Characterizing the Brazilian airspace structure and air traffic performance via trajectory data analytics

This paper presents a data-driven approach for multi-scale characterization of the Brazilian airspace structure and air traffic operational performance from aircraft tracking data recorded by surveillance systems. Unsupervised learning is performed with a flight trajectory clustering analysis to automatically identify spatial traffic patterns in both the terminal and the en route airspace for major origin-destination pairs of the Brazilian air transportation system. Based on the as-flown route structure learned, quantitative metrics are developed to describe the structural efficiency of the airspace and the operational efficiency of the traffic flows. For this, actual flight trajectories are projected onto reference nominal trajectories in space and time. The results allowed for cross-route comparisons of air traffic flow efficiency across multiple flight phases as well as for the identification of causal factors for trajectory deviations from nominal routes. An interactive data analytics tool is also created to output performance statistics and air traffic visualizations. With the provision of a systematic data-driven approach for characterizing actual air traffic operations, the analytics framework is envisioned to assist airspace design and performance monitoring processes and to provide the basis for developing predictive capabilities in support of traffic flow management.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

Embedding economies of scale concepts for hub network design

We explore the idea of endogenous hub location on a network. In contrast to much of the literature, we propose that hub networks may emerge naturally out of a set of assumptions and conditions borrowed from equilibrium traffic assignment. To this end, we focus on applying a nonlinear cost function that rewards economies of scale on all network links. A model is presented and implemented in a GIS environment using both a 100-node intercity matrix and several synthesized interaction matrices. We compare solutions for different assumptions about network costs, and visualize the results. We find that under discounted conditions, network flow is re-routed to take advantage of the cost savings for amalgamation and that several cities emerge as centers through which large amounts of flow pass. Larger cities such as Los Angeles, New York and Chicago serve gateway functions. We also find that smaller cities such as Oklahoma City, Pittsburgh, Indianapolis, and Knoxville serve major gateway functions because of their locational advantages. Our paper should be of interest to the planner of a surface transportation system, or those interested in nodal concepts such as gateways and transport geography. Results are discussed in light of hub and spoke networks and suggestions are made for future research.     

Algebraic connectivity maximization of an air transportation network: The flight routes’ addition/deletion problem

A common metric to measure the robustness of a network is its algebraic connectivity. This paper introduces the flight routes addition/deletion problem and compares three different methods to analyze and optimize the algebraic connectivity of the air transportation network. The Modified Greedy Perturbation algorithm (MGP) provides a local optimum in an efficient iterative manner. The Weighted Tabu Search (WTS) is developed for the flight routes addition/deletion problem to offer a better optimal solution with longer computation time. The relaxed semidefinite programming (SDP) is used to set a performance upper bound and then three rounding techniques are applied to obtain feasible solutions. The simulation results show the trade-off among the Modified Greedy Perturbation, Weighted Tabu Search and relaxed SDP, with which we can decide the appropriate algorithm to adopt for maximizing the algebraic connectivity of the air transportation networks of different sizes. Finally a real air transportation network of Virgin America is investigated.     

Accessibility and attractiveness of European airports: A simple small community perspective

This paper describes a simple method for comparing airports based on their accessibility to regional, continental or global air transport networks, using a variety of different measures of traffic. The method is applied to all Irish and UK airports for the years 2000, 2003 and 2005 and compares the performance of these airports in serving the UK/Ireland, Western European, Greater European, North American and global air transport markets. The method may be generalised to assess other regional groupings of airports in differing regional contexts, in situations where very limited demographic and socio-economic data are available.     

Modeling resilience of the ATC (Air Traffic Control) sectors

This paper develops a theoretical framework containing the methodology for assessing resilience of the ATC (Air Traffic Control) sectors affected by the impact of a given disruptive event. The resilience is considered as ability of these sectors to retain a certain level of the regular/nominal performance during the impact and fully recover relatively fast afterwards. The actually rear disruptive event is considered to be the large-scale failure of a component of the ATC facilities and equipment supporting safe, efficient, and effective air traffic. Under such conditions, different mitigating contingency measures are generally applied resulting in deteriorating the operational, economic, and environmental performance of the affected sectors while maintaining the required level of safety. This performance is represented by the indicators such as demand, capacity, traffic complexity, the ATC controller workload, aircraft/flight delays and their costs, and additional fuel consumption and related emissions of GHG (Green House Gases). The proposed methodology consists of the generic model of resilience, the analytical models for estimating the indictors of ATC sectors’ performance, and the analytical models of resilience based the indicators as figures-of-merit for assessing resilience. These models are based on the practice-close mitigating contingency measures applied to the ATC sectors affected by a given disruptive event. The possible application of the proposed methodology is also elaborated.     

