Traffic Management Initiatives Research Articles

Lane Detection, Segmentation, Pothole Detection and Traffic Sign Recognition for ADAS

Lane detection, a critical aspect of advanced driver assistance systems (ADAS) and autonomous vehicles, is addressed in this work, combining classical computer vision methods with deep learning techniques. The proposed solution, utilizing a UNet-based model trained with TensorFlow and Keras, enhances vehicle perception for intelligent transportation systems. Integrated functionalities for pothole detection and traffic sign detection further contribute to safety and efficiency, enabling vehicles to identify road hazards and comply with regulations. The system encompasses data preprocessing, model training, and real-time video analysis, while a classical lane detection pipeline using OpenCV showcases various stages such as grayscale conversion, Gaussian blur, Canny edge detection, masking, Hough transform, and lane overlay. This comprehensive approach supports road safety, traffic management, and transportation efficiency initiatives, making significant strides in intelligent transportation systems and urban planning.

Optimizing Air Traffic Management in Europe by the introduction of the Single European Sky

Europe´s airspace is facing an arduous situation due to its congestion, which leads to multiple delays and inefficient flights. Introducing the Single European Sky Air Traffic Management (SESAR) initiative will conquer a transformative shift in Europe´s Air Traffic Management (ATM), achieving more optimised and sustainable routes throughout the continent. Therefore, through the successful implementation of the SESAR initiative, we will enhance the performance of Europe´s ATMs in capacity, safety, efficiency, and sustainability. This paper evaluates Europe´s current airspace and highlights the positive impacts of the initiative in the different areas of improvement. Utilising current data provided by the airlines, qualitative methods are applied to prove the effect that SESAR will achieve on different routes. The research results demonstrate how introducing the SESAR will reduce flight distance, fuel consumption, and emissions. In conclusion, this research emphasises the significance of implementing the SESAR to update air traffic control throughout Europe. The aviation industry is experiencing a modernisation that is expected to result in lower operating costs, fewer delays, and a reduction in the environmental impact of their flights.

Designing Traffic Management Strategies Using Reinforcement Learning Techniques

The future vision for traffic flow management is one that leverages advanced automation to assist human decision-makers in the identification of potential constraints and the development of resolution strategies. What makes this problem so challenging is the inherent uncertainty associated with forecasting these constraints, leaving human decision-makers reliant on experience to devise effective traffic management initiatives to mitigate demand in excess of resource capacity. This paper proposes to employ artificial intelligence-based methods to recommend traffic management initiatives under forecast uncertainty and to do so in a real-time planning context. The proposed algorithm consists of 1) a policy network that is generated offline using an Expert Iteration algorithm, 2) a statistical model that updates the likelihood of constraint futures based on observations, and 3) a Monte Carlo tree search algorithm that explores possible combinations of traffic management initiatives to identify the recommended actions for the current decision. The skill introduced by each of the algorithmic components is assessed for a case study focused on managing arrivals into the Atlanta Hartsfield–Jackson International Airport over 92 validation days.

Identification of Airport Similar Days Based on K-prototype Cluster and Grey Correlation Analysis

Considering similar air traffic control techniques for the present based on close historical dates is a good approach due to the unpredictability of weather and air traffic, as well as to increase controller efficiency. A K-prototype clustering technique and grey correlation analysis are proposed to discover similar days to address the problem of similar identification. Firstly, the weather and air traffic datasets are used to create a set of features broken down into numerical and categorical attributes. Secondly, the historical data are clustered using the K-prototype clustering, which is then paired with grey correlation analysis to identify days similar to the reference day and examine the traffic management initiatives employed on that day. Finally, the research uses actual weather information and aircraft schedules from Nanjing Lukou International Airport as examples. The outcomes demonstrate that the similar days picked by the model are representative and can serve as a foundation for airport controllers' decision-making.

Recommending Strategic Air Traffic Management Initiatives in Convective Weather

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.

