has been the highest priority in aviation, a new collaboration between the
International Air Transport Association (IATA) and the Civil Authority of
Singapore (CAAS) aims to take aviation safety to an even higher level and facilitate
sustainable aviation growth.
to a press
statement, IATA and CAAS are joining hands in establishing a
Global Safety Predictive Analytics Research Centre (SPARC) in Singapore.
the Memorandum of Collaboration (MOC), SPARC will utilise predictive analytics
to identify potential aviation safety hazards and assess related risks.
first area of focus for SPARC will be runway safety, such as runway excursions,
which are the most frequent category of accidents in recent years, according to
to Director General and CEO of IATA Mr Alexandre de Juniac, SPARC aims to be “a
system-based, data-driven, predictive approach to preventing accidents,
including analysing the more than 10,000 flights that operate safely every day.”
leverages research capabilities in Singapore as well as operational flight data
and safety information that are available under IATA’s Global Aviation Data
Management (GADM) initiative 1. End users across the aviation community can
then work collaboratively at the system level to address and implement
appropriate safety measures to mitigate the risks, or even to prevent the
occurrences of safety hazards.
of CAAS Mr Kevin Shum, added, “The establishment of SPARC in Singapore is
especially timely given the anticipated doubling of air traffic in the Asia
Pacific by 2036. SPARC’s predictive data analytics capabilities will help the
aviation sector in Asia Pacific better anticipate, prioritise and address
safety issues more effectively.”
envisions to improve flight safety risk management. To do so, it will engage
the rest of the aviation community through broad consultation and collaboration
for knowledge sharing to identify the most effective applications of the safety
In the coming months, the SPARC project team
will be working closely with the industry and its stakeholders to develop
safety predictive models to ensure that the output generated meets the
industry’s current and future needs.