Mohammed Hassan, Holger Pfaender, Thayná Oliveira, Nadir Ougazzaden, Thomas Dussauge, Dimitri N. Mavris
There has been renewed interest in commercial supersonic air travel in recent years with new concepts currently being advertised for a potential comeback within the next decade. Current regulations do not permit overland operations for supersonic vehicles due to the sonic booms produced during flight. To capture a favorable market share, these vehicles would have to serve more routes than previously served while abiding with current regulations. This study presents a novel and comprehensive approach to overland-prohibited flight routing and route-specific demand forecasting for commercial supersonic air travel, in order to assess the expected market capture under nominal air traffic growth conditions. Results for a baseline reference scenario suggest that with efficient flight routing these vehicles could operate on many routes and capture 5 to 8% of the current demand for subsonic air travel. However, a sensitivity analysis of the top 10 routes showed that both flight routing and demand forecasting are notably sensible to the underlying assumptions and that results could vary considerably based on modeling inputs pertaining to both the aircraft and airline characteristics.
{"title":"Commercial Supersonic Air Travel: Flight Routing and Demand Forecasting","authors":"Mohammed Hassan, Holger Pfaender, Thayná Oliveira, Nadir Ougazzaden, Thomas Dussauge, Dimitri N. Mavris","doi":"10.2514/1.d0408","DOIUrl":"https://doi.org/10.2514/1.d0408","url":null,"abstract":"There has been renewed interest in commercial supersonic air travel in recent years with new concepts currently being advertised for a potential comeback within the next decade. Current regulations do not permit overland operations for supersonic vehicles due to the sonic booms produced during flight. To capture a favorable market share, these vehicles would have to serve more routes than previously served while abiding with current regulations. This study presents a novel and comprehensive approach to overland-prohibited flight routing and route-specific demand forecasting for commercial supersonic air travel, in order to assess the expected market capture under nominal air traffic growth conditions. Results for a baseline reference scenario suggest that with efficient flight routing these vehicles could operate on many routes and capture 5 to 8% of the current demand for subsonic air travel. However, a sensitivity analysis of the top 10 routes showed that both flight routing and demand forecasting are notably sensible to the underlying assumptions and that results could vary considerably based on modeling inputs pertaining to both the aircraft and airline characteristics.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"53 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140965874","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}
Christine Taylor, Erik Vargo, Tyler Manderfield, Simon Heitin
Air traffic flow managers are continually faced with the decision of when and how to respond to predictions of future constraints. The promise of artificial intelligence, and specifically reinforcement learning, to provide decision support in this domain stems from the ability to systematically evaluate a sequence of potential actions, or strategy, across a range of uncertain futures. As decision support for human traffic managers, the generated recommendations must embody characteristics of a good management strategy; doing so requires introducing such notions to the algorithm. This paper proposes inducing stability into the strategy by dynamically constraining the design space based on upstream design decisions to promote consistency in the recommendations over time, where two such constraint sets are considered. The paper further evaluates the impact of adding a performance improvement threshold that must be overcome to accept a new strategy recommendation. The combination of search constraints and threshold values is evaluated against the agent’s reward function in addition to measures proposed to capture the stability of the strategy. The results show that the more restrictive set of constraints yields the best performance in terms of strategy stability and is more likely to reduce the delay where implementation of the threshold has a minor impact on overall performance. However, for the highest impact day of 8 June 2018, applying the threshold reverses the performance gains in delay but dramatically improves the stability of the resulting traffic flow management strategy from a flight level perspective, implying a potential tradeoff between delay optimization and flight predictability.