Data-Driven Analysis for Calculated Time Over in Air Traffic Flow Management

Excessive air traffic demand saturates air traffic control and causes traffic delays due to limited airspace and airport capacity. Air traffic flow management (ATFM) is used to regulate such demand using traffic management initiatives (TMIs), such as ground delay, miles-in-trail and speed adjustment. The Calculated Time Over (CTO), which is a time-assignment TMI for an in-flight aircraft, is being widely developed and CTO operational trials have also been conducted in Japan. This study develops means of estimating several important indices, including the compliance rate and mean expected delay, using data collected in the trial and shadow CTO operations. The estimation methods show variation in indices with differing maximum assigned CTO, while the analytical results suggest the optimal maximum assigned CTO. Furthermore, a variable maximum assigned CTO is proposed to improve the CTO operation. The analytical results show that the proposed method can improve the compliance rate as well as the mean expected delay.

Air Traffic Flow Management Integrating Separation Management and Ground Holding: An Efficiency-Equity Bi-objective Perspective

Motivated by a recent air traffic flow management (ATFM) initiative in the Southeast Asia region, this paper considers a new ATFM model that integrates Minutes-In-Trail (MINIT) with ground holding and jointly considers system efficiency and inter-airline equity. Three equity metrics are investigated including two Gini coefficient-based metrics previously not considered for inter-airline equity. We formulate both deterministic and robust bi-objective optimization models, with the notion of robustness introduced to equity as well as to efficiency. Several solution approaches are proposed, including an exact approach based on the ε-constraint method and linearization techniques, and heuristics approaches which adapt nondominated sorting genetic algorithm (NSGA-II) and a learning-augmented nondominated sorting move (L-NSM) algorithm. For solving the robust bi-objective models, Monte Carlo sampling is further integrated with L-NSM. As inter-airline equity is of primary concern in the models, we also propose three methods for solution selection from a Pareto frontier for potential ATFM use that take into account flight-level equity from different perspectives. Extensive numerical analysis is conducted showing superior performance of L-NSM in both computation time and solution quality for small, medium, and large cases, benefits of the proposed ATFM initiative over a case with only MINIT but no ground holding, differences and similarities of the three equity metrics used, characteristics of the selected solutions from Pareto frontiers using the three proposed methods, benefits of considering robustness, and results sensitivity to key parameters. Findings of this research shed lights on further development of integrated ATFM modeling of separation management with ground holding with bi-objective consideration and relevant practices.

Efficient and fair traffic flow management for on-demand air mobility

The increased use of drones and air-taxis is expected to make airspace resources more congested, necessitating the use of unmanned aircraft systems traffic management (UTM) initiatives to ensure safe and efficient operations. Typically, strategic UTM involves solving an optimization problem that ensures that proposed flight schedules do not exceed airspace and vertiport capacities. However, the dynamic nature and low lead-time of applications such as on-demand delivery and urban air mobility traffic may reduce the efficiency and fairness of strategic UTM. We first discuss the adaptation of three fairness metrics into a traffic flow management problem (TFMP). Then, with computational simulations of a drone package delivery scenario in Toulouse, we evaluate trade-offs in the TFMP between efficiency and fairness, as well as between different fairness metrics. We show that system fairness can be improved with little loss in efficiency. We also consider two approaches to the integrated scheduling of both high lead-time flights (i.e., flights with a schedule known in advance) and low lead-time flights in a rolling horizon optimization framework. We compare the performance of both approaches for different horizon lengths and under varying proportions of high and low lead-time flights.

