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Maintenance and Rehabilitation (M&R) of airport pavement assets involves considerable financial resources. As such, even modest improvement in M&R activity planning could lead to non-trivial savings. The state-of-the-practice for planning M&R activities mostly relies on condition thresholds and engineering rules, while the state-of-the-art often requires untested assumptions and relies critically on complete knowledge about pavement deterioration. This study proposes a machine learning (ML)-based approach that integrates pavement condition prediction using supervised ML with M&R activity planning empowered by reinforcement learning (RL), for which the Q-learning method is adopted. Supervised ML involves minimum a priori assumptions while characterizing the relationship between pavement deterioration and its influencing factors, and can yield higher-accuracy prediction of future pavement conditions than traditional methods. Similarly, RL learns the optimal action-value functions in a model-free environment. The integrated ML approach is implemented and demonstrated using real-world data from Chicago O’Hare International Airport. The results show the effectiveness of the proposed approach which can lead to reduced M&R cost compared to practice.

This paper has the objective of mapping the KPIs (Key Performance Indicators) that can be used to measure the level of service and the passenger’ characteristics that mostly impact the boarding procedure in airports. For that, two approaches are made: (i) structured interview with air transport specialists about performance indicators - analyzed through the Analytical Hierarchy Process (AHP) to prioritize the indicators, and (ii) regression analysis of permanent passenger’s satisfaction survey to identify the passenger’s profile. The results show a methodology that can be replicated and its application in three airports from the Multi-Airport System of Terminal Sao Paulo that compete against each other: Guarulhos International Airport (SBGR), Congonhas Airport - Deputy Freitas Nobre (SBSP) and Viracopos - Campinas International Airport (SBKP). Although results might vary for different contexts, for this specific application, it was found that time spent in airport queues can be used as a perceived level of service predictor for boarding. The passenger’ characteristics which mostly impact the perceived levels of service quality were: (i) ground access mode, (ii) family income and (iii) age. Regarding the airport’s level of service, SBSP, which is an airport that is not yet part of the government concessions, have the worst satisfaction rates both in “overall satisfaction” and in “perceived level of service in boarding procedures” when compared to the other two airports of this study. Also, the methodology used to select and evaluate KPIs in this study can help airports around the world that compete against each other to measure the financial return over investments associated with efforts to increase service levels.

Airport retail has undergone significant change in the last decade. Today, many airports provide a broad selection of product varieties and an augmented shopping experience. In this study we focus on sequential purchasing which remains an unexplored topic in airport retail. Grounded in shopping momentum, we use real-world purchase data from 279,000 passenger boarding cards to test a conceptual model that examines relationships between first purchase expenditure, walking distances, time available for sequential shopping, crowding, payment type, product involvement, promotions and product type on sequential purchasing in three terminals at Dubai International Airport. Findings offer a significant theoretical contribution to the travel retail and sequential purchasing literature and have important practical implications for airport retail practitioners.

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. The framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

Polarized political debates can lead to infrastructure megaprojects in advanced stages of construction being left unfinished. Although this scenario is uncommon, there are relevant examples that negatively impact the economy and society. Despite their relevance, unfinished megaprojects are scarcely discussed in project studies. However, political events such as a referendum or government transitions jeopardize the continuation of the megaproject, leading to its termination. This paper focuses on unfinished infrastructure megaprojects and employs the escalation of commitment as a theoretical perspective. It addresses the following research question: How political polarization fosters escalation of commitment leading to unfinished infrastructure megaprojects? The paper employs a longitudinal case study, the USD 13 billion Mexico City New International Airport, recently terminated during the construction phase due to a political decision. The paper describes the interplay between the development of infrastructure megaprojects and political polarization. It shows how political narratives can portray a misleading escalation of commitment to justify project termination, even when it is not rational.

Large space buildings, such as airport terminal buildings, consume high energy due to the cooling demand; especially in hot arid climates; to achieve passenger thermal comfort. Evaluation of thermal environment and energy analysis inside airports is an important issue, in terms of reducing energy and controlling indoor environments. This work aims to analyze electrical energy consumption and the indoor environment of the terminal building at Assiut International Airport, Egypt as a case study. Indoor environmental conditions, and energy consumption with HVAC systems, were monitored inside five multiple functional zones within the terminal building (Departure hall, Public area, Duty-free hall, Gate, and Arrival hall) during summer (August 2017). The indoor environmental parameters monitored include: temperature, relative humidity, and illuminance. The results indicate the HVAC system consumed the largest portion (70%) of the overall building electricity demand, and the Energy Use Intensity (EUI) of ATZ terminal building is 300 kWh/m2/year. Also, indoor temperatures were found to be within the acceptable range of comfort, with a high energy consumption of 6,000,000 kWh for the year 2017. A significant reduction of monthly energy consumption was achieved based on increasing HVAC set temperature from 25 °C to 27 °C; a reduction equal to 24.5% of total energy consumption during the hot months. The results of this study help search for sustainable solutions for reducing energy consumption and improving passenger thermal comfort inside the terminal building (phase 2).

Highlights • Several severe and highly communicable infectious diseases emerged in the world over the past two decades. • No data is available on how to limit the spread of these emergent infectious diseases. • Our study, performed in Guadeloupe (French West Indies), assessed a new strategy in which each traveler would actively participate via a web-based self-administered questionnaire. • Our results suggest that this method would be of limited interest for health monitoring of international travelers; better performance might be achieved in the setting of an international health crisis., Objectives Emerging infectious diseases are a public health issue of international concern. Identifying methods to limit their expansion is essential. We assessed the feasibility of a screening strategy in which each traveler would actively participate in the screening process after an intercontinental flight by reporting their own health status via a web-based self-administered questionnaire. Patients and methods In 2015 and 2017, we invited passengers arriving at or departing from Pointe-à-Pitre international airport to answer an online health questionnaire during the four days following their arrival from or at Paris-Orly international airport. SPIRE 1 was intended for passengers arriving at Pointe-à-Pitre and was conceived as a pilot study. SPIRE 2 was an improved version of SPIRE 1 and consisted in three parts, which permitted to further assess the benefits of pre-flight request and email follow-up. Endpoints were the connection rates and response rates to online health questionnaire. Results For SPIRE 1, 4/1038 travelers (0.4%) completed the two steps of the online health questionnaire. In SPIRE 2, response rates ranged from 3/1059 (0.3%) to 19/819 (2.3%). Response rates were significantly better when passengers were approached before their flight. Conclusions The yield of an online health questionnaire was unexpectedly low.

