Urban air mobility (UAM) is envisioned to move to highly automated and high-density operations in low-altitude urban airspace in the future. Providers of services for UAM (PSU), rather than the legacy air traffic control, are anticipated to support operators with operational planning, aircraft deconfliction, conformance monitoring, and emergency information dissemination. Such services, for hundreds to thousands of simultaneous UAM operations in constrained airspace, can only be realized with automated systems. In this study, airspace and deconfliction models for generating predeparture conflict-free four-dimensional (4-D) flight trajectories are proposed, which can be further developed into an automated flight planning tool for PSU. A semistructured scalable airspace design for future UAM is proposed (i.e., a layered airspace topology with direct routes between vertiports, avoiding physical obstacles, such as buildings, obstructions, and restricted airspace) using the visibility graph method. Based on the proposed airspace design, deconfliction strategies (e.g., flight-level assignment and departure delay) are applied to obtain predeparture conflict-free 4-D trajectories of UAM operations by solving a mixed-integer programming problem, with the objective function to minimize the operating cost of UAM operations. Furthermore, sensitivity analysis is performed to investigate the impacts of three key cost parameters (electricity price, crew hourly rate, and maintenance hourly rate). The relationships of departure delay bound (maximum departure delay allowed) vs operating cost saving and departure delay bound vs delay cost to passengers are examined, as is the tradeoff between operating cost saving and passenger delay cost.
{"title":"Predeparture Flight Planning to Minimize Operating Cost for Urban Air Mobility","authors":"Hualong Tang, Yu Zhang, J. Post","doi":"10.2514/1.d0332","DOIUrl":"https://doi.org/10.2514/1.d0332","url":null,"abstract":"Urban air mobility (UAM) is envisioned to move to highly automated and high-density operations in low-altitude urban airspace in the future. Providers of services for UAM (PSU), rather than the legacy air traffic control, are anticipated to support operators with operational planning, aircraft deconfliction, conformance monitoring, and emergency information dissemination. Such services, for hundreds to thousands of simultaneous UAM operations in constrained airspace, can only be realized with automated systems. In this study, airspace and deconfliction models for generating predeparture conflict-free four-dimensional (4-D) flight trajectories are proposed, which can be further developed into an automated flight planning tool for PSU. A semistructured scalable airspace design for future UAM is proposed (i.e., a layered airspace topology with direct routes between vertiports, avoiding physical obstacles, such as buildings, obstructions, and restricted airspace) using the visibility graph method. Based on the proposed airspace design, deconfliction strategies (e.g., flight-level assignment and departure delay) are applied to obtain predeparture conflict-free 4-D trajectories of UAM operations by solving a mixed-integer programming problem, with the objective function to minimize the operating cost of UAM operations. Furthermore, sensitivity analysis is performed to investigate the impacts of three key cost parameters (electricity price, crew hourly rate, and maintenance hourly rate). The relationships of departure delay bound (maximum departure delay allowed) vs operating cost saving and departure delay bound vs delay cost to passengers are examined, as is the tradeoff between operating cost saving and passenger delay cost.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44971896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aircraft sequencing problem has been well discussed in many variants over the years; and during this time, the problem has changed from a standalone optimization problem to an embedded problem. At hub airports, aircraft sequencing influences passengers’ available connecting times. Short connections could benefit from a modification of the arrival or departure sequence. In our work, we have developed a bilevel optimization problem that combines the aircraft sequencing problem and the airport gate assignment problem at hub airports to make connections more efficient. The decision on the gate assignments is made at the upper level, whereas the efficient aircraft arrival sequencing is addressed at the lower level. Methods for solving this kind of large-scale bilevel optimization problem by considering a full day of operation and dynamically changing traffic situations have yet to be researched. In this paper, we focus on efficient aircraft sequencing by taking into consideration gate assignments, and we propose a multiobjective mixed integer linear program that is solved by an interactive method and considers the (contradicting) objectives of competing airlines and the air traffic control. The capabilities of our approach are demonstrated by modeling and solving a real-case scenario for Munich Airport.