{"title":"Teaching Artificial Intelligence Good Air Traffic Flow Management","authors":"Christine Taylor, Erik Vargo, Tyler Manderfield, Simon Heitin","doi":"10.2514/1.d0414","DOIUrl":"https://doi.org/10.2514/1.d0414","url":null,"abstract":"Air traffic flow managers are continually faced with the decision of when and how to respond to predictions of future constraints. The promise of artificial intelligence, and specifically reinforcement learning, to provide decision support in this domain stems from the ability to systematically evaluate a sequence of potential actions, or strategy, across a range of uncertain futures. As decision support for human traffic managers, the generated recommendations must embody characteristics of a good management strategy; doing so requires introducing such notions to the algorithm. This paper proposes inducing stability into the strategy by dynamically constraining the design space based on upstream design decisions to promote consistency in the recommendations over time, where two such constraint sets are considered. The paper further evaluates the impact of adding a performance improvement threshold that must be overcome to accept a new strategy recommendation. The combination of search constraints and threshold values is evaluated against the agent’s reward function in addition to measures proposed to capture the stability of the strategy. The results show that the more restrictive set of constraints yields the best performance in terms of strategy stability and is more likely to reduce the delay where implementation of the threshold has a minor impact on overall performance. However, for the highest impact day of 8 June 2018, applying the threshold reverses the performance gains in delay but dramatically improves the stability of the resulting traffic flow management strategy from a flight level perspective, implying a potential tradeoff between delay optimization and flight predictability.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"119 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985583","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 potential of system-wide implementation of delayed deceleration approaches is presented through modeling a day of operations. Conventional approach procedures may involve early deceleration in the flight trajectory, resulting in early high-lift device deployment. Alternatively, delayed deceleration approaches involve delaying high-lift device deployment, thus maintaining higher airspeeds for longer and lowering thrust requirements, which reduces fuel burn and community noise. When implementing delayed deceleration approach procedures within traffic, the primary challenge is accrued from the variability of deceleration rates, which can significantly impact approach timing and thus separation among aircraft. In this work, a framework was developed that constructs delayed deceleration approach models based on observed radar data. This framework was then implemented on an average day at Boston Logan Airport to assess the feasibility of implementing delayed deceleration approaches on commercial traffic. The results indicate that delayed deceleration approaches can be implemented for 92.2% of the evaluated traffic without separation concerns, providing an average approach fuel burn savings of 10.27% and reducing exposure of configuration noise along the flight path by an average of 8.10 N miles of ground track distance. Therefore, implementing the delayed deceleration approach on a commercial traffic may provide environmental benefits.
通过模拟一天的运行情况，介绍了全系统实施延迟减速进近的可能性。传统的进场程序可能需要在飞行轨迹中提前减速，从而导致提前部署高升力装置。或者，延迟减速方法涉及延迟高升力装置的部署，从而在更长时间内保持较高的空速并降低推力要求，从而减少燃料消耗和社区噪音。在交通流中实施延迟减速进近程序时，主要的挑战来自于减速率的可变性，这会严重影响进近时机，从而影响飞机之间的间隔。在这项工作中，开发了一个框架，可根据观测到的雷达数据构建延迟减速进场模型。该框架随后在波士顿洛根机场的一个平均日实施，以评估对商业交通实施延迟减速进近的可行性。结果表明，92.2% 的受评估交通可以采用延迟减速进近，而无需考虑分离问题，平均进近燃油消耗可节省 10.27%，沿飞行路径的配置噪声暴露平均减少 8.10 N 英里的地面航迹距离。因此，在商业航班上实施延迟减速进近可能会带来环境效益。
{"title":"Environmental Analysis of System-Wide Implementation of Delayed Deceleration Approach Procedures","authors":"Victoria R. Pellerito, Jacqueline L. Huynh","doi":"10.2514/1.d0345","DOIUrl":"https://doi.org/10.2514/1.d0345","url":null,"abstract":"The potential of system-wide implementation of delayed deceleration approaches is presented through modeling a day of operations. Conventional approach procedures may involve early deceleration in the flight trajectory, resulting in early high-lift device deployment. Alternatively, delayed deceleration approaches involve delaying high-lift device deployment, thus maintaining higher airspeeds for longer and lowering thrust requirements, which reduces fuel burn and community noise. When implementing delayed deceleration approach procedures within traffic, the primary challenge is accrued from the variability of deceleration rates, which can significantly impact approach timing and thus separation among aircraft. In this work, a framework was developed that constructs delayed deceleration approach models based on observed radar data. This framework was then implemented on an average day at Boston Logan Airport to assess the feasibility of implementing delayed deceleration approaches on commercial traffic. The results indicate that delayed deceleration approaches can be implemented for 92.2% of the evaluated traffic without separation concerns, providing an average approach fuel burn savings of 10.27% and reducing exposure of configuration noise along the flight path by an average of 8.10 N miles of ground track distance. Therefore, implementing the delayed deceleration approach on a commercial traffic may provide environmental benefits.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"16 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140653410","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}
Toward implementation of fuel-saving descent operations, this study demonstrated a fixed flight-path angle descent using regularly scheduled commercial flights arriving at Kansai International Airport. This paper summarizes the authors' industry/academia collaboration research including 1) operational bottlenecks and weather analysis, 2) proposals of pilot and air traffic controllers’ operational procedures, 3) validation experiments using an A320 flight simulator, and 4) flight demonstrations conducted across 24 days using A320neo and A320ceo aircraft. The flight demonstration results were evaluated comparing flight recorder data of the new descent procedure relative to current descent operations. A total of 92% of the flights were approved to fly new approach procedure under actual air traffic operations, with an approximate average of 150 lb fuel reduction per flight (230 lb maximum).
