An expert system for air traffic flow management

An expert system for the air traffic flow management (ATFM) problem is presented. Two main prototypes have been constructed, one for timetable rescheduling that attempts to modify airline timetables to smooth traffic peaks at airports during rush-hours and another for centralized flow control that works to forecast the place, time and magnitude of the congestion and to propose mitigative actions. Simulations for the Brazilian ATFM, including the principal 14 airports, show the potential usefulness of the expert system.     

Future air traffic management systems and financial decision-making constraints

The object is to identify characteristics of feasible systems for UK – and European – Air Traffic Management (ATM) in the coming decades. ATM here covers Air Traffic Control (ATC) provision plus wider issues, such as airspace design. The analytical focus is on the financial decision-making processes and constraints that will act to shape this future system. R&D work into control workload and planning based on an "ATM core concept" are proposed as likely to offer the best way forward.     

Reverse time-restricted shortest paths: Application to air traffic management

In this paper, we consider a particular class of network flow problems that seeks a shortest path, if it exists, between a source node s and a destination node d in a connected digraph, such that we arrive at node d at a specified time τ while leaving node s no earlier than a lower-bounding time LB, and where the availability of each network link is time-dependent in the sense that it can be traversed only during specified intervals of time. We refer to this problem as the reverse time-restricted shortest path problem (RTSP), and it arises, for example, in the context of generating flight plans within air traffic management approaches under severe convective weather conditions. We show that this problem is NP-hard in general, but is polynomially solvable under a special regularity condition. A pseudo-polynomial time dynamic programming algorithm is developed to solve Problem RTSP, along with an effective heap implementation strategy. Computational results using real flight generation test cases as well as random simulated problems are presented.     

A simulation based study of subliminal control for air traffic management

Under the so called Subliminal Control concept, an automated system, commanding minor speed adjustments imperceptible by the Air Traffic Controller (ATC), tries to keep the Air Traffic Controller’s risk perception low, emulating a “lucky traffic”. In this paper we outline such a concept and investigate several implementation considerations of subliminal control. A proposed subliminal controller is tested against several encounter geometries for level flights in simulations using a stochastic environment that comprises wind forecast uncertainties. The results demonstrate that subliminal control has the potential to reduce the workload of the ATC in several cases.     

Automation for task analysis of next generation air traffic management systems

The increasing span of control of Air Traffic Control enterprise automation (e.g. Flight Schedule Monitor, Departure Flow Management), along with lean-processes and pay-for-performance business models, has placed increased emphasis on operator training time and error rates. There are two traditional approaches to the design of human–computer interaction (HCI) to minimize training time and reduce error rates: (1) experimental user testing provides the most accurate assessment of training time and error rates, but occurs too late in the development cycle and is cost prohibitive, (2) manual review methods (e.g. cognitive walkthrough) can be used earlier in the development cycle, but suffer from poor accuracy and poor inter-rater reliability. Recent development of “affordable” human performance models provide the basis for the automation of task analysis and HCI design to obtain low cost, accurate, estimates of training time and error rates early in the development cycle.This paper describes a usability/HCI analysis tool that this intended for use by design engineers in the course of their software engineering duties. The tool computes estimates of trials-to-mastery (i.e. time to competence for training) and the probability of failure-to-complete for each task. The HCI required to complete a task on the automation under development is entered into the web-based tool via a form. Assessments of the salience of visual cues to prompt operator actions for the proposed design are used to compute training time and error rates. The web-based tool enables designers in multiple locations to review and contribute to the design. An example analysis is provided along with a discussion of the limitations of the tool and directions for future research.     

