A micro-simulation of airport passengers with advanced traits

An approach for modeling passenger flows in airport terminals by a set of devised advanced traits of passengers is proposed. Advanced traits take into account a passenger’s cognitive preferences which would be the underlying motivations of route-choice decisions. Basic traits are the status of passengers such as travel class. Although the activities of passengers are normally regarded as stochastic and sometimes unpredictable, we advise that real scenarios of passenger flows are basically feasible to be compared with virtual simulations in terms of tactical route-choice decision-making by individual personals. Inside airport terminals, passengers are goal-directed and not only use standard processing check points but also behave discretionary activities during the course. In this paper, we integrated discretionary activities in the study to fulfill full-range of passenger flows. In the model passengers are built as intelligent agents who possess a bunch of initial basic traits and then can be categorized into ten distinguish groups in terms of route-choice preferences by inferring the results of advanced traits. An experiment is executed to demonstrate the capability to facilitate predicting passenger flows.

Passengers in the airport : artefacts and activities

This study addresses the ordinary activities of passengers in airports. Using observational techniques we investigated how passenger activities are mediated by artefacts, in this the bags that people carry. The relationship between passengers and their bags is shown to be complex and contingent on many factors. We report on our early research in the airport and document an emerging taxonomy of passenger activity. The significance of this research is in the contribution made to an understanding of passenger activities which could contribute to the design of future technologies for passenger facilitation and to airport terminal design.

A new model for airport passenger segmentation

Recent changes in the aviation industry and in the expectations of travellers have begun to alter the way we approach our understanding, and thus the segmentation, of airport passengers. The key to successful segmentation of any population lies in the selection of the criteria on which the partitions are based. Increasingly, the basic criteria used to segment passengers (purpose of trip and frequency of travel) no longer provide adequate insights into the passenger experience. In this paper, we propose a new model for passenger segmentation based on the passenger core value, time. The results are based on qualitative research conducted in-situ at Brisbane International Terminal during 2012-2013. Based on our research, a relationship between time sensitivity and degree of passenger engagement was identified. This relationship was used as the basis for a new passenger segmentation model, namely: Airport Enthusiast (engaged, non time sensitive); Time Filler (non engaged, non time sensitive); Efficiency Lover (non engaged, time sensitive) and Efficient Enthusiast (engaged, time sensitive). The outcomes of this research extend the theoretical knowledge about passenger experience in the terminal environment. These new insights can ultimately be used to optimise the allocation of space for future terminal planning and design.

Passenger experience and their implications for airports retail environment design

This project develops new knowledge on the full range of activities and interactions that make up airport passengers' retail experiences. The practical application of this new knowledge will improve the design of airport retail environments and will, in turn, improve passenger experiences, leading to further growth in the airport retail market. The novel methodological approach developed allowed for a new and deeper understanding of how passengers actually experience airport retail environments. Four significant outcomes were discovered: (i) the categorisation of the full range of retail activities and interactions passengers actually undertake, (ii) a new understanding of how passengers use and experience their free airport time, (iii) two new passenger market segments, and (iv) two passenger retail experience tools, with these identifying the broad range of airport-specific factors which influence passengers retail experiences.

Agent-based modelling simulation case study : assessment of airport check-in and evacuation process by considering group travel behaviour of air passengers

Passenger experience has become a major factor that influences the success of an airport. In this context, passenger flow simulation has been used in designing and managing airports. However, most passenger flow simulations failed to consider the group dynamics when developing passenger flow models. In this paper, an agent-based model is presented to simulate passenger behaviour at the airport check-in and evacuation process. The simulation results show that the passenger behaviour can have significant influences on the performance and utilisation of services in airport terminals. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

A policy critique of Stansted Airport's expansion to 25 million passengers per annum (MPPA)