The enormous economic impact of air transportation networks on a local, national, and international level has created an interest for further investments and increased the complexity of the global air transport network. The increased number of worldwide airport passengers and of the aircraft movements and also of the international airport cargo shipments is evident. Therefore, the functionality of this complex air transport network is very important and requires to be investigated and evaluated for identifying the airports that appear to be critical to this network. However, besides the economic benefits that air transport network is offering in local, national and international level through their services (shipments of goods and transport of passengers) play an ever-increasing role in safeguarding global health security. This paper investigated the global air transport network for identifying the airports that may constitute a public health event of international concern from infectious diseases (e.g. COVID-19). In detail, critical airports, in terms of centrality measures (e.g. Closeness, Degree, Betweenness, and Page Rank Centrality) were identified and addressed by pointing them to global authorities for suggesting and implementing routine prevention and control measures for possible future disease outbreaks.

Recent interest by human geographers and urban scholars in the spatialities of platform companies such as Uber and Airbnb has generated a wealth of contributions on platform urbanism – a concept that captures the urbanization of algorithmic business models and sharing economies. This article explores the interface between platform-mediated labour on Uber, the largest ride-sharing company globally, and the geography of the international airport. To comply with or overcome physical and regulatory barriers that airport precincts are subject to, as well as to manage e-hailing-related conflicts, Uber relies on a series of material adaptations which make its algorithmic platform function. These material adaptations range from sign boards, to attendants wearing high-visibility vests, to car lots specifically designed to embed AI-managed labour into the city. With two ethnographic vignettes set in Mumbai’s and Cape Town’s airports, this essay charts some of the ways in which urban infrastructures and algorithmic platforms transform each other. At their interfaces, labour is algorithmically managed and made invisible, but alternative transactions and different visibilities are also enacted. This paper thus makes a case for charting the singularities of platform urbanism and its labour.

Ridesharing services linked to mobile devices are increasingly playing a major role in human mobility in urban areas. With the recent release of Uber Movement data for certain large cities it is now possible to analyze urban mobility at finer spatial scales. In the present study, we used aggregate Uber trip data from 2016 to 2019 for the Miami metropolitan area. The peak hour of Uber trips and mean travel times were calculated using harmonic analysis. The total number of trips were expectedly higher on the weekdays compared to weekends for both sources and destinations. In addition, there was a distinct spatial clustering along major thoroughfares connecting the entire study area from north to south, and the two main airports of the study area, Miami International Airport and Fort Lauderdale Hollywood International Airport. The main thoroughfares along which the Uber trips were clustered also overlap with the two modes of mass transit, metro rail and Tri Rail. This is indicative of most riders using mass transit to the closest station of their destination, and then using Uber as a connection to the location of interest. The results of our study will be useful for urban planning and better understanding of human mobility across the metropolitan area.

The passenger volumes of cross-Strait air travel between Taiwan and China have grown rapidly, largely due to the gradual introduction of direct flights. This study captures passengers’ preferences for airlines and flight departure times in the Taiwan–China market. The model structures examined include the multinomial logit, nested logit, and mixed nested logit models. In particular, trigonometric functions are incorporated into choice modeling to identify passenger preferences regarding flight departure times. A stated preference survey was conducted at Taiwan Taoyuan International Airport. A mixed nested logit model, which captures substitution patterns among competitive alternatives as well as individual heterogeneity, outperforms the other models. The estimation results indicate high degrees of competition among airlines and flight-departure times, especially highly substitutable departure times within the same airline. The continuous departure-time specification, which uses a series of sine and cosine functions, adequately captured passenger preferences. From the qualitative aspect of airline services, a premium for service improvement is highest for cabin crew service, followed by onboard food and check-in service. Airlines can use these results to improve their service quality and adjust flight schedules.

Passenger activities in terminals can be understood better in the current era, when sensors are pervasive throughout airports. This study demonstrated the usefulness of combining multiple-source data to investigate passenger behavior in airports. Objective time-use and terminal activity participation data of 266 air passengers’ behavior at Taipei Songshan International Airport were collected from beacons, a self-developed mobile application, and questionnaires. The study first investigated recall errors generated from self-report questionnaires, an approach commonly used in previous studies with a retrospective design. The study then assessed the participation and duration of terminal activities conducted by passengers with standard, nested, and mixed multiple discrete-continuous extreme value models. The analysis results revealed that recall errors were associated with activity choice and duration and passenger characteristics. These errors were not random but systematic and could potentially lead to biased results in retrospective studies solely based on self-report questionnaires. The estimation results of extreme value models generally confirmed the expected association of terminal activity choice with passenger characteristics, such as more frequent retail store visits in female passengers than in male passengers. However, the associations of passenger characteristics with activity duration could be consistent with or opposite to those with activity choice; for example, frequent flyers tended to consume food and beverages (F&B) more frequently and spend a longer time in restaurants whereas passengers having a long free dwell time were less likely than those having a short free dwell time to consume F&B but the duration was relatively long on average. Managerial implications for airports and retailers and recommendations for future air passenger behavioral studies were provided.

This paper presents a simulation for Hold Baggage Security Screening (HBSS) system at Kuwait International Airport to develop a model to improve the current performance of the system. A well conducted investigation for HBSS system is performed to fully understand and analyze the throughput of the system and the factors affecting the baggage movement and queue duration. The HBSS model was adopted using Arena software which provides an “As is” model. First, the HBSS system flow chart was developed and further investigation related to the time and capacity of each of the HBSS levels was performed. Then, the required input data was collected and analyzed to choose the suitable decisions for each level. To ensure system verification, a warm up period of two hours was allowed, 30 replications were applied, and then the results were tested and compared to the real system. After running the “as is” model, the result analysis showed that the highest waiting time was in level 3 and the highest number of “system time-out” status baggage was in level 2. To tackle this situation, more SSO Operators were added to level 2, which reduced the total waiting time from 270 min to 78 min.

