On load balancing strategies for baggage screening at airports

We study load balancing policies for an airport baggage handling system (BHS). The performance of the screening subsystem is important as it can potentially cause traffic congestion of the whole BHS during peak period. Currently, the round-robin (RR) and the first-available (FA) polices are implemented at airports. This paper presents a simulative approach to evaluate the impact of load balancing policies on the system performance. Using discrete-event simulations, airport practitioners can assess the effectiveness of the policies according to the actual layout of a particular passenger terminal. In addition to the RR and FA, a join-shortest-queue (JSQ) policy is introduced. This scheme can be applied jointly with the RR for daily operations. A case study is presented to illustrate the characteristics of this scheme. Simulation results demonstrate that the RR-JSQ can replace the existing RR-FA for a better balanced load distribution and improve the overall system performance.     

Offsite passenger service facilities: A viable option for airport access?

Offsite passenger service facilities transfer passengers from a common location to an airport and may provide additional services such as baggage handling and passenger check-in. Although they are conceptually promising, there is a lack of modern methods specifically tailored to forecast their demand. This paper reports on the development of two sequential models that forecast demand for an offsite facility. Models were calibrated based on data collected at six airports in California, Maryland, Massachusetts, and Virginia. The results showed that likely candidates for an offsite facility are airports that are not easily accessible, and departing air passengers with early morning flight departure times and high variability in ground travel times to the airport. A case study demonstrates the application of the models to Virginia’s Richmond International Airport.     

The baggage system at Denver: prospects and lessons

This article discusses the fundamental design difficulties of the fully automated baggage system originally planned for the New Denver Airport, and their implications for airport and airline management. Theory, industrial experience, and the reality at Denver emphasize the difficulty of achieving acceptable standards of performance when novel, complex systems are operating near capacity. United Airlines will thus make the Denver system ‘work’ by drastically reducing its complexity and performance. Automated baggage systems are risky. Airlines and airports considering their use should assess their design cautiously and far in advance, and install redundant, supplemental systems from the start.     

What is the busiest time at an airport? Clustering U.S. hub airports based on passenger movements

Accurate clustering of airports enables airport authorities and operators to precisely position themselves in the competitive market, facilitates airlines to efficiently allocate resources, and empowers the research community with credible data for new discoveries. Existing clustering studies largely use traffic volume, network connectivity, or operational efficiency to group airports into hierarchical clusters or parallel partitions. Developing a different perspective, this study focuses upon a key bottleneck of outbound passenger movements in airport terminals, the security checkpoints, and uses the Transportation Security Administration (TSA) Customer Throughput/Wait Times Reports to cluster hub airports of the United States. With the input data structured as univariate time-series, k-shape, a clustering algorithm that is robust to time axis distortion and computationally efficient, is selected to analyze the similarity of time-series using shape-based distance. The clustering results are validated by examining the raw and z-normalized data of selected airport clusters on six sampled dates. Analysis results indicate that k-shape is competent and efficient to process and cluster time-series data used for this specific research. This study offers a fresh perspective to cluster commercial airports using an infrequently employed dataset. The clustering results reveal how the geographical location, hub status in airlines' operational network, and destination type of an airport affect the movement of outbound passengers through terminals     

Flight and passenger efficiency-fairness trade-off for ATFM delay assignment

This paper studies trade-offs between efficiency (performance) and fairness (equity), when assigning ATFM delay pre-tactically (on-ground at origin airport) due to reduced airport capacity at destination. Delay is assigned as the result of the optimisation of a deterministic multi-objective problem considering flight and passenger perspectives when defining objectives of performance and fairness. Two optimisation cases are presented: one where objectives are based on flight metrics, and another one where they are based on passenger metrics. The paper defines and analyses efficiency-fairness trade-offs: the concepts of price of fairness for flights and passengers are defined as the percentage of efficiency loss due to the consideration in the optimisation of fairness; whereas the price of efficiency is considered as the fairness loss relative to the maximum value of the fairness metric, when considering flight or passenger delay in the optimisation. The optimisation model is based on the ground holding problem and uses various objective functions. For performance, total delay for flights (considering reactionary delay), and total delay for passengers (considering outbound connections) are defined. For fairness, the deviation of flight arrivals from a Ration By Schedule solution, and the deviation of delay experienced by passengers with respect to the one obtained in an RBS situation are used. An illustrative application on traffic at Paris Charles de Gaulle airport, a busy European hub airport, and including realistic values of traffic is modelled. A comprehensive trade-off analysis is presented. Results show, how in some cases, gains on one stakeholder can be achieved without implying any detriment on the other one. Passengers are more sensitive to the optimisation and hence, their consideration when assigning delay is recommended. Further research should explore how to combine flight and passenger indicators in the optimisation and consider how the lack of data availability could be mitigated.     