{"title":"Demonstration of Fixed Flight-Path Angle Descent via Scheduled Commercial Flights","authors":"Daiki Iwata, Yuki Nonaka, Yasunobu Funai, Takehiko Shindo, Shinji Tanaka, Makoto Sato, Eri Itoh","doi":"10.2514/1.d0400","DOIUrl":"https://doi.org/10.2514/1.d0400","url":null,"abstract":"Toward implementation of fuel-saving descent operations, this study demonstrated a fixed flight-path angle descent using regularly scheduled commercial flights arriving at Kansai International Airport. This paper summarizes the authors' industry/academia collaboration research including 1) operational bottlenecks and weather analysis, 2) proposals of pilot and air traffic controllers’ operational procedures, 3) validation experiments using an A320 flight simulator, and 4) flight demonstrations conducted across 24 days using A320neo and A320ceo aircraft. The flight demonstration results were evaluated comparing flight recorder data of the new descent procedure relative to current descent operations. A total of 92% of the flights were approved to fly new approach procedure under actual air traffic operations, with an approximate average of 150 lb fuel reduction per flight (230 lb maximum).","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":" 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140210541","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}
Bernd Lorenz, Catherine Chalon-Morgan, I. De Visscher, Thomas Feuerle
A real-time human-in-the-loop (HITL) simulation is a crucial validation activity in the life cycle of air traffic management operational concept development. When planning HITL simulations, however, researchers face a series of experimental design constraints that often limit the application of advanced statistical data analyses. Linear mixed-effects modeling (LMEM) is a multiple regression analysis technique that is comparatively flexible in considering the covariance present in repeated measures data. This paper aims to make LMEM better known to applied researchers in the field of aviation research, particularly those applying HITL. For this purpose, the building steps of LMEM, the output, and model-fit tests are explained based on data obtained from an Airbus 320 flight simulation study that examined the impact of two different wake separation schemes on either a final approach or a departure path on the pilots’ perceived severity of a wake-vortex impact. The experimental setup involved six experimental factors that were not fully crossed, had an unbalanced number of repeats per pilot, and contained missing data. This prevented the use of the more traditional repeated measures analysis of variance. The LMEM was able to handle this and could explicitly test the study hypotheses with statistical confidence.
{"title":"Application of Linear Mixed-Effect Modeling for the Analysis of Human-in-the-Loop Simulation Experiments","authors":"Bernd Lorenz, Catherine Chalon-Morgan, I. De Visscher, Thomas Feuerle","doi":"10.2514/1.d0394","DOIUrl":"https://doi.org/10.2514/1.d0394","url":null,"abstract":"A real-time human-in-the-loop (HITL) simulation is a crucial validation activity in the life cycle of air traffic management operational concept development. When planning HITL simulations, however, researchers face a series of experimental design constraints that often limit the application of advanced statistical data analyses. Linear mixed-effects modeling (LMEM) is a multiple regression analysis technique that is comparatively flexible in considering the covariance present in repeated measures data. This paper aims to make LMEM better known to applied researchers in the field of aviation research, particularly those applying HITL. For this purpose, the building steps of LMEM, the output, and model-fit tests are explained based on data obtained from an Airbus 320 flight simulation study that examined the impact of two different wake separation schemes on either a final approach or a departure path on the pilots’ perceived severity of a wake-vortex impact. The experimental setup involved six experimental factors that were not fully crossed, had an unbalanced number of repeats per pilot, and contained missing data. This prevented the use of the more traditional repeated measures analysis of variance. The LMEM was able to handle this and could explicitly test the study hypotheses with statistical confidence.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"75 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841600","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}
Bernd Lorenz, Catherine Chalon-Morgan, I. De Visscher, Thomas Feuerle
A real-time human-in-the-loop (HITL) simulation is a crucial validation activity in the life cycle of air traffic management operational concept development. When planning HITL simulations, however, researchers face a series of experimental design constraints that often limit the application of advanced statistical data analyses. Linear mixed-effects modeling (LMEM) is a multiple regression analysis technique that is comparatively flexible in considering the covariance present in repeated measures data. This paper aims to make LMEM better known to applied researchers in the field of aviation research, particularly those applying HITL. For this purpose, the building steps of LMEM, the output, and model-fit tests are explained based on data obtained from an Airbus 320 flight simulation study that examined the impact of two different wake separation schemes on either a final approach or a departure path on the pilots’ perceived severity of a wake-vortex impact. The experimental setup involved six experimental factors that were not fully crossed, had an unbalanced number of repeats per pilot, and contained missing data. This prevented the use of the more traditional repeated measures analysis of variance. The LMEM was able to handle this and could explicitly test the study hypotheses with statistical confidence.