A methodology for identifying similar days in air traffic flow management initiative planning

This article describes a methodology for selecting days that are comparable in terms of the conditions faced during air traffic flow management initiative planning. This methodology includes the use of specific data sources, specific features of calendar days defined using these data sources, and the application of a specific form of classification and then cluster analysis. The application of this methodology will produce results that enable historical analysis of the use of initiatives and evaluation of the relative success of different courses of action. Several challenges are overcome here including the need to identify the appropriate machine learning algorithms to apply, to quantify the differences between calendar days, to select features describing days, to obtain appropriate raw data, and to evaluate results in a meaningful way. These challenges are overcome via a review of relevant literature, the identification and trial of several useful models and data sets, and careful application of methods. For example, the cluster analysis that ultimately selects sets of similar days uses a distance metric based on variable importance measures from a separate classification model of observed initiatives. The methodology defined here is applied to the New York area, although it could be applied by other researchers to other areas.     

Determinants of air cargo traffic in California

Studies on the economic impacts of air cargo traffic have been gaining traction in recent years. The slowed growth of air cargo traffic at California’s airports, however, has raised pressing questions about the determinants of air cargo traffic. Specifically, it would be useful to know how California’s air cargo traffic is affected by urban economic characteristics. Accordingly, this study estimates the socioeconomic determinants of air cargo traffic across cities in California. We construct a 7-year panel (2003–2009) using quarterly employment, wage, population, and traffic data for metro areas in the state. Our results reveal that the concentrations of both service and manufacturing employment impact the volume of outbound air cargo. Total air cargo traffic is found to grow faster than population, while the corresponding domestic traffic grows less than proportionally to city size. Wages play a significant role in determining both total and domestic air cargo movement. We provide point estimates for traffic diversion between cities, showing that 80% of air cargo traffic is diverted away from a small city located within 100 miles of a large one. Using socioeconomic and demographic forecasts prepared for California’s Department of Transportation, we also forecast metro-level total and domestic air cargo tonnage for the years 2010–2040. Our forecasts for this period indicate that California’s total (domestic) air cargo traffic will increase at an average rate of 5.9% (4.4%) per year.     

Characterization and prediction of air traffic delays

This paper presents a new class of models for predicting air traffic delays. The proposed models consider both temporal and spatial (that is, network) delay states as explanatory variables, and use Random Forest algorithms to predict departure delays 2–24 h in the future. In addition to local delay variables that describe the arrival or departure delay states of the most influential airports and links (origin–destination pairs) in the network, new network delay variables that characterize the global delay state of the entire National Airspace System at the time of prediction are proposed. The paper analyzes the performance of the proposed prediction models in both classifying delays as above or below a certain threshold, as well as predicting delay values. The models are trained and validated on operational data from 2007 and 2008, and are evaluated using the 100 most-delayed links in the system. The results show that for a 2-h forecast horizon, the average test error over these 100 links is 19% when classifying delays as above or below 60 min. Similarly, the average over these 100 links of the median test error is found to be 21 min when predicting departure delays for a 2-h forecast horizon. The effects of changes in the classification threshold and forecast horizon on prediction performance are studied.     

Computing 4D near-optimal trajectories for dynamic air traffic flow management with column generation and branch-and-price

Abstract

The current air traffic system faces recurrent saturation problems. Numerous studies are dedicated to this issue, including the present research on a new dynamic regulation filter holding frequent trajectory optimisations in a real-time sliding horizon loop process. We consider a trajectory optimisation problem arising in this context, where a feasible four-dimensional (4D) trajectory is to be built and assigned to each regulated flight to suppress sector overloads while minimising the cost of the chosen policy. We model this problem with a mixed integer linear programme and solve it with a branch-and-price approach. The pricing sub-problem looks for feasible trajectories in a dynamic three-dimensional (3D) network and is solved with a specific algorithm based on shortest path labelling algorithms and on dynamic programming. Each algorithm is tested on real-world data corresponding to a complete traffic day in the European air traffic system; experimental results, including computing times measurement, validate the solution process.     

Identification of artificial intelligence applications in air traffic control

Artificial intelligence consists of a broad range of computer science techniques directed at such problems as pattern matching, language processing and solving highly complex, ill-defined problems. The paper briefly reviews the current state of development of the artificial intelligence field and explores how these techniques might be applied to air traffic control. Seven possible control strategies are identified, ranging from visual and electronic collision avoidance, through proposed enhancements of the current U.S. air traffic control system, to strategies in which aircraft follow predetermined, deconflicted flight paths. A large number of possible artificial intelligence applications are grouped into seven functional areas, and ways in which they might be incorporated into the different control strategies are discussed. The paper concludes by considering some of the implementation issues that will arise in the course of applying artificial intelligence techniques to air traffic control.     