In this paper we undertake a preliminary assessment of the regional planning and development implications of BAA Stansted Airport’s planning permission to grow to 25 million passengers per annum (mppa) by 2010. Our concern is not simply to consider the overall growth of the airport on the airport site itself but the nature and type of growth both on- and off-site. In this document we focus on the submitted planning permission documents and test them. The methodology we employed was to draw on published and unpublished numerical estimates of the airport’s growth – particularly including estimates produced by the airport owner, BAA, and their economic and planning consultants DTZ Pieda - and critically, and systematically analyse their figures. We adopted this approach because unless the figures which were employed in the initial calculations were correct then all of the subsequent projections which flow from them - and the polices which could then be based on them – could be flawed. The analysis is divided into two parts – firstly, are the growth forecasts correct?; and secondly, what do these forecasts actually mean in developmental terms? In effect, what we have done is to produce a critique of the existing body of evidence by questioning underpinning assumptions and then draw some preliminary conclusions for the region based on this analysis. A major focus of this report has been analyse the figures involved in the planning application to expand Stansted to 25mppa. Ironically, one of our key findings, that the local impact of Stansted’s proposed expansion in employment terms might well be less than was originally thought, might make it easier to gain the acceptance of the relevant local authorities involved to allow the development to take place. Our main overall findings are that the BAA projections over-estimate the local employment impact of the airport’s proposed growth and under-estimate its potential regional ‘transportation’ employment effect. These two findings are, of course, related to each other in important ways, and we also feel that they have potentially significant medium and long-term economic, competitiveness and planning policy implications for the East of England region

Towards airport passenger experience models

This paper reports on the study of passenger experiences and how passengers interact with services, technology and processes at an airport. As part of our research, we have followed people through the airport from check-in to security and from security to boarding. Data was collected by approaching passengers in the departures concourse of the airport and asking for their consent to be videotaped. Data was collected and coded and the analysis focused on both discretionary and process related passenger activities. Our findings show the interdependence between activities and passenger experiences. Within all activities, passengers interact with processes, domain dependent technology, services, personnel and artifacts. These levels of interaction impact on passenger experiences and are interdependent. The emerging taxonomy of activities consists of (i) ownership related activities, (ii) group activities, (iii) individual activities (such as activities at the domain interfaces) and (iv) concurrent activities. This classification is contributing to the development of descriptive models of passenger experiences and how these activities affect the facilitation and design of future airports.

Check-in processing : simulation of passengers with advanced traits

In order to tackle the growth of air travelers in airports worldwide, it is important to simulate and understand passenger flows to predict future capacity constraints and levels of service. We discuss the ability of agent-based models to understand complicated pedestrian movement in built environments. In this paper we propose advanced passenger traits to enable more detailed modelling of behaviors in terminal buildings, particularly in the departure hall around the check-in facilities. To demonstrate the concepts, we perform a series of passenger agent simulations in a virtual airport terminal. In doing so, we generate a spatial distribution of passengers within the departure hall to ancillary facilities such as cafes, information kiosks and phone booths as well as common check-in facilities, and observe the effects this has on passenger check-in and departure hall dwell times, and facility utilization.

Including airport duty-free shopping in arriving passenger simulation and the opportunities this presents

Simulating passenger flows within airports is very important as it can provide an indication of queue lengths, bottlenecks, system capacity and overall level of service. To date, visual simulation tools such as agent based models have focused on processing formalities such as check-in, and not incorporate discretionary activities such as duty-free shopping. As airport retail contributes greatly to airport revenue generation, but also has potentially detrimental effects on facilitation efficiency benchmarks, this study developed a simplistic simulation model which captures common duty-free purchasing opportunities, as well as high-level behaviours of passengers. It is argued that such a model enables more realistic simulation of passenger facilitation, and provides a platform for simulating real-time revenue generation as well as more complex passenger behaviours within the airport. Simulations are conducted to verify the suitability of the model for inclusion in the international arrivals process for assessing passenger flow and infrastructure utilization.