Although the number of terrorist casualties has recently decreased, terrorism still presents a threat to society. Within the last 20 years there has been a significant increase in the number of monographic studies about terrorist incidents’ impact on consumer behaviour. However, the present study is the first to analyse whether a terrorist threat specifically alters the consumer behaviour of direct victims. According to the literature, the impact of terrorism on many sectors is lower than originally expected and, if significant, only short term. We hypothesize that this influence is more significant and lasts longer with direct victims, who have undergone traumatic experience. For this purpose, we explore how victims of terrorism in Spain respond to hypothetical scenarios describing a terrorist plot against airplanes at an international airport. In addition, we check if there are significant differences between participants when confronted with situations in which they have to opt between taking or cancelling a planned trip by plane under the menace of a (hypothetical) terrorist attack.

Solar PV system in the airport environment is a relatively new application. Unlike land-based solar systems, the site selection for the airport-based PV power plant is a complicated process and lacks proper methodology. The objective of this work was to develop a general sitting procedure for an airport-based solar PV system and identify ideal sites for solar farms in Senai International airport, Malaysia. Feasible sites were selected with due consideration to airport and aviation compatibility constraints. Next, suitability of such selected sites is assessed based on environmental impact and proximity to electrical infrastructure. Using glare prediction software, the adherence to FAA’s solar interim policy is assessed. Eleven (11) sites which lie within the airport are chosen for the study. The duration of glare from sites 2, 3, 4, 6 were 1125, 4724, 3805, 1125 min receptively. As a result, design parameters are changed for these sites. The results of the study showed that the solar PV potential and theoretical energy generation from the selected sites of the airport were 12.50 MW and 16,745 MWh respectively. The knowledge on the suitability of sites and prior glare assessment increases the level of confidence to airport stakeholders and project developers.

Airports, pilots and geographical environment contribute a lot to stable and safe air traffic, especially during the climbing and descending phases. Accidents occur frequently in these two phases, even small errors can lead to serious consequences. Modern equipment has made great progress in monitoring the airspace. However, when encountering strong external interference or blind zones, potential risks will be raised. With the assistance of newly developed technologies and equipment, flight trajectories can be recorded at high frequency, which enhances capabilities of Air Traffic Management systems. A four-dimensional flight trajectory prediction model is put forward in this paper. Combined with sliding windows, Long Short-Term Memory network maintains the long-term features and manages to predict accurate trajectories. Massive data contributes a lot for forecasting, but cannot guarantee better decisions. Decision-making is closely related to threat assessment. Taking the geographical environment into consideration, the static threat is involved in the current work. The experimental scenario is set to be an aircraft takes off from Hong Kong International Airport, which is located close to mountains. Characteristics of terrains are introduced to our system by digital elevation model. The proposed cubic A* search algorithm provides reasonable path by considering the ultimate dynamic performance of an aircraft. Threat is assessed during the path planning process. Finally, the auxiliary decision support system is developed based on ArcGIS 10.0, to graphically provide the intuitive and quick assistance. Multiple sets of experimental results indicate that this system is able to provide timely decision support.

Flexibility has been proposed as a suitable alternative or a complement to the traditional airport development approach based on Master Plans. Flexibility increases airports’ resilience to unexpected or unforeseen events such as economic recessions, regulatory restrictions or technological developments, to name a few. Although the benefits of flexibility have been discussed, the debate has thus far been conducted at the conceptual level. Furthermore, research which refers to ways of measuring the benefits of flexibility is almost inexistent. This manuscript proposes a novel framework to support the incorporation of elements of flexibility in the airport development plan. The framework was primarily developed for cases of extension – i.e., when expansion is not an option. It is organised in three phases: 1) definition of the decision inputs, scenarios and flexible options, 2) ex-ante operational evaluation and, 3) ex-ante economic evaluation. The framework is meant to guide airport managers assessing the plausibility of alternatives in keeping or improving the efficiency of airport’s processes. An application of the framework is presented. The case study is the Terminal 2 of the Lisbon International Airport. The objective of the use of this case study is twofold: 1) to exemplify the applicability of the framework, and; 2) validate the framework together with the interviews that we conducted with airport practitioners. The results evidence that flexibility can increase the efficiency by reducing the waiting times, which will increase the passengers’ dwelling time. The costs are compensated by the increase in the non-aeronautical revenues.

Agent based simulation models have emerged as a significant aid for policy makers as they help them understand and predict the impact of various policies to make informed decisions. In this study, an agent based simulation model for Kempegowda International Airport, Bangalore is developed to understand and predict the impact of various proposed policies aimed at improving the airport system’s throughput. The model is calibrated using observed data on passengers at the airport. Input service time data at various stages of service is determined by fitting various parametric distributions around the observed data, where choice of best distribution is determined using various statistical measures (chi-squared goodness of fit test, and analysis of variance (ANOVA)). Further, a binary logit model is estimated to determine the influence of various individual characteristics on a passenger’s likelihood to engage in discretionary activities inside the terminal building. Six policy scenarios were tested to understand the impact of various policies on system throughput and the model’s sensitivity towards change in factors influencing a passenger’s likelihood to participate in discretionary activity was also analyzed. The results show considerable influence of the proposed policies on the system throughput. This can help in designing various policies to make the terminal operations more efficient.

