The impact of airport capacity constraints on future growth in the US air transportation system

This paper simulates airline strategic decision making and its impact on passenger demand, flight delays and aircraft emissions. Passenger flows, aircraft operations, flight delays and aircraft emissions are simulated for 22 airports in the US, under three airport capacity scenarios. The simulation results indicate that most system-wide implications for operations and environmental impact seem to be manageable, but local impacts at congested hub airports may be significant. The response of the air transportation system to avoid airports with high delays could significantly impact passenger demand and air traffic for these and directly dependent airports. The simulations also suggest that frequency competition effects could maintain flight frequencies at high levels, preventing a significant shift toward larger aircraft, which would otherwise reduce the impact of the capacity constraints.     

A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for short-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative.     

Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior

The introduction of Bus Rapid Transit (BRT), typically involving the use of exclusive bus lanes and related bus priority measures, is increasingly advocated as a flexible and cost-effective way of improving the attractiveness of public transit in congested urban areas by reducing travel times and variability. These schemes typically involve the reallocation of road space for exclusive use by buses, presenting commuters with potentially competing incentives: buses on BRT routes can run faster and more efficiently than buses running in general traffic, potentially attracting commuters to public transit and reducing congestion through modal shift from cars. However, a secondary impact may also exist; remaining car users may be presented with less congested road space, improving their journey times and simultaneously acting as an incentive for some bus-users to revert to the car. To investigate the potential for these primary and secondary impacts, we develop a prototype agent-based model to investigate the nature of these interactions and how they play out into system-wide patterns of modal share and travel times. The model allows us to test the effects of multiple assumptions about the behaviors of individual agents as they respond to different incentives introduced by BRT policy changes, such as the implementation of exclusive bus lanes, increased bus frequency, pre-boarding ticket machines and express stops, separately and together. We find that, under our assumptions, these policies can result in significant improvements in terms of individual journey times, modal shift, and length of rush hour. We see that the addition of an exclusive bus lane results in significant improvements for both car users and bus riders. Informed with appropriate empirical data relating to the behavior of individual agents, the geography and the specific policy interventions, the model has the potential to aid policymakers in examining the effectiveness of different BRT schemes, applied to broader environments.     

Spatial structure and network behaviour of strategic airline groups: A comparison between Europe and the United States

The Gini-index has gained in legitimacy when measuring traffic distributions of air traffic as compared to other more established measures, such as Herfindahl's. In order to render the index more meaningful for policy-makers, a partial decomposition into strategic groups of airlines along distinct geopolitical scopes of air traffic is suggested. Air traffic across airports is considered an aggregate of complex networks that are subject to multiple constraints, such as geopolitics or technology. A multi-layered analytic approach accounts for network operators as economic agents that behave in strategic ways within these constraints. Our approach allows for comparing traffic distributions in Europe with that of the US and, in particular, introduces a normative component by isolating patterns in airlines’ strategies that are likely to induce different degrees of spatial concentration and balanced traffic distributions within these common markets.     

Determinants of daily fluctuations in air passenger volumes. The effect of events and holidays on Milan Malpensa airport

Air traffic flows show large seasonal variability, but arrivals and departures may also be significantly influenced by specific events which generate peaks, which generate peaks rising above baseline traffic. While seasonal variations of air flows are well studied in literature, the daily variations and their causes are seldom analysed and quantified. The paper aims at filling this gap by exploring and quantifying the effect of holidays and events (conferences, trade fairs, sport events) in terms of passenger daily fluctuations.We identified the elements affecting these variations and searched for correlations with daily demand fluctuations using an OLS econometric model applied to Milan Malpensa airport. The model allows one to reproduce the observed daily traffic, identifying the baseline component of traffic (depending on the calendar) and the additional effect ascribable to holidays and occasional events.Results show which types of events generate a visible traffic increase. The effect of some of them can be very significant indeed. The largest international design and fashion shows taking place in Milan generate up to more than 20% extra passenger traffic compared to the normal baseline traffic. In addition, the analysis showed that their effect is not limited to the event days, but impacted on the surrounding days as well. Holidays also influence the patterns of demand, creating additional traffic on certain days and more pronounced peaks, which also differ according to seasons.     

