Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning

In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT.     

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.     

Fuel hedging and airline operating costs

Fuel hedging is a common risk management tool used in the airline industry. But past studies have not addressed the question of whether fuel hedging creates any benefit to airline operations. This study is the first work that empirically examines the role of fuel hedging in reducing airlines’ operating costs. Using US airlines data from 2000 through 2012, we find that, after accounting for the presence of cost inefficiency, fuel-hedging airlines had about 9–12% lower operating costs, but this effect is statistically insignificant. Irrespective of the hedging status, US airlines could reduce operating costs by an average of 12–14% per year without reducing output.     

The hidden cost of uncertainty for airspace users

This article highlights the importance of uncertainty in day-to-day operations, and the need to take it into account to properly assess the cost of delay for airspace users. It defines a cost of uncertainty and estimates it using real data. It provides some easily computable models based on the average and standard deviation of delay to estimate the cost of delay in general. The article shows that uncertainty is also important in the formulation of buffers for airlines and provides a simple model to estimate the optimal assignment, further using real data to compute the optimal value at different airports.     

Collaborative delay management towards demand-capacity balancing within User Driven Prioritisation Process

The concept of User Driven Prioritisation Process (UDPP) was introduced to give Airspace Users (AUs) more flexibility under demand-capacity imbalance. This paper presents two UDPP-DCB models, built on the UDPP principle, using the Selective Flight Protection (SFP) approach to minimize the total delay cost. AUs are enabled to adapt their operations in a more cost-efficient way in the presence of capacity constraints in airspace, optimizing their flights to keep the priorities track. Then, ATFM integrates the AUs’ decision to reassign the time slots and the preferred rerouting trajectories. Results suggest that the delay cost for AUs can be largely reduced through applying the UDPP-DCB models proposed in this paper, while allowing rerouting proves effective in reducing the system delay cost.     

Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit

This paper presents an in-depth comparison of route choice models for work and shop vehicle trips—with emphasis on the interactions between route attributes and individual characteristics—to better understand the route choice determinants that are assumed to vary by trip purpose. Insights into the route choice behavior involving two dominant vehicle trip purposes—work and shop trips—will help in the design of traffic facilities and implementation of measures to influence route choice in the desired direction.In this study, we show that the utility and scale parameters for separate models of work and shop trips differ by direct comparison using a sequential scaling estimation method and likelihood ratio tests, and highlight the differences in route choice behavior by considering the interaction of route attributes and individual characteristics using Path-Size Logit modeling. In the process, we used Potential Path Area - Gateway (PPAG) algorithm—that generates feasible route choice sets for route choice modeling from GPS trajectories of observed routes.The results show that, indeed, route choice behavior varies by trip, which suggests that drivers attach value to route choice determinants relative to trip purpose. The inclusion of interaction terms in model specifications further indicates that work route choice behavior tends to be restrictive compared to the nonrestrictive route choice for shop trips—a generalization consistent with the mandatory and discretionary nature of work and shop trips, respectively. Specifically, individual characteristics such as personal income, age, gender, tenure, household size, and access to public transit affect route choice behavior.     

Managing severe airspace flow programs: The Airlines’ side of the problem

This paper presents a heuristic-based approach for minimizing airlines’ schedule disruptions and operation costs associated with severe airspace flow programs. It considers primary decisions made by flight dispatchers such as flight slot substitution and rerouting outside the boundaries of the flow-constrained area. A two-stage heuristic is developed. In the first, a linear approximation of the problem is used to screen inefficient routing and slot substitution alternatives. The second stage examines possible solution improvements through trading flight assignments for every pair of conflicting routes. A genetic algorithm is developed and used to benchmark the performance of the two-stage heuristic. In the algorithm, flight route and slot allocation schemes are modeled as chromosomes. The fitness of these chromosomes measures the magnitude of schedule disruption and overall operating cost. A set of experiments that compare the performance of the two heuristics considering airspace flow programs with different levels of severity is presented.     