{"title":"Efficient Aircraft Arrival Sequencing Given Airport Gate Assignment","authors":"Philipp Zeunert","doi":"10.2514/1.d0350","DOIUrl":"https://doi.org/10.2514/1.d0350","url":null,"abstract":"The aircraft sequencing problem has been well discussed in many variants over the years; and during this time, the problem has changed from a standalone optimization problem to an embedded problem. At hub airports, aircraft sequencing influences passengers’ available connecting times. Short connections could benefit from a modification of the arrival or departure sequence. In our work, we have developed a bilevel optimization problem that combines the aircraft sequencing problem and the airport gate assignment problem at hub airports to make connections more efficient. The decision on the gate assignments is made at the upper level, whereas the efficient aircraft arrival sequencing is addressed at the lower level. Methods for solving this kind of large-scale bilevel optimization problem by considering a full day of operation and dynamically changing traffic situations have yet to be researched. In this paper, we focus on efficient aircraft sequencing by taking into consideration gate assignments, and we propose a multiobjective mixed integer linear program that is solved by an interactive method and considers the (contradicting) objectives of competing airlines and the air traffic control. The capabilities of our approach are demonstrated by modeling and solving a real-case scenario for Munich Airport.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49478408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Single-pilot operations (SPOs) in commercial air transport present a range of benefits and challenges, but there is a need to define architectures and compare them in different operational contexts. Here, we identified various combinations of architectural decisions based on the literature, and we compared them to current operations (in different operating contexts) on a safety versus cost tradespace. Safety was defined as a function of the pilot nominal operations workload, handling of off-nominal situations, and pilot incapacitation; whereas the cost was defined as a combination of acquisition and operating costs. Our analysis suggests that different classes of aircraft (wide bodies, narrow bodies, and regional jets) have different levels of benefits and costs in moving to SPOs. The capabilities of automation need to improve drastically before the second human in the flight deck can be replaced, and this is borne out by the dominance of human-centered architectures in the tradespace. The analysis also reveals that regional aircraft may be prime candidates to move to SPOs first because most regional architectures generate positive savings.
{"title":"Single-Pilot Aircraft in Commercial Air Transport Operations: A Comparison of Potential Architectures","authors":"Aravind Asokan, B. Cameron","doi":"10.2514/1.d0340","DOIUrl":"https://doi.org/10.2514/1.d0340","url":null,"abstract":"Single-pilot operations (SPOs) in commercial air transport present a range of benefits and challenges, but there is a need to define architectures and compare them in different operational contexts. Here, we identified various combinations of architectural decisions based on the literature, and we compared them to current operations (in different operating contexts) on a safety versus cost tradespace. Safety was defined as a function of the pilot nominal operations workload, handling of off-nominal situations, and pilot incapacitation; whereas the cost was defined as a combination of acquisition and operating costs. Our analysis suggests that different classes of aircraft (wide bodies, narrow bodies, and regional jets) have different levels of benefits and costs in moving to SPOs. The capabilities of automation need to improve drastically before the second human in the flight deck can be replaced, and this is borne out by the dominance of human-centered architectures in the tradespace. The analysis also reveals that regional aircraft may be prime candidates to move to SPOs first because most regional architectures generate positive savings.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41636897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Behavioral Intention Factors for Prescription Deliveries by Small Unmanned Aircraft in Rural Communities","authors":"S. Talley, Robert E. Joslin","doi":"10.2514/1.d0356","DOIUrl":"https://doi.org/10.2514/1.d0356","url":null,"abstract":"","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48373603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Badrinath, James M. Abel, H. Balakrishnan, Emily Joback, T. Reynolds
The assessment of the fuel burn and emissions impact of airport surface operations is a key part of understanding the environmental impacts of aviation. These assessments are needed at two levels: the analysis of inventories (the total amount of fuel burned and emissions discharged over some period of time), and the analysis of spatial distributions (the amount of emissions experienced at a particular location within or near the airport). Although the availability of taxi times for the operations of interest is sufficient for inventory analysis, the analysis of spatial distributions requires estimates of where on the airport surface an aircraft is located as it consumes fuel. In this paper, we show how a data-driven queuing network model can be developed in order to estimate the time that an aircraft spends at different congested locations on the airport surface. These models are useful both in spatial distribution analysis and in accurately predicting taxi times in the absence of measurements (for example, for projected demand sets). We use measurements of ultrafine particles at Los Angeles International Airport to demonstrate that the proposed model can help predict the measured emissions at different monitoring sites located in the vicinity of the airport. In the process, we show how one could develop a machine learning model of the spatial distribution of airport surface emissions given the pollutant measurements, air traffic demand, and prevailing weather conditions. Finally, we develop a clustering-based method to evaluate the generalizability of our surface operations modeling framework.
