Aircraft trajectory prediction is the basis of approach and departure sequencing, conflict detection and resolution and other air traffic management technologies. Accurate trajectory prediction can ...help increase the airspace capacity and ensure the safe and orderly operation of aircraft. Current research focuses on single aircraft trajectory prediction without considering the interaction between aircraft. Therefore, this paper proposes a model based on the Social Long Short-Term Memory (S-LSTM) network to realize the multi-aircraft trajectory collaborative prediction. This model establishes an LSTM network for each aircraft and a pooling layer to integrate the hidden states of the associated aircraft, which can effectively capture the interaction between them. This paper takes the aircraft trajectories in the Northern California terminal area as the experimental data. The results show that, compared with the mainstream trajectory prediction models, the S-LSTM model in this paper has smaller prediction errors, which proves the superiority of the model’s performance. Additionally, another comparative experiment is conducted on airspace scenes with aircraft interactions, and it is found that S-LSTM has a better prediction effect than LSTM, which proves the effectiveness of the former considering aircraft interaction.
There is strong commercial interest in the use of large scale automated transport robots in industrial settings (e.g. warehouse robots) and we are beginning to see new applications extending these ...systems into our urban environments in the form of autonomous cars and package delivery drones. This new technology comes with new risks—increasing traffic congestion and concerns over safety; it also comes with new opportunities—massively distributed information and communication systems. In this paper, we present a method that leverages the distributed nature of the autonomous traffic to provide improved traffic throughput while maintaining strict capacity constraints across the network. Our proposed multiagent-based dynamic traffic management strategy borrows concepts from both air traffic control and highway metering lights. We introduce
controller agents
whose actions are to adjust the robots’ perceived “costs” of traveling across different parts of the traffic network. This approach allows each robot the flexibility of using its own (potentially proprietary) navigation algorithm, while still being bound by the “rules of the road.” The control policies of the agents are defined as neural networks whose weights are learned via cooperative coevolution across the entire traffic management team. Results in a real world road network and a simulated warehouse domain demonstrate that our multiagent traffic management system provides substantial improvements to overall traffic throughput in terms of number of successful trips in a fixed amount of time, as well as faster average traversal times.
Many queueing systems are subject to time-dependent changes in system parameters, such as the arrival rate or number of servers. Examples include time-dependent call volumes and agents at inbound ...call centers, time-varying air traffic at airports, time-dependent truck arrival rates at seaports, and cyclic message volumes in computer systems.
There are several approaches for the performance analysis of queueing systems with deterministic parameter changes over time. In this survey, we develop a classification scheme that groups these approaches according to their underlying key ideas into (i) numerical and analytical solutions, (ii) approaches based on models with piecewise constant parameters, and (iii) approaches based on modified system characteristics. Additionally, we identify links between the different approaches and provide a survey of applications that are categorized into service, road and air traffic, and IT systems.
•We provide an overview of approaches for the performance evaluation of time-dependent queueing systems.•We develop a classification scheme that groups approximation approaches according to their underlying key ideas.•We discuss and establish links between the different approaches.•We review and classify areas of applications for time-dependent queueing systems.
•A review of stochastic modeling applications in air traffic management is provided.•Queueing models of air traffic at airports and air traffic networks are discussed.•Stochastic optimization methods ...for strategic and tactical problems are reviewed.•Promising directions for future research are suggested.
In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management.
Recent progress in remote sensing provides much-needed, large-scale spatio-temporal information on habitat structures important for biodiversity conservation. Here we examine the potential of a newly ...launched satellite-borne radar system (Sentinel-1) to map the biodiversity of twelve taxa across five temperate forest regions in central Europe. We show that the sensitivity of radar to habitat structure is similar to that of airborne laser scanning (ALS), the current gold standard in the measurement of forest structure. Our models of different facets of biodiversity reveal that radar performs as well as ALS; median R² over twelve taxa by ALS and radar are 0.51 and 0.57 respectively for the first non-metric multidimensional scaling axes representing assemblage composition. We further demonstrate the promising predictive ability of radar-derived data with external validation based on the species composition of birds and saproxylic beetles. Establishing new area-wide biodiversity monitoring by remote sensing will require the coupling of radar data to stratified and standardized collected local species data.