Since 2015 3TI Progetti is involved in Doha Metro Project in Qatar, one of the world’s most important and significant construction infrastructure sites. The Project “Phase 1” includes the construction of three (Red, Gold, Green) out of the future four metro lines and 37 passenger stations. In 2017 3TI became “Lead Designer” on the Red Line South Underground that runs for about 40 km from Al Wakra to Lusail connecting Terminal 1 of Hamad International Airport with the Doha city centre. 3TI tasks include the management and coordination of the architectural and the engineering design process of five passenger underground stations and the exchange facilities. One of the main challenges faced during the development of the project was the use of BIM. Given the complexity of the project and the multiplicity of stakeholders involved, the use of BIM allowed the integrated control and update of the information, the timely resolution of clashes even during the progress of the site work, the identification and scheduling of the most effective maintenance strategies during the entire life cycle of the facilities. The BIM technology allowed the management and development of the project from its earliest design stages, through the creation of easily-replicable and automatically-updatable parametric elements, enhancing the operations of realization and construction in order to reduce complexities and to minimize time and costs. The leading role on the entire design process managing all the interdisciplinary BIM interfaces, acquired by 3TI within the JV led by Vinci Construction, was faced and carried out through the creation of a focused team, both in Rome headquarters and in Doha, where up to 90 engineers and architects took turns.

On-time performance is one of the most important Key Performance Indicators for airport operations since flight delays carry a huge economic cost in the industry. Many types of research were conducted to measure aviation delay and its economic cost as consequences (Kuhn, 2013) while environmental impact due to the flight delays was sparsely addressed. Flight delays result in excess fuel consumption and more emission. Existing literature addressed the aggregate emission of the aviation industry(IPCC, 2015). However, assessment of emission levels separately under different phases of flight has not received sufficient attention. Evaluating each phase of flight separately allows initiating more precise emission reduction strategies. In this research, a methodology was developed with available technologies to evaluate the flight delays and CO2 emission at the taxing phase for both arrivals and departures. Bandaranaike International Airport (BIA) was taken as a case for developing the methodology.

The objective of this study is to propose solutions for in-the-city airport countries where significant conflict between airport-access traffic and pass-by traffic (other urban traffic not accessing the airport) is observed. The study firstly investigates major transport problems of in-the-city airport area. It then reviews different solutions from international studies. The development of transport infrastructure system is proposed with different scenarios and the transport impact assessment is conducted by VISSIM microscopic simulation. The recommendation for city-airport connection is highlighted for the case of Ho Chi Minh city, Vietnam where Tan Son Nhat International Airport locates. The airport separated-access road is proposed for a short-term vision. The study reveals that improvement of transport infrastructure only could not resolve traffic congestion since the traffic demand is much beyond the traffic supply. A multimodal connection and traffic demand management are required for urban traffic in a longer term. The outcomes of this study might benefit urban planners and transport authorities in many cities where motorcycles are dominating.

Compared to the other public transport systems, air transport has received limited attention on the assessment of service quality. This paper aims to explore the factors employed to assess airport service quality, taking as case study the International Airport of Lamezia Terme, a peripheral airport placed in the south of Italy. Specifically, through a Principal Component Analysis latent factors influencing service quality are identified and the dimensionality of the phenomenon is reduced. After that, a Structural Equation Modelling approach is performed in order to define the relationships among the latent variables, and between the observed variables and the latent ones. For these purposes, we analyse a database derived from Customer Satisfaction Surveys conducted during 2015-2016 in the Lamezia Terme airport. The results confirm that overall airport service quality is significantly related to latent factors such as accessibility to the services, control operations and environment in the terminal.

We model the impact of weather and arrival demand on ground delay program (GDP) incidence. We use Support Vector Machine (SVM) to analyze how regional convective weather affects GDP incidence and find the impact depends on both distance and direction of convective activity from the airport. We then train and compare the performance of logistic regression (LR) and random forest (RF) in predicting GDP incidence using an SVM-generated regional weather variable, local weather and arrival demand. Generally, RF outperforms LR. Convective weather is the most important factor in predicting GDP incidence at Atlanta International Airport (ATL), while arrival demand has greater impact for the other airports studied. We also examined model transferability across different airports. Lastly, we build GDP duration prediction models to enable a user to assess how long a GDP is likely to continue, if it is in effect in a given hour.

The performance of airport asphalt pavement gradually deteriorates under aircraft load repetitions. In this study, a framework of airport asphalt pavement health monitoring system was developed to evaluate pavement performance under realistic random aircraft loads. The taxiway of the Beijing Capital International Airport was used as an example, and sensor layout was designed to obtain pavement mechanical responses and identify random aircraft loads. Multi-layered system theory was selected as the mechanical model to simulate airport asphalt pavement structure considering its computational efficiency. Mechanical model updating and distress evaluation based on airport asphalt pavement health monitoring system were discussed. First, in accordance with the theoretical relationship between mechanical responses and moduli derived from the mechanical model, the moduli of the pavement structure layers were back-calculated using measured mechanical responses. The Kalman filtering method was developed to identify modulus parameters that can be used to update the mechanical model. Second, full-field pavement mechanical responses under each load were reconstructed from the mechanical responses of a single point acquired by the sensors based on the updated mechanical model with knowledge of random aircraft loads and modulus parameters. Finally, real-time deterministic pavement distress calculation (cumulative damage from fatigue cracking and rutting) at any point were realized based on transfer functions between mechanical responses and performance. The strategy and process for short-term non-deterministic distress prediction were developed, and non-deterministic pavement distress at any point after multiple random aircraft loads can then be obtained. This study is beneficial for the understanding of airport pavement behavior and decision-making regarding pavement maintenance.

Flight delays have always been a source of dissatisfaction for passengers whose travel arrangements are interrupted. This study identifies passengers’ preferences for arrangement mechanisms in flight delay situations. A choice experiment is designed considering a set of attributes, including the duration of the delay, monetary compensation, free snacks and beverages, cabin class upgrade, free airport lounge access, and free hotel accommodation. An integrated model is developed using the discrete choice model, combined with the cumulative prospect theory to identify significant attributes. Questionnaires were collected from Taiwanese air travelers flying full-service carriers at the Taiwan Taoyuan International Airport. The results show that, if a major flight delay occurs and the airline would like passengers to maintain the booked flight, it needs to maintain a good long-term relationship with them by offering exclusive arrangements, such as free meals and, more importantly, free lounge access and hotel accommodation for long-distance travel. If the booked flight cannot resume operation on time, the airline could assist high-priority passengers take the next flight offered by the same airline or transfer their flight to another airline. Offering price discounts to typical passengers who are willing to fly business class and a free upgrade to a higher class for frequent flyers are effective schemes as well.