Causal analysis of flight en route inefficiency

En route inefficiency is measured in terms of extra distance flown by an aircraft, above a benchmark distance that relates to the theoretical shortest distance route (great circle route). In this paper, we have explored causal relations among en route inefficiency with multiple identified sources: convective weather, wind, miles-in-trail (MIT) restrictions, airspace flow programs (AFPs) and special activity airspace (SAA). We propose two mechanisms – strategic route choice and tactical reroute – to ascribe flight en route inefficiency to these factors. In our framework, we first propose an efficient trajectory clustering algorithm to identify nominal routes that reveal both air traffic flow patterns and air route structures in the national airspace system. Second, we develop a tree-based searching algorithm that matches different causal factors to the identified nominal routes in a 4-dimensional manner. Third, we employ a discrete choice modeling framework to quantitatively understand the flight route choice process, and establish a linear model to understand the tactical reroute process. Finally, we estimate the contributions of the identified causal factors to flight inefficiency through counterfactual analysis. Numerical experiments based on 8 representative airport pairs suggest strong negative impact of convection and headwind on route choice process, while MIT, AFP and SAA only demonstrate negative effects on some pairs. We have also shown that the contributions of convective weather, wind, traffic management initiatives, and SAA are up to 7.1%, 14.0%, 4.4%, 21.9%, respectively.

Network-centric benchmarking of operational performance in aviation

Performance analysis of the air traffic operations is challenging because of the need to account for weather impacts and network effects. In this paper, we propose a framework that uses network clustering to identify baselines for benchmarking airline on-time performance. We demonstrate our framework by computing cancellation and departure delay baselines using US flight data for the years 2014–16. Subsequently, we use these baselines to benchmark daily on-time performance at the system-wide, airline-, and airport-specific levels, for both mainline and regional carriers. This framework enables an airline to conduct self- and peer-comparisons, evaluate improvements over time, and diagnose causes of poor on-time performance. Furthermore, our framework can be used by system operators to identify long-term trends in traffic management initiatives.

Machine Learning Approach to the Analysis of Traffic Management Initiatives

Ground delay programs and ground stops are implemented to ensure that airport and flight operations remain safe in spite of constraint(s) at affected airports. Occasionally, ground stops are issued during ongoing ground delay programs, and vice versa, which hinders their efficient planning and implementation. Consequently, this research outlines a methodology for the analysis of their coincidence so as to identify underlying factors and better understand their occurrence. This is achieved by 1) fusing weather, ground delay program, and ground stop data; and 2) leveraging machine learning algorithms, partial dependence plots, and decision trees to assess and understand how different factors influence the coincidence of ground delay programs and ground stops. The methodology was implemented and tested using data from LaGuardia and San Francisco International Airports in order to test its robustness at airports with different weather conditions, locations, etcetera. The boosting ensemble algorithm was identified as the best-suited algorithm for both airports, and it was observed that different factors influence the occurrence of this phenomenon at both airports. This work also discusses how decision trees and partial dependence plots can be leveraged to analyze how different factors influence the coincidence of weather-related ground delay programs and ground stops.

Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.

Certification challenges for next-generation avionics and air traffic management systems

Air traffic is doubling every 15 years, and aviation systems must modernize to address sustainability challenges. The need to balance capacity, efficiency, safety, and environmental requirements is reflected by the several air traffic management (ATM) and avionics modernization initiatives under way. The major collaborative research programs today are the European Union's Single European Sky ATM Research (SESAR) project and the United States' Next-Generation Air Transportation System (NextGen) led by the Federal Aviation Administration (FAA). Other modernization initiatives include the Collaborative Action for Renovation of Air Traffic Systems in Japan, SIRIUS in Brazil, OneSky in Australia, and similar programs in Canada, China, India, and Russia [1]. The International Civil Aviation Organization (ICAO) has authorized a globally coordinated plan, published as the Global Air Navigation Plan (GANP) [1], to guide the harmonized implementation of communication, navigation, surveillance, and avionics (CNS+A) enhancements across regions and states. In the CNS+A context, aircraft safety is a shared responsibility between airborne and ground-based resources [1]. Hence, this is a safety challenge requiring changes to the current regulatory framework to properly capture the nature of this shared responsibility and the concept of integrated CNS+A systems. Certification of aircraft and ground equipment (hardware and software) and organizational approvals are essential elements to ensure continued and enhanced safety.