基于可靠性计算的编组站车流预估方法研究

编组站阶段计划作为编组站作业计划的核心,对编组站高效完成运输生产任务具有重要作用。然而编组站阶段计划的编制通常受限于到达车流的时间不确定性和车流信息的不准确性,为提高自动编制阶段计划的准确性,研究一种依据车流实际确报信息对预报信息进行准确性评价、并且根据站内车流和带有准确性评价的到达车流进行车流自动推算的方法。在量化车流可靠性参数评估和列车平均运行时间评估的基础上构建模型,并提出相应的算法,随着系统运行过程中车流信息的不断积累和可靠性评价参数的动态更新,通过模拟调度人员对车流分配的思考过程实现到发车流的自动推算和分配。该方法可以有效提升车流推算的准确程度,提高自动编制的阶段计划的可用性。     

Discovering temporal and spatial patterns and characteristics of pavement distress condition data on major corridors in New Mexico

Roadway networks, as part of transportation infrastructure, play an indispensable role in regional economies and community development. The high-quality pavement serviceability of these networks is essential to ensure safe, cost-effective daily traffic operations. In-depth analyses of network-wide pavement surface condition data are necessary inputs for optimal pavement design and maintenance, traffic safety enhancement, and sustainable traffic infrastructure system development. This study aims to investigate various pavement distress condition performance measurements and their correlations to better understand temporal–spatial characteristics of roadway distress based on pavement distress condition data collected in New Mexico from 2006 to 2009. Eight major corridors across various urban and rural areas were selected for analyzing pavement surface-distress conditions and discovering their intrinsic characteristics and patterns across both temporal and spatial domains. The results show that there are not strong correlations among different distress measurements, implying the rationality of the current pavement performance measurement protocol used by the state transportation agencies. Regression models were established and GIS-based spatial analyses were performed to extract temporal and spatial patterns of Distress Rate (DR) data. The model results illustrate significant correlations of the DR data on the same route between two consecutive years, which can be partially characterized by a Markov process. GIS-based spatial investigations also show unique features of pavement condition deterioration attributed to diverse geometric characteristics and traffic conditions, such as vehicle compositions and volumes and urban and rural areas. The research findings are helpful to understand the characteristics of pavement distress conditions more clearly and to optimize traffic infrastructure design and maintenance.     

How did COVID-19 impact air transportation? A first peek through the lens of complex networks

The current outbreak of COVID-19 is an unprecedented event in air transportation. This is probably the first time that global aviation contributed to the planet-wide spread of a pandemic, with casualties in over two hundred countries. As of August 23rd, 2020, the number of infected cases has topped 23 million, reportedly relating to more than 800,000 deaths worldwide. However, there is also a second side of the pandemic: it has led to an unmatched singularity in the global air transportation system. In what could be considered a highly uncoordinated, almost chaotic manner, countries have closed their borders, and people are reluctant/unable to travel due to country-specific lock-down measures. Accordingly, aviation is one of the industries that has been suffering most due to the consequences of the pandemic outbreak, despite probably being one of its largest initial drivers. In this study, we investigate the impact of COVID-19 on global air transportation at different scales, ranging from worldwide airport networks where airports are nodes and links between airports exist when direct flights exist, to international country networks where countries are contracted as nodes, and to domestic airport networks for representative countries/regions. We focus on the spatial-temporal evolutionary dynamics of COVID-19 in air transportation networks. Our study provides a comprehensive empirical analysis on the impact of the COVID-19 pandemic on aviation from a complex system perspective using network science tools.     

基于现代物流理念的铁路货运组织改革的思考

铁路货运是物流体系的重要组成部分,也是亟待改进的部分.简要分析现代物流作用和发展趋势,结合现行铁路货运组织存在的问题,从信息系统、管理模式、设施设备、运营机制和人才机制几个方面,探讨基于现代物流理念的铁路货运组织改革,以利于提高铁路货运组织效率和效益.     