This study aims to examine the passengers’ awareness and perceptions of the emergency way-finding tools and procedure in the airport. In addition, the study also investigates the relationship of passengers’ perceptions of feeling safe at the airport with the awareness of way-finding tools and procedure. A survey of 500 respondents in Melbourne Airport in Australia and 450 respondents in Qingdao International Airport in China showed that the passengers’ awareness of emergency way-finding tools and procedure varied in the two airports. While most of the passengers at Melbourne Airport were unfamiliar with emergency way-finding tools, majority of the passengers at Qingdao airport were familiar with those tools. In both airports, passengers’ perceptions of feeling safe in the airport had positive relationship with situational awareness, with them being able to report the suspicious items or suspicious passengers’ behaviour in the airport. Interestingly, a negative association of feeling safe was observed with emergency wayfinding. Further, some differences in the understanding of emergency way finding tools and procedure between males and females as well as among the different age groups were observed. Our findings show that understanding the passengers’ perceptions and knowledge of emergency evacuation tools and procedure in different regions or culture is important to develop a robust emergency plans and procedure in the airport. In future, it is recommended to examine if the correlation between different culture and emergency wayfinding is additionally driven by the architectural layout and features of the airports.

Objective The medical support of military operations over a 5 million km2 area in the Sahel-Saharan strip has justified the use of a medical fixed wing aircraft. Two CASA CN 235 aircraft currently perform medical evacuation (medevac) from the point of injury to forward surgical structures and then to the international airport before strategic medevac to France. Methods A retrospective observational study including all flights performed from January 2013 to December 2017 by the medical CASA located in Mali. Results Three thousand three flight hours were achieved. Four hundred twenty-four medevacs were performed for 898 patients. Seventy-five percent were evacuated from forward surgical structures. Their initial categorization included 10% Alpha, 23% Bravo, and 67% Charlie. Mechanical ventilation was performed for 5%; 34.5% had common medical or surgical pathologies, 34.2% were combat casualties mostly by explosion, and 18.7% were nonbattle injuries. No difficulties related to the aeronautical environment were reported by the teams. Conclusion Tactical medevac with fixed wing aircraft has become a crucial link in the French medical evacuation chain in remote areas. Military emergency medical teams were able to provide in-flight intensive care before and after damage control surgery. Discussions are underway to consider possible doctrinal and logistical evolutions.

To estimate the pollution impact of seven sample airports in California on the surrounding schools, routine high-quality observations of 1-min average wind direction and speed at the airports were used to calculate the time each school within a 10-km radius was downwind of each airport. We demonstrate 0.87 correlation between pollution levels and downwind time at Los Angeles International Airport (LAX). Depending on the airport size, between 10 and 15 years of data was collected and downwind times calculated for the school hours throughout the school-months of August to June. By comparing this data with the 2015–2016 student enrollment size and demographics of the neighboring schools, we have demonstrated that an estimated 65,409 (8.7%) students spend 1–6 h per day at school throughout the academic year with exposure to airport-related air pollution. However, a higher proportion of economically disadvantaged students (10.6%) experience aforementioned levels of exposure. The level of exposure does not appear to be affected by the proposed 1-h delay in the school starting time that is intended to reduce student fatigue.

The paper presents performance evaluation for 830 kWp grid-connected PV power plant at Kamuzu International Airport in Malawi. The study used both measured and simulated data for the period September 2013 to August 2017. The PV array comprises 3540 solar modules of Heterojunction with Intrinsic Thin-layer (HIT) technology; each module was rated 235 Wp and tilted at 30°. The annual four-year average array, inverter and overall system efficiencies were 15.3%, 95.2% and 14.6%, respectively. An average annual capacity factor of 17.7% was obtained and the annual four-year average performance ratio was 79.5%. The results from this study are compared with those of representative PV power plants reported elsewhere.

Expected to launch globally in the forthcoming years, air taxis are brand-new aviation ridesharing services that will be provided by international logistic pioneers. This study is one of the first to estimate the demand for such a service and provide recommendations on the potential location of facilities to support this network operations. We adopt a two-phase approach: phase-1 estimates the demand for air taxi services by taking a subset of the regular taxi customers who are likely to avail this service, and phase-2 proposes a constrained clustering approach, with multimodal transportation-based warm start technique, to identify potential sites for locating infrastructures based on the estimated demand. We test the feasibility of the proposed approach using millions of real-life New York City taxi records. Results indicate that large facilities with a capacity of nearly 150 landings/hour have to be established in JFK International Airport and South Central Park, while smaller stops are required in World Trade Center, Washington Square and Allerton Ballfields. In addition, we evaluate the impact of the commuter’s “willingness to fly” rate, demand fulfillment rate and time-cost tradeoffs. Our analysis shows that the percentage of time savings and “willingness to fly” rate did not significantly impact location decisions and the number of sites, while it is necessary to conduct an intense market study to determine on-road travel limits. Insights provided in this study can act as a decision support tool for any logistics company that is interested in venturing into the air taxi market.

This paper investigates the factors affecting air passengers’ transfer airport choice in the market between Southeast Asia (SE Asia) and North America (NA). By estimating demand parameters, the study sheds lights on an airport operator’s business strategies, including identifying effective routes to compete for transfer passengers. Specifically, a discrete choice model is applied to estimate air travelers’ utility function from choosing different transfer airports in an origin-destination market. Our dataset consists of 78 city-pairs in the SE Asia–NA market. Instrument variables are used to control for potential endogeneity of airfare, while airport characteristics including minimum connection time (MCT), service quality, size, and detour degree are treated as exogenous. The main empirical findings are: (1) Transfer passengers’ airport choice is determined by an airport’s characteristics including MCT and service quality of flight connection, in addition to the traditional factors such as airfares and travel time. (2) Based on the estimated utility function parameters, we quantify transfer passengers’ willingness-to-pay for several characteristics of the transfer airport. (3) With our estimation results, a case study is conducted for Incheon International Airport to identify candidate routes that have the greatest potential for the airport to increase the number of transfer passengers.