Unsupervised prototype reduction for data exploration and an application to air traffic management initiatives

We discuss a new approach to unsupervised learning and data exploration that involves summarizing a large data set using a small set of “representative” elements. These representatives may be presented to a user in order to provide intuition regarding the distribution of observations. Alternatively, these representatives can be used as cases for more detailed analysis. We call the problem of selecting the representatives the unsupervised prototype reduction problem. We discuss the KC-UPR method for this problem and compare it to other existing methods that may be applied to this problem. We propose a new type of distance measure that allows for more interpretable presentation of results from the KC-UPR method. We demonstrate how solutions from the unsupervised prototype reduction problem may be used to provide decision support for the planning of air traffic management initiatives, and we produce computational results that compare the effectiveness of several methods in this application. We also provide an example of how the KC-UPR method can be used for data exploration, using data from air traffic management initiatives at Newark Liberty International Airport.

A Passenger-Centric Approach to Air Traffic Flow Management

Traffic flow management initiatives aim to balance the flows of vehicles in capacity-constrained transportation networks to minimize congestion costs. While existing research has focused on vehicle-centric delay minimization objectives, this may not result in most efficient outcomes from the end users' perspective when travel itineraries involve connections between multiple vehicles. This paper develops an original user-centric approach to traffic flow management. First, an analytical Markov Decision Process is presented to derive structural insights on the drivers of user-centric operations. Second, this paper augments existing air traffic flow management (ATFM) models by explicitly balancing flight delay costs and passenger delays. A large-scale integer programming model is formulated to track the impact of flow management operations on passenger accommodations. An original rolling procedure decomposes the problem over time while ensuring global feasibility, and shown to enable the model's implementation in short computational times. Computational results in the US National Aviation System suggest that this modeling and computational framework can achieve large reductions in passenger delays at comparatively small increases in flight delay costs. Analytical and computational results highlight two major levers of user-centric operations: (i) delay allocation, which determines which flights to delay or prioritize to minimize delay costs, and (ii) delay introduction, which deliberate adds departure holds to avoid passenger misconnections.

Failures of critical systems at airports: Impact on aircraft operations and safety

Failures of critical systems at airports can significantly impact aircraft operations and cause substantial safety risks. This study develops a generalized method for measuring the impacts of unscheduled communication, navigation and surveillance systems outages on airport operational performance (i.e. throughput) and safety. The proposed method consists of two separate models – the aggregate and the disaggregate model. The aggregate model is a regression model that analyzes flight data, which are averaged per quarter hour and obtained from the Aviation System Performance Metrics database, and investigates the reductions in airport throughput due to an outage. The disaggregate model analyzes individual flights using the Performance Data Analysis and Reporting System database to investigate air traffic controllers’ terminal airspace traffic management procedures during an outage by examining the time-separation (i.e. headways) between consecutive arriving aircraft on each runway. More specifically, the model calculates the changes in the probability of a loss of separation and assesses the necessity of enacting a Traffic Management Initiative. The proposed method is tested on the case of two localizer outages at Los Angeles International Airport. The case-study results reveal that the throughput decreased after air traffic controllers enacted a Ground Delay Program during the first observed outage. The probability of a loss of separation doubled and the smaller aircraft were diverted to other airports. Further results indicate that arriving headways can be better described by a combination of two probability distributions, rather than a single distribution, in order to more accurately capture a complexity of air traffic controllers’ actions. The method can help aviation regulators: (1) make better decisions about the value of equipment and redundancies, (2) analyze operational and safety degradations caused by equipment failures, and (3) develop appropriate traffic management procedures and improve existing ones.

