On-demand assessment of air traffic impact of blocking airspace

2018 ◽  
Vol 122 (1258) ◽  
pp. 1985-2009 ◽  
Author(s):  
A. Srivastava ◽  
T. St. Clair ◽  
G. Pan
Keyword(s):  
Air Traffic ◽  
Large Set ◽  
Projection Model ◽  
Traffic Demand ◽  
Traffic Pattern ◽  
On Demand ◽  
Demand Assessment ◽  
The Future ◽  
Traffic Impact ◽  
The Impact

ABSTRACTThe Federal Aviation Administration often blocks strategically located airspace volumes to ensure safety during a variety of operations that are potentially hazardous to aircraft, such as space launches. As the frequency of these operations increases, there is a growing need to deepen collaboration and transparency between stakeholders regarding the use of airspace. This collaboration can be supported by models and capabilities to quickly assess the impact of airspace closures, up to 12 months into the future. This paper presents a technique to enable a ‘what-if’ analysis capability coupled with a prediction model, whereby changes in airspace dimension, location, and activation time are reflected instantaneously as measures of projected impact. The technique can also be used for quick post-operations analysis using historical traffic data and to develop air traffic impact assessment capabilities accessible to a broad range of users outside of the air traffic domain. This research has three key components: developing a model to predict air traffic demand up to 12 months into the future, modelling air traffic impact to the affected traffic, and reducing this information into a data structure that can support on-demand analysis. The focus of this paper is on new techniques to predict demand using a large set of historical track data and further encode these projections to support the quick assessment of the impact of blocking various airspace volumes. Initial results show that the proposed data reduction scheme accurately represented the traffic crossing an airspace and resulted in data size reduction by over 50%. The projection model performed well, the actual number of impacted flights were within the estimated range of approximately 80% of the time. Finally, the responsiveness of the web-based prototype developed to illustrate the concept demonstrated the model’s ability to support an on-demand assessment of the air traffic impact of blocking airspace. A significant limitation of the projection model is that it is based on the historical traffic pattern within the U.S. airspace; separate analysis is needed to adapt it to other geographical location.

scholarly journals Airspace Capacity Assessment with Augmented Cell Transmission Model

2021 ◽  
Author(s):  
Wei Gao ◽  
Man Liang
Keyword(s):  
Air Traffic ◽  
Mixed Integer ◽  
Transmission Model ◽  
Capacity Assessment ◽  
Fast Time ◽  
Cell Transmission Model ◽  
Traffic Demand ◽  
Airport Capacity ◽  
Cell Transmission ◽  
The Impact

Air traffic congestion is caused by the unbalance between increasing traffic demand and saturating capacity. Flight delay not only causes huge economical lost, but also has very negative environmental impact in the whole air transportation system. In order to identify the impact of extended TMA on airport capacity, an airspace capacity assessment method based on augmented cell transmission model was proposed. Firstly, the airspace structure was modeled with points, segments, layers, and cells. Secondly, mixed integer linear programming model was built up with maximum throughput or capacity as the objective function. Finally, genetic algorithm was used to find the optimal result, and the results were validated by comparing with the fast-time simulation results generated by total airspace and airport modeler (TAAM) software. It is found that the proposed method could achieve a relatively accurate result in a much affordable and fast way. The numerical results could be very helpful for air traffic controllers to analyze the dynamic traffic flow entering and exiting TMA, so as to make decisions via reasonable analysis and do planning in advance by referring to the airport capacity.

scholarly journals Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 379 ◽  
Author(s):  
Victor Gomez Comendador ◽  
Rosa Arnaldo Valdés ◽  
Manuel Villegas Diaz ◽  
Eva Puntero Parla ◽  
Danlin Zheng
Keyword(s):  
Bayesian Network ◽  
Traffic Control ◽  
Air Traffic ◽  
Network Modelling ◽  
Traffic Demand ◽  
Complexity Metrics ◽  
Dynamic Airspace Configuration ◽  
Input Variables ◽  
The Impact

Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators” in the “complexity metrics”. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve.

scholarly journals A Novel Performance Framework and Methodology to Analyze the Impact of 4D Trajectory Based Operations in the Future Air Traffic Management System

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Sergio Ruiz ◽  
Javier Lopez Leones ◽  
Andrea Ranieri
Keyword(s):  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Performance Framework ◽  
Performance Requirements ◽  
The Future ◽  
Traffic Management System ◽  
And Performance ◽  
The Impact