This study analyzes high-dimensional data from Beijing International Airport and presents a practical flight delay prediction model. Following a multifactor approach, a novel deep belief network method is employed to mine the inner patterns of flight delays. Support vector regression is embedded in the developed model to perform a supervised fine-tuning within the presented predictive architecture. The proposed method has proven to be highly capable of handling the challenges of large datasets and capturing the key factors influencing delays. This ultimately enables connected airports to collectively alleviate delay propagation within their network through collaborative efforts (e.g., delay prediction synchronization).

In Beirut-Rafic Hariri International Airport (RHIA)-Lebanon, direct openings exist between the apron and the arrivals hall providing a pathway for contaminated air to enter the buildings. Also, airport employees spend no less than 12 h inside the airport's buildings and experience respiratory symptoms. Therefore, the influence of Beirut-RHIA's activities on the indoor air quality of the maintenance room in the control tower (impact on employees) was the focus of this study. This is the first study to assess the speciation of 46 Volatile organic compounds (VOCs) in indoor air of an airport workplace. Measurements, conducted during June and October 2014, has focused, in particular, on nitrogen dioxide (NO2) and Volatile organic compounds (VOCs), key ozone and PM precursors emitted from aircraft exhaust. Gas chromatographic techniques (GC-FID) were used to assess the concentrations of 46 VOCs, whereas colorimetric methods were adopted to determine NO2 concentrations. In general, the concentrations of measured VOCs did not present any risks except for acrolein, and NO2 concentrations inside the maintenance room were significantly below the World Health Organization (WHO) annual threshold limit (40 μg m−3). An interesting correlation was observed between aircraft number and the concentration of light aldehydes/ketones measured in the maintenance room. This is the first study dedicated to assess the speciation of a large number of VOCs in a work place environment in Lebanon.

Data on mass concentration of PM2.5 and its carbonaceous and water soluble inorganic chemical ions were compiled through sampling of PM2.5 at Indira Gandhi International Airport, Delhi during Dec. 16, 2015-Feb. 15, 2016 under Winter Fog Experiment (WIFEX) program of the Ministry of Earth Sciences (MoES) and analysing the samples. The data so generated were interpreted in terms of their variation on different time scales and apportioning their sources. It is found that mass concentration of PM2.5 averaged over the whole period of observation was 198.6 ± 55.6. The concentration of organic carbon (OC) and elemental carbon (EC) was 24.7 ± 9.4 and 11.7 ± 4.7 μg/m3 respectively with no any trend of increase or decrease over the observational period. SO42−, Cl− and NO3− dominated over other anions with their overall average concentration 34.0 ± 23.1, 32.7 ± 16.1 and 13.3 ± 8.7 μg/m3 respectively. Among cations, NH4+ showed highest concentration with an average value of 21.0 ± 10.6 μg/m3. Variation of daily average mass concentration of these parameters over the period of observation matched well with the variation of PM2.5 mass concentration indicating thereby to be the major contributors to the PM2.5 mass. NH4+ mostly occurred as NH4Cl and NH4NO3 and poorly as (NH4)2SO4 or NH4HSO4. H+ ion mostly occurred as H2SO4 and occasionally as HNO3. Carbonaceous aerosols and NO3− were mainly generated from fossil-fuel combustion. NH4+ and anthropogenic Cl− were mostly generated by biomass burning. The source of SO42− was found to be industries and thermal power plants. Continental Ca2+ and Mg2+ originated from thermal power plants and soil dust.

The Seattle-Tacoma International Airport is the hub of Alaska Airlines, the largest airline in the Pacific Northwest. We analyze the impact of Delta Air Lines’ expansion at this airport on the operational and financial performance of Alaska Airlines. The time series consists of quarterly airline and economic data for the period 1Q 2010 to 3Q 2016. We estimated the econometric model using the generalized method of moments based on the Newey-West covariance estimator for robust standard errors. While Alaska Airlines remains dominant at its hub airport its yield has moved closer to Delta Air Lines’, indicating that intense competition has eroded but not eliminated Alaska Airlines’ pricing power. Our results indicate that higher market concentration and advertising intensity tend to reduce profits, implying that the number of firms and the Herfindahl-Hirschman index may not be sufficient in evaluating market power because industries with few competitors may behave as competitive markets when firms engage in profit-reducing non-price competition. Moreover, unit cost has a greater impact on the Lerner index than yield, indicating that cost-efficient airlines have more pricing flexibility, which may increase profits, underscoring the importance of examining an airline’s cost structure in evaluating market power.

The demand for air transportation service in Korea has been increasing rapidly, while the airport operation system has become even further advanced and complex. Accordingly, concerns regarding negative ripple effects, such as damage caused by flight delays or cancellations due to irregular operations (IROPs) of airports, have been amplifying. The IROPs is being newly defined, and guidelines for establishing a response system are being proposed in the U.S. and the U.K. However, studies in relation to preemptive and predictive responses aimed at minimizing the negative impacts, such as to analyze ripple effects generated after an incident, have not been sufficiently conducted. Accordingly, this study was conducted to analyze the ripple effects of IROPs according to severity and duration time, and to thus suggest a methodology to enable predictive response. The situation of IROPs was simulated and analyzed using the tower log data of 2015 from Jeju International Airport (CJU), Gimpo International Airport (GMP), and Gimhae International Airport (PUS) in Korea. The five-level classification for IROPs was then suggested using the K-means algorithm. The methodology suggested was verified for applicability to actual airport operation through scenario analysis. It is expected to serve as a framework for establishing the quantitative standards for goal setting with which airport operators solve the situation of IROPs.