Comparing spatial concentration and assessing relative market structure in air traffic

Traffic distributions of air traffic and their concentration have been measured through Gini, an index that contrasts in many ways with other more established ones, such as Herfindahl's. This research is extended in the sense that it perceives spatial concentration in air transport as an aggregate of complex networks that are subject to multiple constraints, such as geopolitics or technology. We propose a multi-layered analytic approach where network operators are economic agents that behave in strategic ways. It allows for comparing air traffic between airports in Europe with that of the US and, in particular, introduces a normative component by isolating patterns in airlines' strategies that coincide with more or less welfare-oriented degrees of spatial concentration in light of the above constraints.     

The partially private UK system for air traffic control

This paper reviews the publicly stated motives for establishing the UK system for air traffic control as a public–private partnership in 2001, and the significance of this public–private partnership as an example for the reform of other systems. It examines the performance of the PPP up to the present time in terms of safety, delays due to air traffic control, efficiency and financial performance. It attempts to relate actual performance to what would have occurred under alternative arrangements. It concludes that there is no demonstrable change due to the public–private partnership other than the financial result. It argues that the financial expectations of advocates of the PPP have not been fulfilled.     

Characterizing the performance of queuing networks in terminal control systems

The terminal infrastructure is critical for the effective and efficient operation of air traffic flow. The increase in air traffic flow creates pressure on air transport infrastructures and networks. Therefore, decision-makers need to develop and utilize efficient evaluation and analysis tools based on the limited availability of resources. Inspired by the underlying structure of queues for air traffic flow, a Petri-net-based model is developed in this study to characterize the system performance. In this study, air transportation networks are analyzed using a series of performance indicators that are based on a discrete-event system model.     

Customer issues related to limited air connectivity

Air access can be crucial for regional economic development, especially when global industries are involved. However, air traffic generates negative impacts, such as noise and air pollution that can lead to politically motivated calls to reduce air traffic. This paper investigates customer issues and potential responses of frequent flyers to non-market-driven limitations on air transport. Based on a survey conducted at Zurich International Airport we show that frequent air travelers tend to be more senior and to work in industries that involve travel outside of the worker's country of origin. Consequently, they are more critical of potential restrictions on air travel.     

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.     

Approximation of on-line mental workload index in ATC simulated multitasks

To assess the effects of workload pressures, participants interacted with a modified version of air traffic control simulated tasks requiring different levels of cognitive resources. Changes in mental workload between the levels were evaluated multidimensionally using a subjective rating, performance in a secondary task, and other behavioural indices. Saccadic movements were measured using a video-based eye tracking system. The Wickens multiple resource model is used as a theoretical reference framework. Saccadic peak velocity decreases with increasing cognitive load, in agreement with subjective test scores and performance data. That saccadic peak velocity is sensitive to variations in mental workload during ecologically valid tasks is demonstrated.     

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.     

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.     

Air transport and tourism—Perspectives and challenges for destinations,airlines and governments

Air transport and tourism are interlinked. Tourism is a driving factor for and, in some cases, a stimulator of change in air transport; most notably the development of new business models such as charter airlines. On the other hand, air transport opened new destinations and tourism forms such as long-haul excursions. Here these interlinks are analyzed in a system model. For the strategic development of destinations, a clear airline policy and air access strategy seems to be necessary. For airlines an assessment and understanding of the business models of destinations is essential. By shaping the external regulative environment of air traffic, government may influence air traffic as well as tourism.     

Dynamic management of aircraft stand allocation

This paper aimed to present an original approach for solving the aircraft stand allocation (SA) problem dynamically when due to operational disturbances, the planned allocation cannot be accomplished. The proposed Multiple-criteria Dynamic Stand Allocation (MDSA) method uses fuzzy logic to support decision-making under uncertainty. The MDSA method provides effective solutions in a short time, necessary for traffic management in case of delays, emergency, and untypical cases. It considers partially conflicting points of view of different airport users (airport managers, air traffic controllers, airlines, handling agents, and passengers) and may significantly support managers on the SA problem. The approach proposed can also be used for creating an initial SA plan for a considerable number of aircraft.     