The introduction of new Air Traffic Management (ATM) concepts such as Trajectory Based Operations (TBO) may produce a significant impact in all performance areas, that is, safety, capacity, flight efficiency, and others. The performance framework in use today has been tailored to the operational needs of the current ATM system and must evolve to fulfill the new needs and challenges brought by the TBO content. This paper presents a novel performance assessment framework and methodology adapted to the TBO concept. This framework can assess the key performance areas (KPAs) of safety, capacity, and flight efficiency; equity and fairness are also considered in this research, in line with recent ATM trends. A case study is presented to show the applicability of the framework and to illustrate how some of the complex interdependencies among KPAs can be captured with the proposed approach. This case study explores the TBO concept of “strategic 4D trajectory deconfliction,” where the early separation tasks of 4D trajectories at multisector level are assessed. The framework presented in this paper could potentially support the target-setting and performance requirements identification that should be fulfilled in the future ATM system to ensure determined levels of performance.

Evaluation of Signalized Intersection Service Level in the Traffic Impact Assessment

2013 ◽  
Vol 869-870 ◽  
pp. 327-333
Author(s):  
Qian Wang ◽  
Chun Fu Shao
Keyword(s):  
Impact Assessment ◽  
Traffic Flow ◽  
Service Level ◽  
Signalized Intersection ◽  
Traffic Demand ◽  
The Road ◽  
Traffic Impact ◽  
On The Road ◽  
Vehicle Capacity ◽  
The Impact

Traffic Impact Assessment focuses on analysis and evaluation of the traffic flow generated by the proposed project impact on the road network in the future, through comparing the sections,intersection and other transportation infrastructure indexes such as traffic flow and road vehicle capacity, evaluate whether the traffic system can meet the increased traffic demand. In this paper, make the delay time and the road saturation as the evaluation index, studies the influence scope of the key signal intersection service level, in order to assess the impact of new projects on the signalized intersection. Cited Haikou province Hongzhou center as an example, based on the investigation of the traffic flow, calculate the time delay and road saturation to analyze the service level.

Snowmelt contribution to seasonal runoff: Lessons learned from using a bucket-type model on a large set of catchments

2020 ◽  
Author(s):  
Michal Jenicek ◽  
Ondrej Ledvinka
Keyword(s):  
Groundwater Recharge ◽  
Snow Water Equivalent ◽  
Large Set ◽  
Annual Variations ◽  
Low Flows ◽  
Snow Storage ◽  
The Future ◽  
Seasonal Runoff ◽  
The Impact ◽  
Catchment Runoff

<p>The streamflow seasonality in mountain catchments is largely influenced by snow. However, a shift from snowfall to rain is expected in the future. Consequently, a decrease in snow storage and earlier snowmelt is predicted, which will cause changes in spring and summer runoff. The objectives of this study were to quantify 1) how inter-annual variations in snow storages affect spring and summer runoff, including summer low flows and 2) the importance of snowmelt in generating runoff compared to rainfall. The snow storage, groundwater recharge and streamflow were simulated for 59 mountain catchments in Czechia in the period 1980–2014 using a bucket-type catchment model. The model performance was evaluated against observed daily runoff and snow water equivalent. Hypothetical simulations were performed, which allowed us to analyse the effect of inter-annual variations in snow storage on seasonal runoff separately from other components of the water balance. This was done in the HBV snow routine using the threshold temperature T<sub>T</sub> that differentiates between snow and rain and sets the air temperature of snowmelt onset. By changing the T<sub>T</sub>, we can control the amount of accumulated snow and snowmelt timing, while other variables remain unaffected.</p><p>The results showed that 17-42% (26% on average) of the total runoff in study catchments originates as snowmelt, despite the fact that only 12-37% (20% on average) of the precipitation falls as snow. This means that snow is more effective in generating catchment runoff compared to liquid precipitation. This was documented by modelling experiments which showed that total annual runoff and groundwater recharge decreases in the case of a precipitation shift from snow to rain. In general, snow-poor years are clearly characterized by a lower snowmelt runoff contribution compared to snow-rich years in the analysed period. Additionally, snowmelt started earlier in these snow-poor years and caused lower groundwater recharge. This also affected summer baseflow. For most of the catchments, the lowest summer baseflow was reached in years with both relatively low summer precipitation and snow storage. This showed that summer low flows (directly related to baseflow) in our study catchments are not only a function of low precipitation and high evapotranspiration, but they are significantly affected by previous winter snowpack. This effect might intensify the summer low flows in the future when generally less snow is expected.</p><p>Modelling experiments also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.</p>