Airborne particles, specifically nanoparticles, are identified health hazards and a key research domain in air pollution and climate change. We performed a systematic airport study to characterize real-time size and number density distribution, chemical composition and morphology of the aerosols (∼10 nm–10 μm) using complementary cutting-edge and novel techniques, namely optical aerosol analyzers, triple quad ICP-MS/MS and high-resolution STEM imaging. The total number density of aerosols, predominantly composed of nanoparticles, reached a maximum of 2 × 106 cm−3 and is higher than reported values from any other international airport. We also provide evidence for a wide range of metal in aerosols, and emerging metals in nanoparticles (e.g., Zn and Ni). The geometric mean, median and 99th and 1st percentile values of observed nanoparticle number densities at the apron were 1.0 × 105, 9.0 × 104, 1.2 × 106 and 9.3 × 103 cm−3, respectively. These observations were statistically higher than corresponding measurements in downtown Montreal and at major highways during rush hour. This airport is thus a hotspot for nanoparticles containing emerging contaminants. The diurnal trends in concentrations exhibit peaks during flight and rush hours, showing correlations with pollutants such as CO. The HR-TEM-EDS provided evidence for nano-sized particles produced in combustion engines. Implications of our results for air pollution and health are discussed.

Centered on Incheon International Airport (ICN), South Korea's Incheon Aerotropolis is a global leader in aviation-linked commercial development and smart technology applications. This report details the evolution of ICN, its Air-City, and key outlying nodes such as Songdo International Business District (Songdo IBD), drawing lessons for the planning and development of other airports, airport cities, and aerotropolises around the world.

This study examines the passengers perceptions of Tom Jobim International Airport – RIOgaleao branding strategies and its impact to their experiences in light of the airport branding elements proposed by the literature. To achieve these aims it was conducted a semi-structured interview with the airport's branding manager and a questionnaire with the passengers (n = 92). The findings showed that the airport's new management company has been employing tourist branding strategies using a mix of elements, but it is possible that these actions have had the main objective of raising the airport to international service standards, whereby the operation of terminals by large companies is a trend. It was also concluded that some of the airport branding elements (i.e. logos and slogans) are well developed and represent a higher impact on passenger experience, while others (i.e. retail pricing strategies) require further attention and new approaches in order to improve passenger experience in the terminal.

Taxi is one important component of ground transportation at airports. Taxi drivers face two options after dropping off passengers: waiting to pick up passengers at the airport or ditching the airport and searching elsewhere for their next trips. Understanding how taxi drivers make decisions will not only benefit themselves but also benefit passengers. In addition, policies can be made to balance the taxi supply and demand at airports. In this research, a M / M / 1 / ∞ / N model was developed to investigate taxi drivers' decision-making mechanism based on queueing theory. By comparing the net income of these two options in the same time period, a better strategy could be selected. The model was then validated by a case study conducted at Yaoqiang International Airport in Jinan, China. With the model established, drivers' decisions in different time periods could be determined. By analyzing the model results, it was found that the results obtained by the model were consistent with the real-world situations. Furthermore, it was also discovered that taxi drivers’ decisions were strongly influenced by the number of flights taking off and landing in a certain period.

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.

Beijing Capital International Airport, as one of the busiest airports in the world, has three terminals and three runways. The middle runway lies between Terminal 2 and Terminal 3, and underground transportation is necessary to alleviate the congested surface traffic and reduce the impact of the surface traffic on the middle runway operations. As a result, twin neighboring tunnels, one each for passenger transportation and automobiles, passing underneath the operating middle runway were constructed. The section of the twin tunnels that was approximately 6 m under the operating runway was excavated using the sequential excavation method under the protection of a pipe-screen system. The elaborate installation of the pipe-screen system was performed using slurry pipe-jacking machines with an excavation diameter of 1020 mm. The annular gap between the excavation diameter and the outer diameter of the pipe was filled in time to reduce surface settlements. To conduct an information-oriented construction project, the real-time monitoring of the runway pavement surface with high-precision automatic total stations was employed during and after construction. With the monitored results, in-pipe grouting was performed when necessary to decrease the runway settlements. The undercrossing construction was completed as scheduled, and the final settlements of the runway were controlled within the predefined allowable settlement. The airport continued all operations without any interruptions. The success of crossing under the operational runway of the twin tunnels lies in the use of the large-diameter pipe screen, the well-controlled excavation of the twin tunnels, the installation of the high-precision and automatic runway movement measurement system and, most importantly, the employment of the in-pipe grouting system.

Airports are moving toward utilizing clean energy technologies along with the implementation of practices that reduce local emissions. This includes replacing fossil fuel-based with electricity-based operations. These changes would significantly impact the energy demand profile of airports. Electrically-conductive concrete (ECON) is currently a focus of heated pavement design for replacing conventional snow removal practices. ECON heated pavement systems (HPSs) use electricity to heat the pavement surface. Since experimental studies are resource intensive and ECON HPS performance depends on weather conditions, developing a field data-validated numerical model enables its long term energy performance evaluation. In this research, a finite element (FE) model is developed and experimentally-validated using two proposed model-updating methods for full-scale ECON HPS test slabs constructed at Des Moines International Airport (DSM) in Iowa. The model predicts energy demands and average surface temperatures within 2% and 13% respectively. The estimated power demand ranges from 325 to 460 W/m2 for different weather conditions. The results of this study provide a validated tool that can be used to evaluate the energy demand of ECON HPS. Studying the energy demand of ECON HPS opens the way for developing control strategies to optimize its energy use which will contribute to developing sustainable communities.

This study compares the performance of ensemble machine learning, ordinary least-squared and penalized algorithms to predict taxi-out time at two different periods of NextGen capability implementation. In the pre-sample, ordinary least-squared and ridge models performed better than other ensemble learning models. However, the gradient boosting model provided the lowest root mean squared errors in the post-sample. No algorithm fits data better in all cases. This paper recommends selecting the model that provides the best balance between bias and variance.

