Multiple bus, ferry and light rail services in Sydney and Newcastle will be used to study how real-time views of these services can guide reliability, future network decisions and improvements in maintenance work.

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The New South Wales Government and Cisco are working with artificial intelligence, Internet of Things and edge computing technologies to improve the reliability of public transport in Sydney and Newcastle.

As part of the experiment, Transport for New South Wales (TfNSW) is using the Internet of Things to enable physical objects to be “digitized” and connected to transportation networks through sensors, while edge computing will be used to obtain real-time data from connected objects to achieve faster decision making. At the same time, artificial intelligence will be used to help understand data and automate processes.

The state government stated that these technologies will be connected to multiple buses, ferries and light rail vehicles in the two cities.

"We have worked with Cisco to study how real-time views of vehicle supply and customer demand and performance can guide future network decisions, and monitor road conditions to determine where repairs are needed," said Rob Stokes, Minister of Transportation and Roads.  

“We use artificial intelligence, Wi-Fi and edge computing on Pitt Street near Central Station to capture real-time data and identify high-risk events.

"As COVID-19 restrictions continue to be relaxed and more and more commuters and pedestrians return to Sydney's busiest transportation hub, we will be able to closely monitor the movements of vehicles."

The state government added that the data captured will also be used to monitor assets and understand the comfort of the customer journey in real time.

According to Simon Young, general manager of transportation and infrastructure at Cisco Australia and New Zealand, TfNSW is the only organization in the world to trial the company's AI system.

"These trials represent the strength of the partnership between Cisco and Transport for NSW to jointly innovate and use technology to solve some of the most pressing and challenging problems facing transportation agencies," he said.

However, these experiments are not the first time TfNSW has used artificial intelligence and data analysis. In September, the agency said it was using artificial intelligence to develop predictive algorithms to help national, state, and local governments manage their road safety performance.  

TfNSW worked with iMove Cooperative Research Centre (CRC), University of Technology Sydney, International Road Assessment Program (iRAP) and geospatial data company Anditi to develop a faster and more automated method to extract raw road data.

As part of the plan, which is called the Accelerated and Intelligent Road Assessment Program Data Collection (AiRAP) project, the group plans to use TomTom’s MN-R next-generation map data to provide what it calls the 20,000 kilometers of roads in New South Wales. Available data, as well as lidar data extraction technology and machine learning. Pilot evaluations will also be conducted on samples of local, state, and national highways to demonstrate these methods.

At the end of last year, TfNSW worked with Microsoft to develop a proof of concept that uses data and machine learning to mark potentially dangerous intersections and reduce road accidents.

As part of the proof-of-concept, Transport for New South Wales conducted an experiment in Wollongong to discover five potentially risky intersections. Before using Databricks and Azure to manage, ingest, and interpret data, it involved 50 cars generating more than 1 billion rows of data in 10 months.

The telematics data is used to identify the speed, sudden braking, sudden acceleration and lateral movement in front of the intersection. Then compare it with the patterns of existing collision survey data.

Since the trial, two of the five intersections have been scheduled for renovation.

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