The air traffic is mainly divided into en-route flight segments, arrival and departure segments inside the terminal maneuvering area, and ground operations at the airport. To support utilizing ...available capacity more efficiently, in our contribution we focus on the prediction of arrival procedures, in particular, the time-to-fly from the turn onto the final approach course to the threshold. The predictions are then used to determine advice for the controller regarding time-to-lose or time-to-gain for optimizing the separation within a sequence of aircraft. Most prediction methods developed so far provide only a point estimate for the time-to-fly. Complementary, we see the need to further account for the uncertain nature of aircraft movement based on a probabilistic prediction approach. This becomes very important in cases where the air traffic system is operated at its limits to prevent safety-critical incidents, e.g., separation infringements due to very tight separation. Our approach is based on the Quantile Regression Forest technique that can provide a measure of uncertainty of the prediction not only in form of a prediction interval but also by generating a probability distribution over the dependent variable. While the data preparation, model training, and tuning steps are identical to classic Random Forest methods, in the prediction phase, Quantile Regression Forests provide a quantile function to express the uncertainty of the prediction. After developing the model, we further investigate the interpretation of the results and provide a way for deriving advice to the controller from it. With this contribution, there is now a tool available that allows a more sophisticated prediction of time-to-fly, depending on the specific needs of the use case and which helps to separate arriving aircraft more efficiently.
•We evaluate how the provision of high-speed rail services affects tourism outcomes in Spain.•We find that air traffic, a strong predictor of tourist arrivals, is negatively affected by HSR.•However, ...HSR may have a positive (weak) direct effect on tourism but results are mixed.•Our findings add to the existing literature on the disappointing ex-post impact evaluations of HSR.
This paper evaluates how changes in the provision of high-speed rail (HSR) services affect tourism outcomes in Spain, a tourist country with the newest and longest HSR network in Europe. To do so it employs an empirical strategy based on the differences-in-differences panel data method with double fixed effects. Data are provided by Spain’s National Statistics Institute (INE) and cover 50 provinces over a 15-year time span (1998–2013). Our results provide mixed evidence about the impact of HSR accessibility on tourist outcomes. On the one hand, we find that air traffic is negatively affected by HSR and air traffic is a strong predictor of tourist arrivals. This suggests a negative indirect effect of HSR on tourist outcomes. On the other hand, HSR may have a positive (weak) direct effect on tourism. However, such result is conditioned on the measure of HSR accessibility and econometric technique used. Thus, the net effect of HSR on tourism outcomes is not consistently positive. This pattern might be attributed to a network design that does not respond to ridership needs and which has a substitution effect on air transportation, the main mode for long-distance tourist mobility.
•A technical enabler towards a fully deployed trajectory based operations environment.•Arrival traffic is sequenced with 4D closed-loop instructions in a tromboning.•The amount of CDOs is maximized ...and delay is minimized.•TMA entry time distribution greatly affects the number of neutral CDOs that can be flown.
This paper proposes to enhance the current tromboning paradigm with a four dimensional trajectory negotiation and synchronization process with the aim to maximise the number of neutral Continuous descent operations (CDOs, descents with idle thrust and no speed-brakes usage) achieved by the arriving traffic in terminal maneuvering areas (TMAs). An optimal control problem has been formulated and solved in order to generate a set of candidate CDO trajectories per aircraft, while a mixed-integer-linear programming model has been built in order to optimally assign routes of the arrival procedure and required times of arrival (RTAs) to the arriving traffic when still in cruise. The assessment has been performed for Frankfurt am Main airport (Germany), by using arrival traffic gathered from historical data. Results show that, after assigning an RTA and a route to every arriving aircraft, it is possible to maximize the number of aircraft performing CDOs while ensuring a safe time separation throughout the arrival procedure. For low traffic scenarios, the totality of traffic can be successfully scheduled, while for high traffic scenarios this is not the case and not all aircraft can be scheduled if neutral CDOs are flown. However, by assuming different arbitrarily defined arrival times to the TMA or by considering more additional shortcuts in the trombone procedure it is possible to increase the number of aircraft scheduled. Besides improving current operations in the short-mid term, the methodology presented in this paper could become a technical enabler towards a fully deployed trajectory based operations (TBO) environment.
Air traffic system is a typical complex network with dynamic delay propagation between airports. However, difficulties in measuring delay propagation strength and constructing rational networks make ...investigating the features of delay propagation based on real data from the perspective of complex network is rarely seen. In this paper, using two largest real-world flights dataset of China and the USA over a period of two months in 2018 (from Jul. to Aug.), we construct the delay propagation networks among airports by calculating causality relationship between delay time series of airports. We identify the multilayer structures of networks by k-core decomposition and reveal that a core layer with a few of tightly connected airports dominants delay propagation in the whole systems. Through the analysis on motifs, bidirectional edges are found to be the culprit to expand the scope of delay propagation from airport pairs to local structures. At the system level, we analyze the properties of communities and found that airports geographically close to each other are likely to be classified into a delay propagation community, while the delay propagation between communities is mainly affected by the air traffic flow between them. Moreover, through the comparisons of structures for the delay propagation networks on different days, we reveal the temporal dependence of delay propagation that the more serious the delay propagation in one day, the more similar it will be to that in the next day. Finally, the usefulness of features found is assessed through applying them to predict the delay propagation in the future.
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