Assessing performance variability of ground handlers to comply with airport quality standards

The increasing competition in the air transport industry continuously pushes ground handlers to improve their performance. Recent European regulations prescribe that the managing body of large European airports (more than 5,000,000 passengers/year or 100,000 tonnes/year of freight) have to define quality standards of service level for ground handler operations. In case a handler fails to meet these minimum requirements, the airport has to report it these irregularities, potentially imposing a fine or even suspending, partly or fully, the handler’s services. As always recognized by the passengers, one of the most critical quality standards is the baggage handling waiting time. This paper defines a methodology to assess whether the handler fits the requirements of first baggage delivery for both the overall process and the specific segments (flight, conveyor belt, departing airport, day of the week, etc.). The methodology, analysing the performance by a two-dimensional perspective (structure and frequency), will help the decision makers in defining mitigating actions to take the performance under statistical control, respecting the requirements and preventing any drift.     

Ratio-based design hour determination for airport passenger terminal facilities

To avoid both over-design and under-sizing of airport passenger terminal facilities such as security checkpoints, the infrastructure is designed for a specifically determined design load. As such, the design load is considered for a short period of time, usually an hour of operation, during which peak, though not necessarily maximum, demand occurs. For strategic planning applications, future design loads can be determined by either fictitious flight schedules or ratio-based models which forecast the relationship between design load and annual demand. This study presents two ratio-based methods which allow the direct determination of design hour loads (DHL) for passenger terminal facilities. The unsaturated DHL model considers the relationship between observed passenger flows in the terminal and aggregated annual demand data. The saturated DHL model includes several operational constraints which limit the actual DHL, such as limitations in the runway system or the fleet mix operating at an airport. Both models are applied to two real-world airports, for which the DHL of the security checkpoint facilities is estimated from large datasets covering multiple years. Results are significant at the 5 % level and suggest that the proposed ratio-based methods are appropriate for airport strategic planning applications.     

Route optimization for energy efficient airport shuttle operations – A case study from Dallas Fort worth International Airport

The objective of this research is to reduce energy consumption from intra airport shuttle operations by optimizing routes and schedules, without compromising on passenger travel experience. To achieve this objective, we propose an optimization model that generates optimal airport shuttle routes for a given set of constraints and a discrete-event simulator that evaluates the optimal shuttle routes in a stochastic environment to understand the tradeoffs between the amount of time passengers wait for shuttles, and shuttle energy consumption. The proposed optimization model and stochastic simulation are tested using shuttle route data provided by the Dallas Fort Worth International Airport. Results indicate that optimized routes can lead to a 20% energy reduction in shuttle operations with a modest 2-min increase in average shuttle wait times. The optimization model and simulator presented here are designed to be generalizable and can be adapted to optimize shuttle operations at any major airport.     

Queue behavioural patterns for passengers at airport terminals: A machine learning approach

Passengers go through different handling processes inside airport terminal buildings. The quality of these processes is usually measured by the time passengers require and by the level of comfort experienced by them. We present an analysis of behavioural patterns in queues at check-in desks and security controls, which are two of the most critical processes regarding passenger service. The passengers' flow is simulated to obtain queue lengths at one busy European airport between 2014 and 2016, supported by real flight data. Simulation is designed as a store-and forward cell-based system, whose parameters have been tuned and validated with real data from observations and empirical capacity and demand studies within the airport. Random Forest algorithms are then implemented to develop different models for each parameter prediction, after a data analysis stage based on statistical and visualization methods. Feature analysis techniques between dependent variables and the target outputs (queue lengths) determine which are the fundamental elements to explain queue behaviour and to predict target variables. We provide a method to forecast behavioural patterns at check-in desks and security controls, to help airport operators to implement adequate response policies. Queue behavioural patterns are captured by Machine Learning models, which can be used to offer improved passenger services (such as real-time predictions for expected waiting time at queues), or can be considered in a dynamic approach for terminal services design (as the entire progress of terminal handling depends on the stochastic behaviour of passengers). This could be a key tool for managing passengers demand and optimise the infrastructure's capacity through resource allocation.     

Analysing Chinese citizens' intentions of outbound travel: a machine learning approach

Due to the tremendous expansion of Chinese outbound travel market, it is of great significance to identify the characteristics of potential Chinese outbound tourists so as to develop targeted marketing strategies. As both extrinsic and intrinsic characteristics of individuals are important in influencing their decision-making process, this study explores potential Chinese outbound tourists from these two aspects. Demographic and psychographic factors, as the proxy of extrinsic and intrinsic characteristics, respectively, are incorporated into this study to construct the relationship between tourists' characteristics and their intentions of outbound travel. An advanced machine learning approach, called twice-learning, is employed for modelling in this study. As an intelligent data analysis tool, this method is able to construct models that can provide insight into the ground-truth relationships hidden beneath the data in an essentially comprehensible way, without being limited by the typical assumptions held by the traditional data analysis methods. By applying this method, the important personal factors that influence Chinese citizens' intentions of outbound travel are detected, and the typical groups of potential Chinese outbound tourists are characterised. The findings would be beneficial for destination marketers to develop marketing strategies on positioning and advertising which are tailored to potential Chinese outbound travel market.     