{"title":"Spatial Modeling of Airport Surface Fuel Burn for Environmental Impact Analyses","authors":"S. Badrinath, James M. Abel, H. Balakrishnan, Emily Joback, T. Reynolds","doi":"10.2514/1.d0294","DOIUrl":"https://doi.org/10.2514/1.d0294","url":null,"abstract":"The assessment of the fuel burn and emissions impact of airport surface operations is a key part of understanding the environmental impacts of aviation. These assessments are needed at two levels: the analysis of inventories (the total amount of fuel burned and emissions discharged over some period of time), and the analysis of spatial distributions (the amount of emissions experienced at a particular location within or near the airport). Although the availability of taxi times for the operations of interest is sufficient for inventory analysis, the analysis of spatial distributions requires estimates of where on the airport surface an aircraft is located as it consumes fuel. In this paper, we show how a data-driven queuing network model can be developed in order to estimate the time that an aircraft spends at different congested locations on the airport surface. These models are useful both in spatial distribution analysis and in accurately predicting taxi times in the absence of measurements (for example, for projected demand sets). We use measurements of ultrafine particles at Los Angeles International Airport to demonstrate that the proposed model can help predict the measured emissions at different monitoring sites located in the vicinity of the airport. In the process, we show how one could develop a machine learning model of the spatial distribution of airport surface emissions given the pollutant measurements, air traffic demand, and prevailing weather conditions. Finally, we develop a clustering-based method to evaluate the generalizability of our surface operations modeling framework.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47694333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohd Hasrizam Che Man, Anush Kumar Sivakumar, Haoliang Hu, Kin Huat Low
Small unmanned aircraft systems or drones are expected to be used for different applications, such as parcel delivery, inspection, and aerial photography, in urban areas. However, drones usually use an electric system to power up the propulsion, communications, navigation, and flight control system, which means that it is not as reliable as the manned aircraft system and may result in failure during operation and then crash to the ground. At present, there is almost no extensive publication about the high-fidelity modeling used by drones to calculate the crash trajectory and point of crash. The experimental data for modeling and simulation verification of multirotor aircraft are limited. So far, crash trajectory prediction has been limited to point mass or ballistic methods, and these methods are usually only suitable for complete power failure and without any control system. This study intends to investigate the effects of different multirotor drones’ failure modes on its crash trajectory and crash area compared to the ballistic model by using ADAMS and MATLAB cosimulation methods. Conclusions from the study show the crash trajectory, flight distance, and impact speed of the drones under four failure modes, which are quite different from the ballistic trajectory. The findings can potentially contribute to better risk assessment of the multirotor drones for the urban environment operation.
{"title":"Ground Crash Area Estimation of Quadrotor Aircraft Under Propulsion Failure","authors":"Mohd Hasrizam Che Man, Anush Kumar Sivakumar, Haoliang Hu, Kin Huat Low","doi":"10.2514/1.d0320","DOIUrl":"https://doi.org/10.2514/1.d0320","url":null,"abstract":"Small unmanned aircraft systems or drones are expected to be used for different applications, such as parcel delivery, inspection, and aerial photography, in urban areas. However, drones usually use an electric system to power up the propulsion, communications, navigation, and flight control system, which means that it is not as reliable as the manned aircraft system and may result in failure during operation and then crash to the ground. At present, there is almost no extensive publication about the high-fidelity modeling used by drones to calculate the crash trajectory and point of crash. The experimental data for modeling and simulation verification of multirotor aircraft are limited. So far, crash trajectory prediction has been limited to point mass or ballistic methods, and these methods are usually only suitable for complete power failure and without any control system. This study intends to investigate the effects of different multirotor drones’ failure modes on its crash trajectory and crash area compared to the ballistic model by using ADAMS and MATLAB cosimulation methods. Conclusions from the study show the crash trajectory, flight distance, and impact speed of the drones under four failure modes, which are quite different from the ballistic trajectory. The findings can potentially contribute to better risk assessment of the multirotor drones for the urban environment operation.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48388601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tristan A. Hearn, Mark T. Kotwicz Herniczek, Brian J. German
In this work, a framework for optimizing the configuration of service areas in airspace into disparate partitions is demonstrated in the context of urban air mobility (UAM) operations. This framework is applied to a conceptual UAM airspace configuration, where a free-flight-based routing service and a corridor-based routing service are dynamically allocated to control different portions of the airspace over time, based on traffic demand. This allocation seeks to determine the least amount of structured coordination (in terms of active flight corridors) needed to safely meet traffic demand. This framework integrates several modeling components, including a novel spatiotemporal graph theoretic UAM traffic model capable of optimizing vehicle trajectories while maintaining multiple flight constraints. Airspace complexity and trajectory efficiency metrics are both implemented to quantify the overall safety and cumulative cost of routing a set of missions according to a given airspace configuration. Finally, spatial airspace partitions are managed using a support vector machine-based algorithm. Metrics are then applied to optimize the airspace configurations, according to desired objectives. Simulated results show that this framework can produce airspace configurations that ensure safety, while providing trajectory efficiency more effectively than purely uniform free-flight or corridor-based flight. This is demonstrated for both low- and high-density traffic scenarios.
