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Intelligent Train-Carriage Load Analysis and Prediction

Yellow Sydney Train - front view

 

Innovating the Future of Surf Patrol

White Westpac Little Ripper Drone with yellow inflatable safety device attached

 


 

 

Intelligent Train-Carriage Load Analysis and Prediction

two different suburban trains with train data displayed

 

Research Leads
D/Professor Jie Lu
Dr Anjin Liu

AAII Research Lab
Decision Systems and e-Service Intelligence (DeSI) Lab

Collaborators
Sydney Trains

To better spread the customer across the train’s carriages and manage platform crowding, carriage load information is provided on both SPI screens and to the third-party apps feed for the Waratah fleet. The passenger load information to SPI Screens via the On-Board Handler (OBH) displays the train load from the weight captured as the doors closed at the last station.

The intent of this work is to utilize historical and real time Waratah carriage load data to build predictive capability which can provide expected passenger load across the train journey based on time of day. This work also looks for options to provide passenger loads across other sets which don’t have real time carriage load reporting capabilities.

 

 


 

Innovating the Future of Surf Patrol

 

Sharkspotter drone on beach with awards

Sharkspotter: the world's first drone-enabled automatic shark detection system

Research Lead 
Professor Michael Blumenstein
Associate Professor Nabin Sharma                                          
AAII Research Lab
Intelligent Drone Lab (iDL)

Collaborators
The Ripper Group


Sharks, rips and other factors present real challenges to keeping swimmers safe in our oceans. A pioneering collaboration between UTS researchers and The Ripper Group is fusing artificial intelligence and drone technologies to herald a new wave in surf lifesaving. SharkSpotter leverages advanced machine learning techniques known as ‘deep learning’ to detect sharks and other potential threats in real time.

The UTS-developed software turns aerial drones into powerful and highly accurate shark detectors, analysing streaming video from the drone to detect valid objects instantly. It then categorises them into one of 16 categories such as shark, whale, dolphin, ray, various watercraft and boat types, surfer and swimmer. When a shark is detected, SharkSpotter provides a visual indication on the computer screen and an audible alert to the operator. The operator verifies the alert, triggering system text messages to surf lifesaving crews on the ground and enabling them to act quickly. It’s not just swimmers who benefit: safer beaches are a boon for coastal tourism and regional economies. The ground-breaking technology also demonstrates more effective ways to detect moving objects in a complex, dynamic marine environment, with significant potential to protect our unique marine life.


 

Acknowledgement of Country

UTS acknowledges the Gadigal People of the Eora Nation and the Boorooberongal People of the Dharug Nation upon whose ancestral lands our campuses now stand. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands. 

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