Assessment of air quality near traffic intersections in Bangalore city using air quality indices

Air quality indices are used for local and regional air quality management in many metro cities of the world. In the present study, air quality indices have been calculated using the US Environmental Protection Agency procedure to assess the status of ambient air quality near busy traffic intersections in Bangalore, India. The measured 24 h average criteria pollutants such as sulfur dioxide, oxides of nitrogen, respirable suspended particulate matter and suspended particulate matter for the period from 1997 to 2005 at three air quality monitoring stations are used for the development of AQIs. The result indicated that the air pollution at all the three air quality monitoring stations can be characterized as ‘good’ and ‘moderate’ for SO₂ and NO_x concentrations for all days from 1997 to 2004. Analysis of air quality indices values for both forms of suspended matter concentrations during 1999–2005 indicates 91% and 94% of the times days are in category ‘good’ and ‘moderate’. The yearly average air quality indices values of respirable suspended particulate matter and suspended particulate matter concentrations indicated decreasing trend and are coming under the category of ‘good’ and ‘moderate’ form the category of ‘poor’ and ‘very poor’.     

The ambivalance underlying highway traffic management

The aim of the paper is to examine critically the policies which have been developed to manage and control highway traffic in urban areas in the U.K., in the belief that conclusions can be drawn which are of international significance. In particular, the paper explores the objectives underlying these policies and compares them with the outcome which has been observed. The underlying theme of the paper is that of ambivalence. It is shown that although many policies—for example those which promote both transit and car use—can be seen to be “pulling in opposite directions”, such ambivalence may be inevitable with an approach involving the use of generalised objectives. It is concluded that when objectives are counteractive, choices have to be made which will involve the subjugation of one or more objectives, otherwise it is likely that none of the objectives will be achieved.     

Development of network restructuring models for improved air traffic forecasts

Current air traffic forecast methods employed by the United States Federal Aviation Administration function under the assumption that the structure of the network of routes operated by airlines will not change; that is, no new routes will be added nor existing ones removed. However, in reality the competitive nature of the airline industry is such that new routes are routinely added between cities possessing significant passenger demand; city-pairs are also removed. Such phenomena generates a gap between the forecasted and actual state of the US Air Transportation System in the long term, providing insufficient situational awareness to major stakeholders and decision-makers in their consideration of major policy and technology changes. To address this gap, we have developed and compared three algorithms that forecast the likelihood of un-connected city-pairs being connected by service in the future, primarily based on the nodal characteristics of airports in the US network. Validation is performed by feeding historical data to each algorithm and then comparing the accuracy and precision of new city-pairs forecasted using knowledge of actual new city-pairs that developed. While an Artificial Neural Network produces superior precision, fitness function and logistic regression algorithms provide good representation of the distribution of new route types as well as greater flexibility for modeling future scenarios. However, these latter two algorithms face difficulty in resolving differences among the large number of ‘spoke’ airports in the network – additional parameters that may be able to differentiate them are currently under review. These insights gained are valuable stepping stones for exploiting knowledge of restructuring in the service route network to improve overall forecasts that drive policy and technology decision-making.     

Stated choice valuation of urban traffic air pollution and noise

In this study environmental problems related to urban traffic are valued by Stated Choice. Stated Choice is a Stated Preference method in which non-market goods are assessed relatively to each other and not in absolute amounts as with the more common Contingent Valuation Method. The use of Stated Choice to estimate people’s willingness to pay to reduce environmental problems caused by road traffic can be seen as an extension of the established use of Stated Choice in transport research. Despite the fact that the results from this study have been used by the Norwegian Public Roads Administration in their cost benefit analyses since 1995, this paper points to the fact that considerable uncertainty remains with regard to (1) the area of validity of the results, (2) the impact on the valuations of interaction effects and other general methodological problems with Stated Preference methods, and (3) problems related to the complex choice situation of Stated Choice.     