{"title":"Conceptual Framework for Dynamic Optimal Airspace Configuration for Urban Air Mobility","authors":"Tristan A. Hearn, Mark T. Kotwicz Herniczek, Brian J. German","doi":"10.2514/1.d0327","DOIUrl":"https://doi.org/10.2514/1.d0327","url":null,"abstract":"In this work, a framework for optimizing the configuration of service areas in airspace into disparate partitions is demonstrated in the context of urban air mobility (UAM) operations. This framework is applied to a conceptual UAM airspace configuration, where a free-flight-based routing service and a corridor-based routing service are dynamically allocated to control different portions of the airspace over time, based on traffic demand. This allocation seeks to determine the least amount of structured coordination (in terms of active flight corridors) needed to safely meet traffic demand. This framework integrates several modeling components, including a novel spatiotemporal graph theoretic UAM traffic model capable of optimizing vehicle trajectories while maintaining multiple flight constraints. Airspace complexity and trajectory efficiency metrics are both implemented to quantify the overall safety and cumulative cost of routing a set of missions according to a given airspace configuration. Finally, spatial airspace partitions are managed using a support vector machine-based algorithm. Metrics are then applied to optimize the airspace configurations, according to desired objectives. Simulated results show that this framework can produce airspace configurations that ensure safety, while providing trajectory efficiency more effectively than purely uniform free-flight or corridor-based flight. This is demonstrated for both low- and high-density traffic scenarios.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44625597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The presence of uncertainty in weather forecasts poses significant challenges for air traffic managers. These challenges can have major repercussions on stakeholders in terms of their impact on the delay within the system. In this paper, we discuss an approach for recommending traffic management initiative (TMI) parameters during uncertain weather conditions. We propose four methods for TMI selection. The first two favor random exploration of TMI decisions. An epsilon-greedy approach and a softmax algorithm are also evaluated against the two random exploration approaches. A parallel fast-time simulation framework is presented for evaluating the proposed methods over a range of weather forecast scenarios. A set of regional TMIs is applied and tested against a set of case days in which the airspace capacity in the Northeast United States was compromised by convective weather. Both the softmax and epsilon-greedy approaches demonstrate strong performance relative to the other methods. The results suggest that the approach could potentially aid air traffic stakeholders in understanding how to best deal with weather forecast uncertainty.
{"title":"Recommending Strategic Air Traffic Management Initiatives in Convective Weather","authors":"James C. Jones, Zachary Ellenbogen, Y. Glina","doi":"10.2514/1.d0297","DOIUrl":"https://doi.org/10.2514/1.d0297","url":null,"abstract":"The presence of uncertainty in weather forecasts poses significant challenges for air traffic managers. These challenges can have major repercussions on stakeholders in terms of their impact on the delay within the system. In this paper, we discuss an approach for recommending traffic management initiative (TMI) parameters during uncertain weather conditions. We propose four methods for TMI selection. The first two favor random exploration of TMI decisions. An epsilon-greedy approach and a softmax algorithm are also evaluated against the two random exploration approaches. A parallel fast-time simulation framework is presented for evaluating the proposed methods over a range of weather forecast scenarios. A set of regional TMIs is applied and tested against a set of case days in which the airspace capacity in the Northeast United States was compromised by convective weather. Both the softmax and epsilon-greedy approaches demonstrate strong performance relative to the other methods. The results suggest that the approach could potentially aid air traffic stakeholders in understanding how to best deal with weather forecast uncertainty.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43504496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juan A. Vila Carbó, J. V. Balbastre Tejedor, Pablo Morcillo Pallarés, Pedro Yuste Pérez
Risk assessment is a key issue in enabling the use of unmanned aircraft systems (UASs) in nonsegregated areas, especially in very low-level airspace and urban areas. The specific operations risk assessment (SORA) methodology represents an important milestone in performing the risk assessments required by aviation safety agencies to UAS operators in specific operations. However, the SORA is a qualitative method used for UASs operating inside a well-bounded operational volume. This paper proposes a quantitative method that can not only be used in a closed volume but also in an airspace shared by several UAS missions and even general aviation. The basis for this is providing a separation volume (a “bubble”) to prevent collisions that is calculated using a risk-based approach. The method consists of setting a target level of safety, which is accomplished using a tradeoff between strategic and tactical mitigations. A probabilistic methodology for performing quantitative risk assessment of strategic and tactical mitigations is provided, and the dependence of the separation distance is carefully analyzed. All factors affecting the separation distance are identified, and their contributions to collision risk are probabilistically estimated. The method takes into account specific factors relating to UASs such as trajectories, separation methods, and performance. As a result, the method allows numerical determination of a separation distance for a given target level of safety and an operational scenario.