Model of passenger behavior choice under flight delay based on dynamic reference point

Flight delay has always been a concern of scholars, but in previous studies, there has been little discussion of passenger behavior choice after flight delay. The paper based on the prospect theory, taking the Beijing-Shanghai route as an example, constructs the passenger behavior choice model under flight delay, and in order to explore the decision-making behavior of passengers with different attributes under different delay scenarios. The results show that the reference point of passengers' behavioral decision-making is dynamic due to different delay scenarios and passengers' attributes. For the same reason, passengers have different behavioral choices when encountering flight delay. Through empirical analysis, it is found that the choice of passengers based on prospect theory is in line with the actual choice of passengers. The choice model proposed in the paper can effectively describe the passenger's choice behavior.     

Optimizing and assigning price levels for air traffic management

Pricing policies could encourage airline companies to modify departure times and routes of their flights in order to reach a route-slot allocation target that minimizes the en-route congestion. The problem of restricting the number of price levels and the assignment of one price level to each sector at each time period is studied using a simulation based on a Logit discrete choice model. In this model, an option is defined as the combination of a departure time and a route. For each flight, a utility is associated to each option and takes into account the flying cost, the cost of ground delay and the prices of crossed sectors. The optimization of the pricing policy considers average flows and minimizes the quadratic difference between desired and expected flows on each option. A heuristic algorithm that involves successive iterations of simulated annealing and gradient methods performs this optimization. The simulated annealing assigns a price level to each sector at each time period, and the gradient algorithm computes new values of price levels. The test of the method on constructed examples indicates that the use of only four price levels does not significantly deteriorate the performance of the system with respect to the use of independent prices.     

Route choice of bike share users: Leveraging GPS data to derive choice sets

To identify the determinants of bike share users' route choices, this research collects 132,397 hub-to-hub global positioning system (GPS) trajectories over a 12-month period between April 1, 2015 and March 31, 2016 from 750 bicycles provided by Hamilton Bike Share (HBS). A GIS-based map-matching algorithm is used to derive users' routes along the cycling network within Hamilton, Ontario and generate multiple attributes for each route, such as route distance, route directness, average distance between intersections, and the number of turns, intersections, and unique road segments. Concerning route choice analysis, the origin and destination pair should be the same for all routes within a choice set, thus HBS users' trips are grouped by origin-destination hub pairs. Since trips taken by different users between a hub pair can follow the same route, unique routes are extracted using a link signature extraction tool. Following this, a normalized Gini (G_n) coefficient is calculated for each hub pair to evaluate users' preferences among all the unique hub-to-hub route choices. A G_n closer to 0 indicates that routes between a hub pair are more evenly used, while a value closer to 1 implies a higher preference toward one dominant route. Three route choice models, a global model, a medium G_n model, and a high G_n model, are estimated using Path-Size Logit to determine how route choice is affected by the presence of dominant routes. These models suggest that HBS users are willing to detour for some attributes, such as bicycle facilities, but tend to avoid circuitous routes, turns, steep slopes, and roads with high traffic volume.     

April 2010 UK Airspace closure: Experience and impact on the UK’s air-travelling public and implications for future travel

Ash emitted from the Eyjafjallajökull Icelandic volcano resulted in the closure UK’s airspace in 2010. This research highlights passengers experience throughout the crisis and it is impacted on their wellbeing. Analysis of a survey shows that just 90% of respondents highlighted the failure of airline, travel agencies and/or government to provide timely and appropriate information. The airspace closure also caused adverse health impacts, with 79% of respondents highlighting this as a concern. Although passengers were greatly inconvenienced and had a negative experience, seventy-nine percent of respondents indicated that the crisis had little or no impact on their decision to fly in the future.     

Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London

Understanding and modelling route choice behaviour is central to predicting the formation and propagation of urban road congestion. Yet within conventional literature disagreements persist around the nature of route choice behaviour, and how it should be modelled. In this paper, both the shortest path and anchor-based perspectives on route choice behaviour are explored through an empirical analysis of nearly 700,000 minicab routes across London, United Kingdom. In the first set of analyses, the degree of similarity between observed routes and possible shortest paths is established. Shortest paths demonstrate poor performance in predicting both observed route choice and characteristics. The second stage of analysis explores the influence of specific urban features, named anchors, in route choice. These analyses show that certain features attract more route choices than would be expected were individuals choosing route based on cost minimisation alone. Instead, the results indicate that major urban features form the basis of route choice planning – being selected disproportionately more often, and causing asymmetry in route choice volumes by direction of travel. At a finer scale, decisions made at minor road features are furthermore demonstrated to influence routing patterns. The results indicate a need to revisit the basis of how routes are modelled, shifting from the shortest path perspective to a mechanism structured around urban features. In concluding, the main trends are synthesised within an initial framework for route choice modelling, and presents potential extensions of this research.     

A GPS data-based analysis of built environment influences on bicyclist route preferences

This study examines the effects of built environment features, including factors of land use and road network, on bicyclists' route preferences using the data from the city of Seattle. The bicycle routes are identified using a GPS dataset collected from a smartphone application named “CycleTracks.” The route choice set is generated using the labeling route approach, and the cost functions of route alternatives are based on principal component analyses. Then, two mixed logit models, focusing on random parameters and alternative-specific coefficients, respectively, are estimated to examine bicyclists' route choice. The major findings of this study are as follows: (1) the bicycle route choice involves the joint consideration of convenience, safety, and leisure; (2) most bicyclists prefer to cycle on shorter, flat, and well-planned bicycle facilities with slow road traffic; (3) some bicyclists prefer routes surrounded by mixed land use; (4) some bicyclists favor routes which are planted with street trees or installed with street lights; and (5) some bicyclists prefer routes along with city features. This analysis provides valuable insights into how well-planned land use and road network can facilitate efficient, safe, and enjoyable bicycling.     

Analyzing the effect of aviation infrastructure over aviation fuel consumption reduction

The purpose of this paper is to examine the effect of various aviation infrastructure dimensions over aviation fuel consumption reduction (AFCR) performance. This study is an effort that considers the role of dimensions collectively from all aspects belonging to aviation infrastructure. The relevance of dimensions and constructs for hypothesis development are based on extensive literature review. Exploratory factor analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed in the consecutive purification processes. Also, hypothesis testing was conducted using Structural Equation Modeling (SEM). A customized questionnaire was developed for collecting data from both kinds of respondents: Aviation industry experts and academic experts. Out of 382 approaches through mail survey, a total of 194 valid responses were collected. Analysis of the results shows the positive and significant impact of various factors such as: airport design, airspace management and air traffic control over the aviation fuel consumption reduction. Maximum importance is adjudged on air traffic control (ATC) and airspace route flexibility. The results of this study will encourage airlines and airport development authorities to increase their insight over aviation infrastructure, also to perform deeper analysis and find out precise values for real life implications.     

En-route weather and place valuations for different transport mode users

With the increasing societal interest in climate change, mostly separated strands of literature have investigated the travel-behavioural, thermo-sensational and environmental–psychological effects of weather on people in everyday life. This research conceptually and statistically integrates these fragmented insights. Drawing on unique Greater Rotterdam (The Netherlands) travel diary data enriched with hourly meteorological and spatial route attributes, we analyse how weather affects different transport mode users’ en-route place valuations in terms of liveliness, friendliness and aesthetics. Our main findings indicate that windy, cloudy, cold (<15 °C) or too hot (⩾25 °C) weather conditions negatively affect en-route place valuations, either directly or through lower thermal comfort. Active mode users generally value their route surroundings more positively than motorised transport modes, however they also appear more strongly affected by weather in their thermal experiences and place valuations. Policy makers are advised to expand climate-sensitive urban planning along active transport mode infrastructures.     