Study of the topology and robustness of airline route networks from the complex network approach: a survey and research agenda

Communication via air routes is an important issue in a world organized around a web city network. In this context, the robustness of network infrastructures, e.g. air transport networks, are a central issue in transport geography. Disruption of communication links by intentional causes (e.g., terrorist attack on an airport) or unintentional (e.g., weather inclemency) could be crucial for countries, regions and even the airlines affected themselves. Policymakers and the management of airlines and alliances should be able to reduce the effects of such interruptions in order to ensure good communication through air transport (i.e., maximize the robustness of their network at a reasonable cost). The literature review of the study of air transport route networks through an analysis of complex networks has highlighted a lack of contributions to the study of the topology and the robustness of such networks which contrasts, with advances undertaken for other transport networks or communication systems. This survey suggests areas in which research should be undertaken, based on the existing literature in other areas and from three different perspectives: global route networks, airline alliances, airlines and airports so as to help towards a better understanding of air traffic and, therefore, to be able to assess the potential damage of any airport being inoperative for a continent, country or airline.     

Policy issues and planning of UK regional airports

The principal motivation for this paper is to examine the policy issues for UK regional airports within the air transport system with particular reference to airport airline relationships. The geography of airline route networks is considered a key factor that determines location of the pressure for infrastructure development. Regional airports policy is considered in this context. The problems of the resultant concentration of air traffic has largely been ignored up until now and so this paper seeks highlight the issues this presents to policy makers. The paper reviews the changing regional airport ownership patterns and examines a range of implications. It then explores the policy implications for the future regulation and development of the air transport system. The authors contend that regulation and the planning system are the only two remaining policy levers for government to guide privately owned airlines, privately owned airports and commercialised airports towards national policy goals.     

Exploring multiple eco-routing guidance strategies in a commuting corridor

The introduction of eco-routing systems has been suggested as a promising strategy to reduce carbon dioxide emissions and criteria pollutants. The objective of this study is to scrutinize the impacts of an eco-routing guidance system on emissions through the use of a case study in a commuting corridor. This research aims at assessing the potential environmental benefits in terms of different pollutant emissions. Simultaneously, it addresses the extent of variations in system travel time (S_TT) that each eco-routing strategy implies. The methodology consists of three distinct phases. The first phase corresponds to the adjustment of a microsimulation platform of traffic and emissions with empirical data previously collected. Second, to volume-emission-functions (VEF), developed based on the integrated modeling structure. Final, to different scenarios of traffic flow optimization performed at the network level based on a simplified assignment procedure. The results show that if the traffic assignment is performed with the objective to minimize overall impacts, then the total system environmental damage costs can be reduced up to 9% with marginal oscillations in total S_TT. However, if drivers are advised based on their own emissions minimization, total system emissions may be higher than under the standard user equilibrium flow pattern. Specifically, environmentally friendly navigation algorithms focused on individual goals may tend to divert traffic to roads with less capacity affecting the performance of the remaining traffic. This case study brings new insights about the difficulties and potentials of implementing such systems.     

Increasing the resilience of air traffic networks using a network graph theory approach

Air traffic networks are essential to today’s global society. They are the fastest means of transporting physical goods and people and are a major contributor to the globalisation of the world’s economy. This increasing reliance requires these networks to have high resilience; however, previous events show that they can be susceptible to natural hazards. We assess two strategies to improve the resilience of air traffic networks and show an adaptive reconfiguration strategy is superior to a permanent re-routing solution. We find that, if traffic networks have fixed air routes, the geographical location of airports leaves them vulnerable to spatial hazard.     

A method to estimate air traffic controller mental workload based on traffic clearances

Workload estimation is a complex domain which has been investigated extensively over the years. Past estimation techniques have focused on measuring workload directly from the air traffic controllers (ATCOs) or inferring it from traffic factors. The limitations of these techniques are interfering into the ATCO job and not being able to capture the differences amongst individual ATCOs respectively. This paper presents a novel technique overcoming these limitations, able to accurately estimate the workload experienced by the ATCO based exclusively on the clearances provided to air traffic. The technique, which was calibrated for the EUROCONTROL Maastricht Upper Area Control (MUAC) Centre, thereby has the potential to more accurately estimate actual airspace capacity. It is independent of the level of system automation and therefore applicable not only with the current ATM system, but also in the anticipated future highly automated environments as well as during the transition period. The paper discusses potential applications such as real time monitoring of operational workload and post-operations identification of sector workload imbalances. Both can contribute towards enhancing the performance of the ATM system.     