A review of Australian traffic system design models

The demands for traffic infrastructure are increasing. Yet over the last decade investment in new infrastructure has decreased at all levels. Traffic systems designers are, therefore, being asked to be more accurate in their prediction of the impacts of changes, to analyse ever more complex situations and to extract more from the existing traffic system. This paper reviews developments in techniques for analysing the impacts of changes in the traffic system. It looks at intersection, route, network, parking lot and public transport design models that have been developed in Australia. Particular emphasis is given to the considerable developments in microcomputers and graphics and the impact these are having on the models. Future developments are also discussed.     

Network models in traffic management and control

Abstract

The context for network modelling in traffic management and control is described in terms of the current area‐wide nature of traffic management and the range of objectives to which it can contribute. Representation of a road system and traffic management measures in terms of nodes and links and parameters associated with them is described. It is shown that the pattern of traffic has to be represented not only in terms of flows on links of the network but also in terms of numbers of movements per unit time between points of entry to and points of exit from the area being modelled. In modelling so far, these numbers of movements are regarded as given, but the routes taken are estimated by traffic assignment. Models can so far be used for comparison of a range of given schemes and for optimization of traffic control within a scheme. Variation over time is a central feature of the modelling, and this requires the use of time‐dependent queueing theory, and the specification of numbers of movements for a succession of periods of between 10 and 30 minutes. Theoretical approaches to the resulting problems of modelling and optimization are discussed, and the ways in which these are supplemented by heuristic methods in currently available models is described. Some requirements‐for further research are outlined.     

Optimal traffic assignment to urban streets with air pollution consideration

Traffic assignment is usually determined solely on the basis of minimum travel time through the network. The present study on traffic assignment has taken into account not only traffic performance but also air quality over the street. A simple model of highway air pollution is developed by considering macroscopic material balance of polluted air mass over a segment of a highway that passes through an urban area, A new traffic assignment scheme has been developed based on the air pollution model. The optimal traffic assignment obtained by the new scheme is affected significantly by meteorological conditions.     

ATCEM: a synthetic model for evaluating air traffic complexity

Air traffic complexity, which measures the disorder of air traffic distribution, has become the critical indicator to reflect air traffic controller workload in air traffic management (ATM) system. However, it is hard to assess the system accurately because there are too many correlated factors, which make the air traffic complexity nonlinear. This paper presents an air traffic complexity evaluation model with integrated classification using computational intelligence (ATCEM). To avoid redundant factors, critical factors contributing to complexity are analyzed and selected from numerous factors in the ATCEM. Subsequently, to construct the mapping relationship between selected factors and air traffic complexity, an integrated classifier is built in ATCEM. With efficient training and learning based on aviation domain knowledge, the integrated classifier can effectively and stably reflect the mapping relationship between selected factors and the category of air traffic complexity to ensure the precision of the evaluation. Empirical studies using real data of the southwest airspace of China show that the ATCEM outperforms a number of state‐of‐the‐art models. Moreover, using the critical complexity factors selected in ATCEM, the air traffic complexity could be effectively estimated. Copyright © 2015 John Wiley & Sons, Ltd.     

Multiagent architectures for intelligent traffic management systems

This paper reports our experiences with agent-based architectures for intelligent traffic management systems. We describe and compare integrated TRYS and TRYS autonomous agents, two multiagent systems that perform decision support for real-time traffic management in the urban motorway network around Barcelona. Both systems draw upon traffic management agents that use similar knowledge-based reasoning techniques in order to deal with local traffic problems. Still, the former achieves agent coordination based on a traditional centralized mechanism, while in the latter coordination emerges upon the lateral interaction of autonomous traffic management agents. We evaluate the potentials and drawbacks of both multiagent architectures for the domain, and develop some conclusions respecting the general applicability of multiagent architectures for intelligent traffic management.