Beijing Capital International Airport (ZBAA) is the world's second busiest airport. In this study, the emissions of air pollutants from aircraft and other sources at ZBAA in 2015 were estimated using an improved method, which considered the mixing layer height calculated based on aircraft meteorological data relay (AMDAR), instead of using the height (915m) recommended by ICAO. The yearly emissions of NOx, CO, VOCs, SO2, and PM2.5 at the airport were 8.76×103, 4.43×103, 5.43×102, 4.80×102, and 1.49×102ton/year, respectively. The spatial-temporal distribution of aircraft emissions was systematically analyzed to understand the emission characteristics of aircraft. The results indicated that NOx was mainly emitted during the take-off and climb phases, accounting for 20.5% and 55.5% of the total emissions. CO and HC were mainly emitted during the taxi phase, accounting for 91.6% and 92.2% of the total emissions. Because the mixing layer height was high in summer, the emissions of aircraft were at the highest level throughout the year. Based on the detailed emissions inventory, four seasons simulation using WRF-CMAQ model was performed over the domain surrounding the airport. The results indicated that the contribution to PM2.5 was relatively high in winter; the average impact was about 1.15μg/m3 within a radius of 1km around the airport. Meanwhile, the near surroundings and southwest areas of the airport are the most sensitive to PM2.5.

The Hamad International Airport (HIA) in Doha, Qatar is the only international airport operating in the country. The airport was officially opened in 2014 to function as the engine of the country's economic growth. Due to the anticipated high passenger demand of this airport, especially to account for the 2022 FIFA World Cup and in support of Qatar National Vision (2030), it is important to analyze, model, and evaluate the current and future airport ground access and traveler's modal choice. As such, the aim of this study is to analyze the current HIA ground access mode choice characteristics and users, examine the importance of introducing the Doha Metro to airport mode choice, and identify the preference of air travelers for this new mode. A revealed- and stated preference face-to face interview survey was conducted at the airport to collect data related to trip and user characteristics. Quantitative and qualitative methods were used to analyze the data. Binary logit mode was used to the current access mode choice, and a multinomial logit model was used to model the future access mode choice after introducing the Doha Metro. The models results showed that current and future access mode choice is significantly affected by the trip and socioeconomic characteristics of the HIA users. This research was gratefully funded by the Office for Research at Qatar University . The authors acknowledge the valuable cooperation of the Qatar University Research Grant Number QUUG-CAS-DIA-15/16-24 . All views and results presented in the paper are solely the authors. Scopus

Rapid population growth in South-East Asia has placed immense pressure upon lowland regions both to supply food and employment and space for residential, commercial and infrastructure development. This pressure has led to sites on tropical peatland previously considered unsuitable for development to be revisited. One such site, the KLIA2 terminal and runway, Kuala Lumpur International Airport which opened in May 2014 at a cost of 3.6 billion MYR has been beset by well documented subsidence problems. Coverage of the tropics by the Sentinel-1 satellite constellation presents an opportunity to monitor the ongoing subsidence at KLIA 2, identify potential knowledge gaps and help inform more sustainable infrastructure development in tropical peatland regions. Our results show that the ISBAS InSAR method produces reproducible ground deformation maps which can clearly identify the patterns of deformation across both urban infrastructure and adjacent rural plantations and tropical peat swamp. This is particularly well defined around the terminal building at KLIA-2 where different ground preparation and foundation design have resulted in stable terminal buildings and subsidence of surrounding pavement. Deformation is greatest in the runway area where alternate bands of uplift and subsidence presumably accompany the greatest loads associated with landing aircraft. In contrast, areas where peat replacement was the primary form of ground preparation, ground motion is stable, however this comes at high economic and environmental cost.

Long taxiing times at large airports lead to fuel wastage and dissatisfied passengers. This paper investigates the 4D taxi scheduling problem in airports to minimize the taxiing time. We propose an iterative two-stage scheduling strategy. In the first stage, all aircrafts in a current schedule period are assigned initial 4D routes. In the second stage, landing aircrafts that are unavailable to fulfil their initially assigned routes are rescheduled using a shortest path algorithm based approach. In this paper, the simplified model used in most existing literature, that depicts a runway as having a single entrance and a single exit or even sets only one point to represent both of them has been discarded. Instead, we model the fact that a runway has multiple entrance and exit points and use an emerging concept—Runway Exit Availability (REA)—to measure the probability of clearing a runway from a specific exit during a specific time interval so that the taxiing scheduling model can be much higher approximation to the practical operation. An integer programming (IP) model factoring REA is proposed for assigning 4D taxiing routes in the first stage. The IP model covers most practical constraints faced in airport taxiing procedures, such as the rear-end/head-on conflict constraint, runway-crossing constraint, take-off/landing separation constraint, and taxi-out constraint. Besides, flight holding patterns at intersections are much more realistically modelled. Furthermore, to accelerate the solving process of the IP model, we have refined the formulation using several tricks. Simulation results by proposed scheduling approach for operations at the Beijing Capital International Airport (PEK) for an entire day demonstrate a surprising taxiing time saving against the empirical data and simulation results based on a strategy similar to what being used now days while showing an acceptable running time of our approach, which supports that our approach may help in real operation in the future.

A large number of international airport hubs are now under expansion to have two or more terminals and additional satellites or midfield concourses under the pressure of quick and massive growth of air traffic volume. As a result, the problems of passengers’ long walking between buildings, ground traffic congestion in the air side and low operational efficiency of transportation are becoming more and more prominent. Attributed to the merits of underground transport, the underground rapid transport system (URTS) has emerged as a sensible solution to provide a safe, accessible and pleasant way for passengers, reducing the congestions, flight delay and enhancing the operational efficiency as well. In order to better understand the merits of URTS and the way of its application in the airport hubs, this paper firstly explored the present status of URTS in airports around the world and the urgent need of URTS for the international airport hubs was addressed. Then, the lessons learnt from actual projects and recommendations were summarized and provided through a comprehensive case study of the planning and design of URTS in the expansion of Shanghai Pudong International Airport (SPIA). It is recommended that more importance should be attached to the URTS in the planning of airport expansion with a long-term view. The attempt, adopting the urban underground railway system into the URTS in the expansion of SPIA, could also provide a valuable reference for the development of URTS in other international airport hubs.