Domestic air passenger traffic and economic growth in China: Evidence from heterogeneous panel models

This paper applies recent panel methodology to examine the short-run dynamics, the long-run equilibrium relationships and the Granger causal relationship between economic growth and domestic air passenger traffic. It is based on the quarterly panel data of 29 provinces in China from the period of 2006Q1 to 2012Q3. Tests for panel unit roots, cointegration in heterogeneous panels and panel causality are employed in a bi-variate panel vector error correction model (PVECM), which is estimated by the system generalized moment method (SYS-GMM). The results show evidence of a long-run equilibrium relationship between economic growth and domestic air passenger traffic. Specifically, 1% increase in the air passenger traffic is found to lead to an increase of 0.943% in real gross domestic product (GDP). A long-run and strong bi-directional Granger causal relationship is found between these two series. It is also found that there is a short-run uni-directional Granger causality running from the domestic air passenger traffic to the economic growth.     

The impact of progressive liberalization on the spatiality of airline networks: a measurement framework based on the assessment of hierarchical differentiation

There seems to be a renewed trade-off between processes of concentration and dispersal in airline networks. This development calls for a framework that allows for a systematic assessment of the overall evolution of the spatiality of airline networks. To this end, we develop an analytical framework based on a set of spatial interaction indices, which allows measuring the degree of hierarchical differentiation (i.e. dominance and connectivity) in a network. Moreover, a normalization of these indices through a systematic comparison with the corresponding values for a rank size distribution makes it possible to engage in meaningful longitudinal analyses and/or to compare the spatiality of networks with different numbers of nodes and/or links. Our framework is tested by applying it to data on air passenger flows within Europe, and its usefulness is discussed in the context of research on air traffic liberalization.     

A multi-level spatial-temporal model for freight movement: The case of manufactured goods flows on the U.S. highway networks

The spatial production, attraction, and movement of manufactured goods are vital to the economy of a region and country. The U.S. department of transportation also mandates to incorporate continuing and efficient freight movement and infrastructure into statewide and local long range planning. Studies on supply, demand, and transport of manufactured goods by firm, industry, mode, or commodity are vast in the logistics and supply chain literature. However, relatively sparse research is available on aggregated movement or freight on national, state, or local transportation networks. Better understanding and modeling freight movement on highway networks to facilitate local transportation, land use, economic development, and comprehensive planning is at the heart of freight research. Therefore, the major endeavors and novel contributions of this research include a conceptual framework proposed for freight movement research, a multi-level spatial-temporal freight model based upon the social optimum assignment for optimal “from”, “to”, “within”, and “through” freight flows of manufactured goods on the U.S. highway networks, and a set of performance measures designed to reveal states in terms of their competitive advantages in production, attraction, self-sufficiency, or cross-road. The freight flows were first visualized and highlighted by state at the U.S. level, then by county at the state level for Oklahoma, and finally by traffic analysis zone for the Tulsa metropolitan area. The spatial split of freight flows was accomplished through using freight, network, and demographic-economic databases at state, county, and zone scales.     

Real-time mobilization decisions for multi-priority emergency response resources and evacuation groups: Model formulation and solution

This paper presents a model formulation and solution for simultaneous mobilization destination, traffic assignment, and departure schedule for multi-priority groups (SMDTS-MPG) for real-time emergency response in no-notice disasters. The presented approach addresses the decision context in which multiple emergency responses and evacuation flow groups with different destinations and varying priorities coexist in the same traffic network, within which simultaneous mobilization strategies must consider this requirement. The proposed modeling technique and the cell transmission model (CTM)-based linear-programming model provides a mechanism to accomplish this goal in an intuitive and coherent manner. The proposed matrix formulation of the SMDTS-MPG model permits the rapid deployment of the model to large networks. Model formulation and numerical examples are presented in detail in this paper.     

Commuting and energy consumption: toward an equitable transportation policy

Like other major metropolitan areas, the urban complex that extends from Los Angeles to Orange County faces numerous transportation challenges. Daily traffic congestion, reduced productivity and loss of income, air pollution, environmental degradation and significant energy consumption are only a few outcomes of the millions of miles travelled every day on the region’s highways and streets. An important response to this significant urban challenge has been the desire for further expansion of an efficient public transportation network and increasing densities in particular areas within the larger metropolitan region. In this paper, we estimate the current energy consumption patterns in various communities, arguing that policy attempts to achieve higher density and better jobs-housing balance should fully consider the social geography of our metropolitan areas and their close relationship with energy consumption patterns.