铁路行包快运节点等级划分及布局方法研究

铁路行包快运节点的等级划分是其布局优化的基础，针对目前节点等级划分大多依靠经验而不够客观的问题，采用重要度计算和聚类分析的方法，对全国行包快运节点的等级进行划分，并在此基础上给出节点布局的方法和建议。     

Evaluating the strategic design parameters of airports in Thailand to meet service expectations of Low-Cost Airlines using the Fuzzy-based QFD method

The proliferation of Low-Cost Airlines (LCA) has phenomenally increased across the globe bringing a paradigm shift in the business model of airports specifically integrating the requirements of LCA. The current study attempts to evaluate the strategic design parameters of the airport integrating the requirements of LCA. The Fuzzy based Quality Function Deployment (QFD) approach has been utilized to conduct a House of Quality analysis for the integration of the voice of LCA in the design characteristics of the airport. The findings of the study identify evaluated design parameters of the airport for integrating the LCA requirement. It has been observed that the studied airport has been fulfilling the LCA requirements. However, few minor improvements are expected especially with regard to airside facilities, aeronautical tariff and other criteria. The study demonstrates and signifies that the Fuzzy based QFD method is a promising and pragmatic decision-making tool for customer-oriented airport strategic planning.     

British airways’ move to Terminal 5 at London Heathrow airport: A statistical analysis of transfer baggage performance

This paper investigates transfer baggage performance when British Airways’ occupancy of Terminal 5 at London Heathrow Airport took place. Operational data on transfer baggage performance are collated from BA performance scorecards and the Gini coefficient is used as a measure of consolidation of flight operations within a single terminal and in the investigation of correlation of consolidated flights in Terminal 5 with transfer baggage performance variation. The relationship between consolidation of operations in the terminal and improving transfer baggage performance is found to be significant. In addition, there is evidence of significant changes in transfer baggage performance on switch phases of flights as they were moved to Terminal 5 in steps. The exclusive use of a terminal gives improved performance.     

An investigation into traveler preferences and acceptance levels of airline ancillary revenues

This study determines traveler preferences and acceptability levels for a range of airline ancillary products and services by employing an on-line passenger survey to examine booking preferences as well as attitudes toward a selection of air and non-air travel components sold by the airlines. The survey results are combined with expert opinions collated from a recent international conference, along with secondary data, to generate an acceptance ranking which can be used by a range of airlines to formulate their ancillary revenue strategies. It is found that airport car parking and checked baggage charges proved to be the most accepted commission based and unbundled products for airlines to sell respectively. Despite the recent focus and successes in ancillary revenues, however, it can also observed that none of the ancillary products and services examined in this study achieved a high take up rating suggesting that airlines can do much more to convince travelers of the benefit and value in airlines selling non-core products and services to them.     

Investigating travelers’ multi-impulse buying behavior in airport duty-free shopping for Chinese traveler: Intrinsic and extrinsic motivations

Airport duty-free shopping is becoming the most important source of revenue and profit. As airports provide a unique retailing environment that differs from the day-to-day distribution channels, observation of travelers' shopping behavior in airports shows that it is different from that in traditional retail store environments. Duty-free retailers at airports must recognize this specific shopping behavior to maximize their sales performance. We examine the relationship between two different types of impulse buying behaviors: cognitive and affective. We also study travelers' satisfaction/loyalty in the context of airport duty-free shopping using two moderators of this relationship: intrinsic and extrinsic motivations. Data were collected from outbound Chinese travelers, who represent the largest proportion of travelers worldwide, to identify implications for airport duty-free retailers. This study found a positive relationship between affective impulse buying behavior and travelers' satisfaction, an aspect that also generates loyalty in the context of airport duty-free shopping. Furthermore, travelers' extrinsic motivations moderate the negative relationship between affective impulse buying behavior and travelers' satisfaction. This study provides insights that airport duty-free retailers can utilize to motivate travelers’ consumption and enhance their satisfaction in duty-free shopping, thereby increasing consumer loyalty.     