Data-driven risk assessment and multicriteria optimization of UAV operations

This paper introduces a novel data-driven risk metric and assessment method for UAVs operating in environments typically encountered in civilian applications. A truly “data-driven risk measure” is derived through a probabilistic formulation that not only accounts for the intrinsically stochastic nature of the considered environmental factors (such as weather and signal strength), but also incorporates extrinsic prediction uncertainties originating from the geographical sparsity of data collection sources. We present a data-driven modeling of the stochastic environmental factors using Gaussian process-based function approximations. Notably, the proposed mathematical definition of the risk metric is based on the probabilistic predictions of such a Gaussian process model and introduced through a path-integral formulation. The problem of minimizing operational risk for multiple UAVs in partially unknown environments is then defined in a multicriteria optimization framework to address the trade-off between the path-integral risk measure and classical path-efficiency (distance). We show that such approach can be embedded into current standard risk assessment methods which could be easily integrated into UAVs traffic management initiatives. We analyze the results through a number of simulations, including realistic scenarios.

Fast-time simulation modeling as a tool for identifying best practices in strategic TFM: Initial analysis

Fast-time simulation Traffic Flow Management (TFM) models can play a significant role in helping to identify best practices for weather impact scenarios, to apply these practices, and to review their efficacy. A model that can enforce Traffic Management Initiatives (TMIs) to provide sufficient real ism is highly desirable for these activities. The model must also be comprehensively weather-aware so as to faithfully reflect the effect of convective and non-convective weather on air traffic. We have leveraged the ability of the Dynamic Airspace Routing Tool (DART), a superfast-time simulation model, to objectively evaluate the potential benefits (or added impacts) of alternative TFM strategies in support of operationally relevant what-if testing possibilities. We describe a methodology enabled by these simulation capabilities to objectively, and with great detail, evaluate adverse-weather-related TMI utility and potential alternatives with the aim of building a library of ‘best practices’. This overall approach, developed for visual, operator-driven Strategic TFM analysis through comparison of baseline and alternative scenarios, can also be augmented by a parametric search, i.e. partial optimization, of feasible TMI solutions for still greater testing and automation support for impact management decision-making. We discuss how this simulation model has already been effectively applied to support alternative-response analyses for air traffic operations. The opportunities for Strategic TFM to evolve to a more consistent and efficient operation under this alternative support paradigm are discussed.

Unsupervised Prototype Reduction for Data Exploration and An Application to Air Traffic Management Initiatives

We discuss a new approach to unsupervised learning and data exploration that involves summarizing a large data set using a small set of ``representative'' elements. These representatives may be presented to a user in order to provide intuition regarding the distribution of observations. Alternatively, these representatives can be used as cases for more detailed analysis. We call the problem of selecting the representatives the Unsupervised Prototype Reduction problem. We discuss the KC-UPR method for this problem and compare it to other existing methods that may be applied to this problem. We propose a new type of distance measure that allows for more interpretable presentation of results from the KC-UPR method. We demonstrate how solutions from the Unsupervised Prototype Reduction problem may be used to provide decision support for the planning of air traffic management initiatives, and we produce computational results that compare the effectiveness of several methods in this application. We also provide an example of how the KC-UPR method can be used for data exploration, using data from air traffic management initiatives at Newark Liberty International Airport.

Including Linear Holding in Air Traffic Flow Management for Flexible Delay Handling

This paper introduces a strategy to include linear holding into air traffic flow management initiatives, together with the commonly used ground holding and airborne holding measures. In this way, flow management performance can be improved when handling delay assignment with uncertainty. First, a trajectory generation method is presented, aiming at computing, per flight, the maximum linear holding realizable using the same fuel as the original nominal flight. This information is assumed to be computed and shared by the different airlines and it is then used to build a network air traffic flow management model to optimally assign delays, in the scope of trajectory-based operations. Hence, the best distribution of delay is optimized at given positions along the flight trajectory (combining the three holding practices together) and taking into account the cost of delay, especially in the fuel consumption. The problem is formulated as a mixed integer linear program and solved with a commercial off-the-shelf solver. An illustrative example is given, showing that under the circumstance of capacity recovered ahead of schedule, including linear holding contributes to a notable delay reduction compared with the case where only ground and airborne holding apply.