The influence of personality traits on airport public transport access mode choice: A hybrid latent class choice modeling approach

Many objective and subjective factors affect individual tendencies. Such subjective factors include personality traits, attitudes, identities, perceptions, and feelings. The choice of transportation mode is an individual tendency that is considered important in policy-making decisions, and it can affect sustainable transportation, particularly in metropolitan areas. The present study’s main aim is to determine the impact of the Big Five Personality Factors on individual preferences toward public transportation modes. We use data from a survey conducted in January and February of 2015 at Imam Khomeini International Airport (IKIA). Passengers were asked to indicate their preferred mode of transportation to access the IKIA and to respond to questions on the NEO Five-Factor Inventory. Based on 557 valid responses, hybrid discrete latent class modeling was conducted to understand the heterogeneity in the respondents’ individual preferences regarding the Big Five Personality Factors and their preferences toward public modes of transportation. The results indicated that individuals who display neuroticism were more likely than the others to be concerned about carrying heavy luggage and about inclement weather conditions when using public transportation. In addition, interesting results indicated that conscientious individuals likely paid more attention to travel cost than to any other attribute of public transportation, and the model of the conscientious latent personality trait was a better fit to the data. Finally, this paper examined the taste heterogeneity of each personality trait and the results indicate the usefulness of considering personality traits in mode choice models for richer insights toward sustainable transportation.     

Oil price dynamics and airline earnings predictability

This study examines the effect of oil price dynamics on quarterly earnings and their predictability with a sample of 30 airlines for 1994–2017. First, we document a significantly positive impact of demand-driven oil shocks on airline earnings, suggesting that the revenue effect from shifting air travel demands dominates the cost effect of aviation fuels. Regarding earnings predictability, we find evidence of deterioration in oil-volatile quarters as indicated by both the earnings variability based on common benchmarks and the quality of analyst forecasts issued early in the quarter. We further show that supply-driven oil shocks have a more detrimental impact on earning predictability than demand-driven oil shocks. Finally, we do not find supporting evidence for the possible moderating effect of hedging.     

Flight delay impact on airfare and flight frequency: A comprehensive assessment

This paper presents a comprehensive empirical analysis of flight delay impact on airfare and flight frequency in the US air transportation system. We model airfare and flight frequency as functions of cost and demand characteristics, competition effects, and flight delays at origin, destination, and intermediate hub airports. Estimation results confirm that airlines tend to pass delay cost onto passengers through higher fare, whereas delay has an upward effect on flight frequency. We find that proportionate airport delay reduction across the system can result in annual fare reduction in the order of billion dollars.     

Optimizing road network daily maintenance operations with stochastic service and travel times

This paper studies optimization methods for a routing problem encountered in daily maintenance operations of a road network. Stochastic service and travel times on road segments are considered. The problem is formulated as a variation of the capacitated arc routing problem (CARP). A chance-constrained programming model is firstly developed and solved by a branch-and-cut algorithm. A stochastic programming model with recourse is also proposed to take into account the recourse costs in case of route failure. The problem is solved by an adaptive large neighborhood search algorithm. The computational experiments demonstrate the effectiveness of the algorithm.     

基于满意度分析的高速铁路列车运行调整优化研究

为了研究高速铁路列车在恶劣天气影响下导致区段临时限速情景下的列车运行调整问题,文中选择总晚点时间和晚点列数2个指标,分别转化为列车延误满意度和正点率满意度,建立线性满意度、梯形满意度和传统多目标线性加权模型,通过改变列车到发时刻、运行径路、次序等实现高效、准点的列车运行调整。最后设计一个限速场景,运用软件求解,通过改变总晚点时间和晚点列数的权重,得到模型的帕累托解,结果表明满意度模型的帕累托解优于线性加权模型。此外,算例验证了梯形满意度线性加权模型从计算时间上优于传统多目标线性加权模型,提出的满意优化模型可以辅助调度员在发布限速命令后尽快获得满意的调度方案。