{"title":"Application of Linear Mixed-Effect Modeling for the Analysis of Human-in-the-Loop Simulation Experiments","authors":"Bernd Lorenz, Catherine Chalon-Morgan, I. De Visscher, Thomas Feuerle","doi":"10.2514/1.d0394","DOIUrl":"https://doi.org/10.2514/1.d0394","url":null,"abstract":"A real-time human-in-the-loop (HITL) simulation is a crucial validation activity in the life cycle of air traffic management operational concept development. When planning HITL simulations, however, researchers face a series of experimental design constraints that often limit the application of advanced statistical data analyses. Linear mixed-effects modeling (LMEM) is a multiple regression analysis technique that is comparatively flexible in considering the covariance present in repeated measures data. This paper aims to make LMEM better known to applied researchers in the field of aviation research, particularly those applying HITL. For this purpose, the building steps of LMEM, the output, and model-fit tests are explained based on data obtained from an Airbus 320 flight simulation study that examined the impact of two different wake separation schemes on either a final approach or a departure path on the pilots’ perceived severity of a wake-vortex impact. The experimental setup involved six experimental factors that were not fully crossed, had an unbalanced number of repeats per pilot, and contained missing data. This prevented the use of the more traditional repeated measures analysis of variance. The LMEM was able to handle this and could explicitly test the study hypotheses with statistical confidence.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"16 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781756","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}
Ramon Dalmau, Paolino De Falco, Miroslav Spak, José Daniel Rodriguez Varela
Airports plan their resources well in advance based on anticipated traffic. Currently, the only traffic information accessible in the pretactical phase is the flight schedules and historical data. In practice, however, flights do not always depart or arrive on time for a variety of reasons, such as air traffic flow management or reactionary delays. Because neither air traffic flow management regulations nor aircraft rotations are known during the pretactical phase, predicting the precise arrival and departure delays of individual flights is challenging given current technologies. As a result, probabilistic flight delay predictions are more plausible. This paper presents a machine learning model trained on historical data that learned the various quantiles of the departure and arrival delay distributions of individual flights. The model makes use of input features available during the pretactical phase, such as the airline, aircraft type, or expected number of passengers, to provide predictions of the delay distribution several days before operations. The performance of the model trained on operational data from Geneva airport is compared to a statistical baseline, providing evidence that machine learning is superior. Furthermore, the contribution of the various input features is quantified using the Shapely method, stressing the importance of the expected number of passengers. Finally, some practical examples are presented to illustrate how such a model could be applied in the pretactical phase.
{"title":"Probabilistic Pretactical Arrival and Departure Flight Delay Prediction with Quantile Regression","authors":"Ramon Dalmau, Paolino De Falco, Miroslav Spak, José Daniel Rodriguez Varela","doi":"10.2514/1.d0406","DOIUrl":"https://doi.org/10.2514/1.d0406","url":null,"abstract":"Airports plan their resources well in advance based on anticipated traffic. Currently, the only traffic information accessible in the pretactical phase is the flight schedules and historical data. In practice, however, flights do not always depart or arrive on time for a variety of reasons, such as air traffic flow management or reactionary delays. Because neither air traffic flow management regulations nor aircraft rotations are known during the pretactical phase, predicting the precise arrival and departure delays of individual flights is challenging given current technologies. As a result, probabilistic flight delay predictions are more plausible. This paper presents a machine learning model trained on historical data that learned the various quantiles of the departure and arrival delay distributions of individual flights. The model makes use of input features available during the pretactical phase, such as the airline, aircraft type, or expected number of passengers, to provide predictions of the delay distribution several days before operations. The performance of the model trained on operational data from Geneva airport is compared to a statistical baseline, providing evidence that machine learning is superior. Furthermore, the contribution of the various input features is quantified using the Shapely method, stressing the importance of the expected number of passengers. Finally, some practical examples are presented to illustrate how such a model could be applied in the pretactical phase.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"53 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139601576","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 uncertainties related to target off-block time (TOBT), the pushback-ready time predicted by aircraft operators, affect greatly airport operations. The accuracy of TOBT is, in general, difficult to be improved, because there are many uncertain factors in the departure process, e.g., delays in the passengers’ boarding. A better understanding of TOBT uncertainties, however, may help to improve airport surface operations. Currently, TOBT is estimated as a single point in time and updated as necessary by aircraft operators. Instead, the author proposes that TOBT is estimated as a distribution with a Johnson-SU distribution. The distribution parameters are estimated with time by neural networks using the history of TOBT updates. The main benefit of the proposed method is found in assigning the better pushback approval time of each departure aircraft for more efficient surface operations, which is demonstrated clearly by the simulation results. Using the proposed method, the aircraft operators can save fuel from improved ground operations via a probabilistic approach at the cost of reporting TOBT as a single point.
