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The research will develop and solve a mathematical model for the shuttle service network design with autonomous vehicles within an airport. A time-space service network with airport shuttles and time-space networks for passenger flows will be developed.ÌýÌý
The service network design problem is formulated as a mixed integer linear programming model. The objective is to minimise the waiting and ride time of passengers as well as the operating costs of running the autonomous shuttles.ÌýÌý
The results of this study include a modelling framework for shuttle service network design within an airport, a solution algorithm for the developed service network design model and a case study of Sydney airport under different demand scenarios. Based on the case study, several managerial insights will be generated.Ìý
Category: Ground operationsÌý
Researcher: Runqing ZhaoÌý
Supervisor: Dr Tay KooÌý
Level: PhDÌý
Status: CurrentÌý
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Category:ÌýGround operations
Researcher:ÌýDan Wang
Supervisor:ÌýProfessor GabrielÌýLodewijks
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Category:ÌýGround operations
Researcher:ÌýSarika Tyagi
Supervisor:ÌýProfessor Gabriel Lodewijks
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One of the major difficulties thatÌýinvolvesÌýoperational challenges forÌýairportsÌýworldwide is the inefficient energy loss connected with the fixed baggage conveying method in airports.Ìý
The focus of this study is to design a significant energy efficient, safety enhanced autonomous vehicles (AVs) based baggage handling systems (BHS) for highly complex airfields by adopting automotive technologiesÌýthat offerÌýsustainable, flexible and reliable mobility solutions.ÌýThe aim is toÌýsave about half of the energy utilisation in comparison with the traditional BHS.Ìý
Baggage will be carried through utilising each of the individual AVs by determining the most optimal pathway through the airport. This project will result in potential efficiency gains from automation across airport related functions such as cargo-handling and logistics operations, as well as passenger transportation both to and within airports.Ìý
ThisÌýcould have a great impact on human civilisation in airports, mine-fields, railways, ports and other automotive industries. Moreover, it will have a significant environmental advantage by decreasing emissions effectively because aboutÌýthreeÌýper centÌýof greenhouse gas emissions globally are caused by airfield implementations.
Category: Ground operations
Researcher: MdÌýYeasirÌýArafat
Supervisor: Professor Gabriel Lodewijks
Level: PhD
Status: Current
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Managing risks have become increasingly important in modern organisations. Managers recognise it is difficult to manage without measurement, however, it is difficult to know what to measure in order to drive the continuous safety improvements promised by modern safety management systems.ÌýÌý
One industry where safety performance improvement appears to have stalled is aviation ground operations. Unlike the flight operations, which are now regarded as an ultra-safe system (Amalberti 2001, p. 109) ground aviation has languished behind the rest of the industry (Verschoor and Young 2011), with activities on the ramp now accounting for more than a quarter of all aviation incidents (Balk and Bossenbroek 2010).ÌýÌý
In recent years, both damage to aircraft on the ground as well as harm to ground personnel have escalated (Passenier 2015 p. 38). The aim of this research was therefore to review current approaches to safety measurements and develop a new data collection tool to inform evidenced-based interventions that will reinvigorate ground safety improvement.Ìý
To achieve this, models and measures influential to aviation safety are reviewed. The Threat and Error Management Model (TEM) and Line Operation Safety Audit (LOSA) method (Klinect, Murray, Merritt and Helmreich 2003) are critically examined for their suitability. Strengths and weaknesses are identified with the aim of building on the benefits of LOSA whilst addressing key concerns about LOSA’s validity.ÌýÌý
A new model and method proposed is known as Normal Operations Monitoring (NOM). NOM is applied in a ground handling organisation with data collected from over 1300 observations of aircraft turnarounds.Ìý
The results provide novel data about human and safety performance and suggest new opportunities for safety interventions and improvement. Implications for ground safety are explored as well as the potential applications and benefits of NOM generally. The final discussion explores ways in which the current research and NOM tools could be taken forward as a method for informing and improving safety management in high hazard industries.Ìý
Category: Ground operationsÌý
Researcher: Dr Louise RaggettÌý
Supervisor: Professor Ann WilliamsonÌý
Level: PhDÌý
Status: CompletedÌý
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Many airports today consider the application of autonomous vehicles for the transportation of passengers from parking lots to the terminals or for the transportation of baggage from the lateral to the aircraft.Ìý
This project concerns anÌýanalysis of the transport requirements at airports and aÌýdetermination ofÌýwhere, how and under what circumstances autonomous vehicles can be applied.
Category: Ground operations
Researcher: N/A
Supervisor: Professor Gabriel Lodewijks
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Status: Future