways on urban rail transit systems: Ventilation and safety requirements
The ventilation and fire safety requirements for subway tunnels with dipped profiles between stations as compared to subway tunnels with level profiles were evaluated. This evaluation is based upon computer simulations of a train fire emergency condition. Each of the tunnel configurations evaluated was developed from characteristics that are representative of modern transit systems. The results of the study indicate that: (1) The level tunnel system required about 10% more station cooling than dipped tunnel systems in order to meet design requirements; and (2) The emergency ventilation requirements are greater with dipped tunnel systems than with level tunnel systems.
Source record↗
Embedding Climate Change in Urban Planning and Urban Design in New York City
Confronting the challenges of a rapidly urbanizing world threatened by climate change requires expanding the traditional influence and capabilities of urban planning and urban design, integrating climate science, natural systems and compact urban form to configure dynamic, desirable and healthy communities. Cost-effective planning and design measures that help mitigate emissions while bringing adaptive benefits should be prioritized. The chapter draws from the publication Climate Change and Cities (Cambridge University Press 2018) by the Urban Climate Change Research Network (UCCRN). The two-phase New York City case study by the Urban Design Climate Lab at the New York Institute of Technology and a team of international urban design climate experts illustrates how this emerging expertise can be replicated and implemented worldwide. Its focus on configuring people-centered public spaces that enhance energy efficiency and improve public health draws from four urban climate factors: improving efficiency of urban systems, both in energy and transportation; optimizing the form and layout of urban districts to enhance ventilation; promoting appropriate building materials with high reflectivity; and increasing green and blue urban infrastructure. The chapter highlights a set of tools and methods to measure success.
Resiliency↗