Airline delay management problem with airport capacity constraints and priority decisions

This paper deals with the Airline Delay Management Problem (ADMP), which can be described as the task of dealing with daily airline operational delays and deciding whether to delay subsequent flights at a hub airport or to have them departing on time. An innovative integer linear programming approach is presented to the capacitated case of the ADMP and airport limitations in terms of bay availability, taxiway capacity and runway separation are incorporated to represent capacity constraints. Fuel cost, passenger compensation, and passenger inconvenience costs are included in the objective function. The decision variables include the re-timing of flight departures and arrivals, the use of the airport capacity over time and the rebooking of passengers in case of missed connections. To guarantee the linearity of the optimization model and fast computational times, a receding horizon modeling framework is adopted. The approach is applied to a case study using real operational and passenger data from an international hub-and-spoke carrier. The case study shows the capability of the linear model to deal with a complete day of operations within a few minutes. The results suggest that the proposed approach can lead to cost reductions of almost 30% during recovery, when compared with the solution from the airline. In addition, a sensitivity analysis is provided to investigate the impact of not including passenger inconvenience costs and of reducing runway capacity.     

US airport financial reform and its implications for airport efficiency: An exploratory investigation

This study investigates the effect on airport productive efficiency of two major funding sources used by US airports, namely the Airport Improvement Program (AIP) grants and the Passenger Facility Charges (PFC). A two-stage Data Envelopment Analysis (DEA) modeling approach is employed for this purpose. In the first stage, we estimate airport productive efficiency using a variable returns-to-scale DEA model with both desirable and undesirable outputs. In the second stage, random effects regression models are estimated with airport efficiency scores from the first stage as the dependent variable and PFC and a proxy for AIP grants as two of the explanatory variables. By applying the two-stage DEA model to 42 primary US airports, it is found that PFC use has a positive impact on airport productive efficiency, whereas the impact of AIP grants is negative. Multiple counterfactual scenarios are examined by altering the mix of the two types of funding sources. The results show that simultaneously raising the PFC ceiling and decreasing AIP grants could lead to greater airport productive efficiency. The US federal aviation authority would also benefit from realizing these scenarios, especially given the budgetary constraints it faces.     

Air traffic forecast and its impact on runway capacity. A System Dynamics approach

The uninterrupted growth of air traffic in Colombia has been reinforced since the 1990s by a public policy of liberalization of airspace, and by redirecting public and private investment towards the modernization and updating of airport infrastructures, giving in concession the busiest airports in the country, 19 to date. In the commercial air sector, and in this same period, the flag airline was privatized and new air operators, including low cost airlines (LCC), entered the market. Since 2012, air fares are completely deregulated. Consequently, in the last two and a half decades, passenger transport in Colombia grew by 863%. This important growth rate has been driven and led by the country's main airport, the Bogotá-El Dorado International Airport (BOG), in the capital of Colombia. But some technical reports consider the airport will reach its maximum capacity in the short term mainly because of its inability to expand its airfield (runway system). Due to this circumstance, the aeronautical public authority of Colombia gave course to the planning, design and construction of a new airport (complementary) on the outskirts of the city of Bogotá, which will enter the design phase in 2026/2027. Therefore, it is considered suitable the development of a traffic forecast for BOG in the medium term and to evaluate the impact of future demand on the runway capacity of the airport. Then, and due to the complexity of the air transport forecast, the use of System Dynamics (SD), is considered to be appropriate as an analysis approach. System Dynamics is based on feedback control theory and it is equipped with mathematical computer simulation models, which uses linear and non-linear differential equations. The results suggests a need to expand the airport case study (runway system) after mid-2019, where the current capacity utilization factor is around 100% and two to three runways will be required for the normal operation; after October 2022 the number runways required is set to three until the last period simulated (2023).     

Processing passengers efficiently: An analysis of airport processing times for international passengers

With immense and growing pressure on stakeholders in international airport terminals to process passengers faster than previously, there is a great benefit to understanding which factors affect passenger processing times and in which situations. In addition, storing and analysing the collected data in batch is itself a difficult and time consuming task that could be made much simpler with sequential analysis. We aim to present a method for airport managers to discover which variables are important to understanding passenger processing times and identifying problematic passenger profiles without the need for high computational capacity and full historical datasets.In this paper we introduce Bayesian hierarchical models as a method of sequentially processing data, reducing computation time and obviating storage of large amounts of raw data. We use a range of exploratory models to identify which variables are important to predicting passenger processing time using a dataset from a day of operations at an international airport terminal, then compare a range of regression models. A Bayesian hierarchical regression model based on the model of best fit discovered through exploration is then applied to two subsets of data. We demonstrate that sequential updating based on daily data achieves similar results to batch processing based on full historical datasets and can therefore be used as an alternative in appropriate circumstances. Using the presented models, we find that the airline operating a flight is the most important variable to determining passenger processing time, followed by each passenger's age, sex and nationality. We demonstrate that in our dataset, the passenger profiles correlated with higher mean processing times overall were not the same as those passengers most problematic for meeting processing time targets.