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Projects: Projects for Investigator
Reference Number EP/N029496/1
Title TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing
Status Completed
Energy Categories Energy Efficiency(Transport) 10%;
Not Energy Related 90%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 30%;
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 30%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 40%;
UKERC Cross Cutting Characterisation Not Cross-cutting 90%;
Sociological economical and environmental impact of energy (Environmental dimensions) 10%;
Principal Investigator Dr J Chen
No email address given
Engineering and Materials Science
Queen Mary, University of London
Award Type Standard
Funding Source EPSRC
Start Date 01 July 2016
End Date 31 May 2017
Duration 11 months
Total Grant Value £316,421
Industrial Sectors Transport Systems and Vehicles
Region London
Programme NC : ICT
Investigators Principal Investigator Dr J Chen , Engineering and Materials Science, Queen Mary, University of London (99.999%)
  Other Investigator Dr MM Lone , Sch of Aerospace, Transport & Manufac, Cranfield University (0.001%)
  Industrial Collaborator Project Contact , University of Bristol (0.000%)
Project Contact , Queen Mary, University of London (0.000%)
Project Contact , BAE Systems Integrated System Technologies Limited (0.000%)
Project Contact , Beijing University, China (0.000%)
Project Contact , Rolls-Royce PLC (0.000%)
Project Contact , Air France KLM, France (0.000%)
Project Contact , Manchester Airport Plc (0.000%)
Project Contact , Zürich Airport, Switzerland (0.000%)
Project Contact , Simio LLC, USA (0.000%)
Web Site
Abstract There is an imminent need to make better use of existing aviation infrastructure as air traffic is predicted to increase 1.5 times by 2035. Many airports operate at near maximum capacity, and the European Commission recognises the necessity to increase capacity to satisfy demand. In addition, inefficient operations lead to delays, congestion, and increased fuel costs and noise levels inconveniencing all stakeholders, including airports, airlines, passengers and local residents.A critical issue is routing and scheduling the ground movements of aircraft. Although ground movement is only a small fraction of the overall flight, the inefficient operation of aircraft engines at taxiing speed can account for a significant fuel burn. This applies particularly at larger airports, where ground manoeuvres are more complex, but also for short-haul operations, where taxiing represents a larger fraction of an overall flight. It is estimated that fuel burnt during taxiing alone represents up to 6% of fuel consumption for short-haul flights resulting in 5m tonnes of fuel burnt per year globally. This project aims to investigate a decision support system which considers multiple factors to provide more robust taxiing routes.Current decision support systems for routing and scheduling taxiing aircraft suffer from several limitations:1) The only objective they consider is minimising taxi time, ignoring other important factors. These other factors include taking into account engine performance which is linked to fuel consumption, environmental impact and cost. Routes and schedules, which are efficient in terms of fuel and cost, are therefore compromised as a result of considering a one dimensional objective.2) Airframe dynamics are not taken into account during planning of routes and schedules. Consequently, the taxing instructions issued may be hard to follow, making compliance with the allocated routes unrealistic.3) Taxi time is typically based on average speeds of aircraft. This is an over-simplification meaning that any taxiing manoeuvre which falls outside the expected duration can affect the taxiing of other aircraft. Furthermore, if the approach of including overly conservative time buffers to absorb uncertainty is adopted, the resulting overall airport operating efficiency will be degraded.4) It is difficult to specify taxiing speeds and heuristic rules for routing and scheduling systems as: they depend on airport layout and operational requirements, which can vary throughout the day according to the volume of air traffic. Consequently, routing and scheduling systems have to be reconfigured for specific airports and operational constraints.5) Due to variability in taxi speed and over-simplistic models of aircraft, there is lack of understanding as to how much benefit can be achieved by automated routing and scheduling in real-world settings.TRANSIT will directly address these limitations of current systems, to make better use of existing airport infrastructure and lessen the impact of the growing aviation sector on the environment. Multi-objective optimisation algorithms will be integrated with models of aircraft to balance the reduction of taxi time, cost and emissions. We aim to make the routing and scheduling system easily reconfigurable to any airport. The uncertainty will be directly incorporated in planning, resulting in robust taxiing, verified by pilot-in-the-loop trials.TRANSIT aims to investigate such a system and its associated benefits in collaboration across a broad range of disciplines and fields (Engineering, Operational Research, and Computer Science) needed to tackle such challenging problem. Cooperation with leading industrial stakeholders, and consultation with established academics, ensure that the work is cutting edge while reflecting needs of the industrial partners.
Publications (none)
Final Report (none)
Added to Database 18/02/19