4D-trajectory time windows: definition and uncertainty management

2019 ◽  
Vol 91 (5) ◽  
pp. 761-782 ◽  
Author(s):  
Álvaro Rodríguez-Sanz ◽  
Fernando Gómez Comendador ◽  
Rosa M. Arnaldo Valdés ◽  
Javier A. Pérez-Castán ◽  
Pablo González García ◽  
...  
Keyword(s):  
Traffic Management ◽  
Time Windows ◽  
Air Traffic ◽  
Operational Environment ◽  
Mass Model ◽  
Content Type ◽  
Fundamental Parameters ◽  
The Future ◽  
Operational Concept ◽  
The Impact

PurposeThe use of the 4D trajectory operational concept in the future air traffic management (ATM) system will require the aircraft to meet very accurately an arrival time over a designated checkpoint. To do this, time intervals known as time windows (TW) are defined. The purpose of this paper is to develop a methodology to characterise these TWs and to manage the uncertainty associated with the evolution of 4D trajectories.Design/methodology/approach4D trajectories are modelled using a point mass model and EUROCONTROL’s BADA methodology. The authors stochastically evaluate the variability of the parameters that influence 4D trajectories using Monte Carlo simulation. This enables the authors to delimit TWs for several checkpoints. Finally, the authors set out a causal model, based on a Bayesian network approach, to evaluate the impact of variations in fundamental parameters at the chosen checkpoints.FindingsThe initial results show that the proposed TW model limits the deviation in time to less than 27 s at the checkpoints of an en-route segment (300 NM).Practical implicationsThe objective of new trajectory-based operations is to efficiently and strategically manage the expected increase in air traffic volumes and to apply tactical interventions as a last resort only. We need new tools to support 4D trajectory management functions such as strategic and collaborative planning. The authors propose a novel approach for to ensure aircraft punctuality.Originality/valueThe main contribution of the paper is the development of a model to deal with uncertainty and to increase predictability in 4D trajectories, which are key elements of the future airspace operational environment.

Exploring Deep Reinforcement Learning for Task Dispatching in Autonomous On-Demand Services

2021 ◽  
Vol 15 (3) ◽  
pp. 1-23
Author(s):  
Lei Yang ◽  
Xi Yu ◽  
Jiannong Cao ◽  
Xuxun Liu ◽  
Pan Zhou
Keyword(s):  
Reinforcement Learning ◽  
Response Time ◽  
Expected Value ◽  
Future Perspective ◽  
Response Patterns ◽  
On Demand ◽  
The Future ◽  
Future Demands ◽  
Real World Datasets ◽  
The Impact

Autonomous on-demand services, such as GOGOX (formerly GoGoVan) in Hong Kong, provide a platform for users to request services and for suppliers to meet such demands. In such a platform, the suppliers have autonomy to accept or reject the demands to be dispatched to him/her, so it is challenging to make an online matching between demands and suppliers. Existing methods use round-based approaches to dispatch demands. In these works, the dispatching decision is based on the predicted response patterns of suppliers to demands in the current round, but they all fail to consider the impact of future demands and suppliers on the current dispatching decision. This could lead to taking a suboptimal dispatching decision from the future perspective. To solve this problem, we propose a novel demand dispatching model using deep reinforcement learning. In this model, we make each demand as an agent. The action of each agent, i.e., the dispatching decision of each demand, is determined by a centralized algorithm in a coordinated way. The model works in the following two steps. (1) It learns the demand’s expected value in each spatiotemporal state using historical transition data. (2) Based on the learned values, it conducts a Many-To-Many dispatching using a combinatorial optimization algorithm by considering both immediate rewards and expected values of demands in the next round. In order to get a higher total reward, the demands with a high expected value (short response time) in the future may be delayed to the next round. On the contrary, the demands with a low expected value (long response time) in the future would be dispatched immediately. Through extensive experiments using real-world datasets, we show that the proposed model outperforms the existing models in terms of Cancellation Rate and Average Response Time.

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