{"title":"Risk-Based Method for Determining Separation Minima in Unmanned Aircraft Systems","authors":"Juan A. Vila Carbó, J. V. Balbastre Tejedor, Pablo Morcillo Pallarés, Pedro Yuste Pérez","doi":"10.2514/1.d0326","DOIUrl":"https://doi.org/10.2514/1.d0326","url":null,"abstract":"Risk assessment is a key issue in enabling the use of unmanned aircraft systems (UASs) in nonsegregated areas, especially in very low-level airspace and urban areas. The specific operations risk assessment (SORA) methodology represents an important milestone in performing the risk assessments required by aviation safety agencies to UAS operators in specific operations. However, the SORA is a qualitative method used for UASs operating inside a well-bounded operational volume. This paper proposes a quantitative method that can not only be used in a closed volume but also in an airspace shared by several UAS missions and even general aviation. The basis for this is providing a separation volume (a “bubble”) to prevent collisions that is calculated using a risk-based approach. The method consists of setting a target level of safety, which is accomplished using a tradeoff between strategic and tactical mitigations. A probabilistic methodology for performing quantitative risk assessment of strategic and tactical mitigations is provided, and the dependence of the separation distance is carefully analyzed. All factors affecting the separation distance are identified, and their contributions to collision risk are probabilistically estimated. The method takes into account specific factors relating to UASs such as trajectories, separation methods, and performance. As a result, the method allows numerical determination of a separation distance for a given target level of safety and an operational scenario.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45301843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher R. Chin, Karthik Gopalakrishnan, H. Balakrishnan, M. Egorov
Advanced air mobility operations (e.g., air taxis and drone deliveries) are expected to significantly increase the demand for limited airspace resources. Two key characteristics of these operations are that flights will be scheduled with short lead times, and operators may be unable or reluctant, for reasons of privacy, to share flight intent information. Consequently, there is a need for congestion management algorithms that are efficient and fair in dynamic, reduced-information settings. In this paper, we address these challenges by designing a protocol that determines the “rules-of-the-road” for airspace access under these settings. The proposed protocol centers on the construction of priority queues to determine access to each congested volume of airspace. We leverage the concepts of backpressure and cycle detection to avoid gridlock and promote efficiency, and present several flightand operator-level prioritization schemes. We evaluate the impacts of the prioritization schemes on systemwide and operator-level efficiency and fairness through extensive simulations of three scenarios: random flight patterns, crossflows, and hub-based operations. In all scenarios, we find that backpressure prioritization yields the most efficient solution, and that accrued delay or dominant resource prioritization is the most fair depending on the user’s choice of fairness metric. Keywords—Advanced air mobility, UAS traffic management, congestion control protocols, efficiency, fairness
{"title":"Protocol-Based Congestion Management for Advanced Air Mobility","authors":"Christopher R. Chin, Karthik Gopalakrishnan, H. Balakrishnan, M. Egorov","doi":"10.2514/1.d0298","DOIUrl":"https://doi.org/10.2514/1.d0298","url":null,"abstract":"Advanced air mobility operations (e.g., air taxis and drone deliveries) are expected to significantly increase the demand for limited airspace resources. Two key characteristics of these operations are that flights will be scheduled with short lead times, and operators may be unable or reluctant, for reasons of privacy, to share flight intent information. Consequently, there is a need for congestion management algorithms that are efficient and fair in dynamic, reduced-information settings. In this paper, we address these challenges by designing a protocol that determines the “rules-of-the-road” for airspace access under these settings. The proposed protocol centers on the construction of priority queues to determine access to each congested volume of airspace. We leverage the concepts of backpressure and cycle detection to avoid gridlock and promote efficiency, and present several flightand operator-level prioritization schemes. We evaluate the impacts of the prioritization schemes on systemwide and operator-level efficiency and fairness through extensive simulations of three scenarios: random flight patterns, crossflows, and hub-based operations. In all scenarios, we find that backpressure prioritization yields the most efficient solution, and that accrued delay or dominant resource prioritization is the most fair depending on the user’s choice of fairness metric. Keywords—Advanced air mobility, UAS traffic management, congestion control protocols, efficiency, fairness","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48611000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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