{"title":"Prediction of Off-Block Time Distribution for Departure Metering","authors":"Ryota Mori","doi":"10.2514/1.d0359","DOIUrl":"https://doi.org/10.2514/1.d0359","url":null,"abstract":"The uncertainties related to target off-block time (TOBT), the pushback-ready time predicted by aircraft operators, affect greatly airport operations. The accuracy of TOBT is, in general, difficult to be improved, because there are many uncertain factors in the departure process, e.g., delays in the passengers’ boarding. A better understanding of TOBT uncertainties, however, may help to improve airport surface operations. Currently, TOBT is estimated as a single point in time and updated as necessary by aircraft operators. Instead, the author proposes that TOBT is estimated as a distribution with a Johnson-SU distribution. The distribution parameters are estimated with time by neural networks using the history of TOBT updates. The main benefit of the proposed method is found in assigning the better pushback approval time of each departure aircraft for more efficient surface operations, which is demonstrated clearly by the simulation results. Using the proposed method, the aircraft operators can save fuel from improved ground operations via a probabilistic approach at the cost of reporting TOBT as a single point.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"121 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139616069","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":"A Successful Year for the Journal of Air Transportation","authors":"","doi":"10.2514/1.d0430","DOIUrl":"https://doi.org/10.2514/1.d0430","url":null,"abstract":"","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"67 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139637113","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}
In this paper, we present a comprehensive and reproducible urban air mobility (UAM) demand model centered around publicly available data and open source tools capable of demand estimation at the national level. A discrete mode-choice demand model is developed using longitudinal origin–destination employment statistics flow data, American community survey economic data, and the Open Source Routing Machine (OSRM) to identify the utility of a UAM commuter service relative to other modes of transportation. Using the implemented model, we identify New York City, San Francisco, and Los Angeles as cities with the highest potential commuter demand, and Seattle as the city most resilient to increases in delay time. A sensitivity study of demand is performed and shows that strong demand exists for short trips with low total delay times and for longer trips with a low ticket price per kilometer, with the former showing resilience to increases in operational costs and the latter showing resilience to increases in delays. The demand model is supported by a speed-flow model, which fuses highway performance monitoring system data with OpenStreetMap data to provide traffic-adjusted road segment speeds to OSRM. The speed-flow model has the capability of providing congestion data for road segments across the United States without the use of commercial data sets or routing services and is shown to improve routing duration accuracy in congested regions.
{"title":"Nationwide Demand Modeling for an Urban Air Mobility Commuting Mission","authors":"Mark T. Kotwicz Herniczek, Brian J. German","doi":"10.2514/1.d0371","DOIUrl":"https://doi.org/10.2514/1.d0371","url":null,"abstract":"In this paper, we present a comprehensive and reproducible urban air mobility (UAM) demand model centered around publicly available data and open source tools capable of demand estimation at the national level. A discrete mode-choice demand model is developed using longitudinal origin–destination employment statistics flow data, American community survey economic data, and the Open Source Routing Machine (OSRM) to identify the utility of a UAM commuter service relative to other modes of transportation. Using the implemented model, we identify New York City, San Francisco, and Los Angeles as cities with the highest potential commuter demand, and Seattle as the city most resilient to increases in delay time. A sensitivity study of demand is performed and shows that strong demand exists for short trips with low total delay times and for longer trips with a low ticket price per kilometer, with the former showing resilience to increases in operational costs and the latter showing resilience to increases in delays. The demand model is supported by a speed-flow model, which fuses highway performance monitoring system data with OpenStreetMap data to provide traffic-adjusted road segment speeds to OSRM. The speed-flow model has the capability of providing congestion data for road segments across the United States without the use of commercial data sets or routing services and is shown to improve routing duration accuracy in congested regions.","PeriodicalId":36984,"journal":{"name":"Journal of Air Transportation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135696238","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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