Airport traffic complexity and environment efficiency metrics for evaluation of ATM measures

For a given (current or planned) traffic demand, different air traffic management measures could result in different airport traffic complexity and efficiency. This paper presents the research on the relationship between airport traffic complexity and time and environmental efficiency for different air traffic control (ATC) tactics applied to the given or planned airport layout. Emphasis is placed on the evaluation of airport traffic complexity, aircraft fuel consumption, gas emissions and time efficiency for different ATC tactics and/or airport airfield layouts. For busy airports during peak hours, arrival queuing delays, taxi-in, taxi-out times and departure queuing delays increase, which induces additional unnecessary fuel consumption, gas emission and time inefficiency. In order to find a tool which could indicate potential delay generators, a measure of airport traffic complexity – called Dynamic Complexity is proposed. Experiments were performed for airports with different airfield layouts, for different traffic demands and ATC applied tactics using SIMMOD simulation model. Traffic situations were analyzed and delays were measured. The values of airport traffic complexity, fuel consumption and gas emissions were also determined. A comparative analysis of the results show: first, the proposed airport traffic complexity metric quite satisfactorily reflects the influence of traffic characteristics upon the environmental state of the system, and second, different ATM strategic and tactical measures (airport airfield infrastructure development and applied ATC tactics) could significantly reduce traffic complexity and increase time and environmental efficiency at the airport.     

Methods of air traffic management in the airport area including the environmental factor

In the modern approach to air traffic management (ATM), issues related to air quality and climate protection have led to the introduction of a growing number of new restrictions instead of having these issues be the objectives of actions taken. The aim of the study was to investigate whether it is possible to improve air quality while controlling pollution emission by introducing changes in the organization of air traffic control at the airport. For this purpose, mathematical models of emissions were created. These concern the movement of aircraft near the airport and emission of carbon dioxide and the spread of human health related substances emitted from a landing airplane's engine. The first case refers to the possibility of using different arrival procedures. The second case was to adapt the Pasquill formula by treating landing aircraft as a sequence of point-source emissions. As a result of applying the designed models and software tools, it was proven that both a change in the arrival trajectory and a change of the runway can contribute to a reduction of CO₂ emission into the atmosphere. The emission maps created for different aircraft approach profiles show the ability to control the local concentration of harmful emissions. The study clearly shows that the current approach of maximizing throughput is not beneficial to air quality. ATM services should consider the use of other variants of air traffic organization, particularly during periods of reduced traffic.     

Does bicycle network level of traffic stress (LTS) explain bicycle travel behavior? Mixed results from an Oregon case study

This paper draws on census data, mode choice, and regional household travel survey data to investigate the relationship between the varying levels of traffic stress (LTS) routes and bicycle travel behavior. Specifically, does bicycle level of traffic stress explain bicycle behavior enough to warrant its use given the limitations? Bicycle level of traffic stress is a new system of bicycle infrastructure classification for cities that cannot afford existing alternatives such as bicycle level of service which is more data-intensive. The LTS criteria lacks many of the input variables found significant in predicting both route choice and mode choice, including several that are required for its more expensive alternatives. The authors select the Salem-Keizer metropolitan area, a case study representative of the kinds of small and medium-sized communities that may prefer to use the LTS to evaluate cycling infrastructure given its lower input costs. The results validate LTS on travel behavior data from the Oregon Household Activity Survey (OHAS), but not mode choice data from the American Community Survey (ACS). These results suggest the LTS criteria may not be useful for cities looking to prioritize infrastructure improvements for specifically increasing commuter cycling. That said, results suggest the system provides a valid measure of a household's propensity to cycle. Further research on a broader cross-section of communities can clarify these mixed results.     

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.