Flexible airport terminal design : towards a framework

Flexibility is a key driver of any successful design, specifically in highly unpredictable environment such as airport terminal. Ever growing aviation industry requires airport terminals to be planned and constructed in such a way that will allow flexibility for future design, alteration and redevelopment. The concept of flexibility in terminal design is a relatively new initiative, where existing rules or guidelines are not adequate to assist designers. A shift towards flexible design concept would allow terminal buildings to be designed to accommodate future changes and to make passengers’ journey as simple, timely and hassle free as possible. Currently available research indicates that a theoretical framework on flexible design approach for airport terminals would facilitate the future design process. The generic principles of flexibility are investigated in the current research to incorporate flexible design approaches within the process of an airport terminal design. A conceptual framework is proposed herein, which is expected to ascertain flexibility to current passenger terminal facilities within their corresponding locations as well as in future design and expansion.

Modelling passenger flow at airport terminals : individual agent decision model for stochastic passenger behaviour

Airport system is complex. Passenger dynamics within it appear to be complicate as well. Passenger behaviours outside standard processes are regarded more significant in terms of public hazard and service rate issues. In this paper, we devised an individual agent decision model to simulate stochastic passenger behaviour in airport departure terminal. Bayesian networks are implemented into the decision making model to infer the probabilities that passengers choose to use any in-airport facilities. We aim to understand dynamics of the discretionary activities of passengers.

Towards a taxonomy of passenger airport experience

Airports are vital sources of income to a country and city. Airports are often understood from a management perspective, rather than a passenger perspective. As passengers are a vital customer of airports, a passenger perspective can provide a novel approach in understanding and improving the airport experience. This paper focuses on the study of passenger experiences at airports. This research is built on recent investigations of passenger discretionary activities in airports by the authors, which have provided a new perspective on understanding the airport experience. The research reported in this paper involves field studies at three Australian airports. Seventy one people who had impending travel were recruited to take part in the field study. Data collection methods included video-recorded observation and post-travel interviews. Observations were coded and a list of activities performed was developed. These activities were then classified into an activity taxonomy, depending on the activity location and context. The study demonstrates that there is a wide range of activities performed by passengers as they navigate through the airport. The emerging activity taxonomy consists of eight categories. They include: (i) processing (ii) preparatory (iii) consumptive (iv) social (v) entertainment (vi) passive (vii) queuing and (viii) moving. The research provides a novel perspective to understand the experience of passenger at international airports. It has been applied in airports to improve passenger processing and reduce waiting times. The significance of the taxonomy lies in its potential application to airport terminal design and how it can be utilised to understand and improve the passenger experience.

Understanding the airport passenger landside retail experience

The landside environment of an airport terminal is an important area for both passengers and the airport as it is the first area passengers enter and experience, influencing passengers’ overall airport experiences. This paper focuses on landside passenger experiences and factors which influence the quality of these experiences. Data collection occurred through video recorded observations of 40 passengers’ airport experiences at two Australian international departure terminals. The Observer software was used to code and analyse data. Indicative results show that passengers spend over half of their landside dwell time undertaking processing activities. The results highlight the important influencing role passengers’ companions have over the proportion of landside dwell time passengers spend undertaking discretionary activities. The findings provide an understanding of passenger landside experiences and how they can be improved. The significance of these findings lies in their potential application to landside airport terminal design with specific examples outlined. This new knowledge will assist in improving passenger airport experiences through informing future airport planning and design of landside spaces and retail environments.

Using advanced traits of passengers to facilitate route-choice decision-making

Research interest in pedestrian behaviour spans the retail industry, emergency services, urban planners and other agencies. Most models to simulate and model pedestrian movement can be distinguished on the basis of geographical scale, from the micro-scale movement of obstacle avoidance, through the meso-scale of individuals planning multi-stop shopping trips, up to the macro-scale of overall flow of masses of people between places. In this paper, route-choice decision-making model is devised for modelling passengers flow in airport terminal. A set of devised advanced traits of passengers is firstly proposed. Advanced traits take into account a passenger’s cognitive preferences and demonstrate underlying motivations of route-choice decisions. Although the activities of passengers are normally regarded as stochastic and sometimes unpredictable, real scenarios of passenger flows are basically feasible to be compared with virtual simulations in terms of tactical route-choice decision-making. Passengers in the model are as intelligent agents who possess a bunch of initial basic traits and are categorized into five distinguish groups in terms of routing preferences. Route choices are consecutively determined by inferring current advanced traits according to the utility matrix.