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CCTV Analytics improve peak service punctuality on Sydney Trains

The NSW government gave Sydney Trains an undertaking to identify and rectify the cause of delays on its train network.

Research identified peak service congestion and passenger flow on and off the trains as contributing factors for delays and the cause of ongoing commuter frustration. 

In response to this research, Sydney Trains developed a trial to assess how alterations to internal seating configurations would improve the flow of passengers on and off the train and improve peak-service punctuality 

Trial Changes in Seating Configuration 

The trial seating configuration included the removal of 16 seats from each of the two carriages, increasing capacity by up to 80 people, or around 15 per cent for the two carriages. 

The new seating configuration was trialled on four train carriages to give an accurate and supported result. 

Methods to Acquire Meaningful Metrics 

To measure the impact of a change in seat configuration, Sydney Trains had to implement a method for collecting data across some meaningful metrics, including: 

  • The number of people boarding the carriage at stops 
  • The number of people leaving the carriages at stops 
  • Which direction passengers move once inside the carriage 
  • Behavioural data, density, comfort 

This data would assist operators in ascertaining how the seating configuration influenced passenger flow, and how that flow then impacted the on-time performance. 

Data Collection & Monitoring System 

The technology required to build a data collection and monitoring system included: 

  • IP CCTV camera system 
  • Passenger counting and virtual tripwire software 
  • PoE switching 
  • GPS technology 
  • Network Attached Storage (NAS) 
  • WiFi backbone network 

Working Together to Deliver Intelligence 

The CCTV provided a view of passengers boarding and disembarking the carriages, while integrated camera analytics allowed for passenger counting and heat flow mapping. 

GPS functionality provided additional insight such as time synchronisation that correlated to station location data. 

The data was then extracted from the onboard NAS via WiFi when the trains were at the marshalling yard. 

All equipment was EN50155 certified to ensure the data would not be compromised by environmental factors like vibration. 

"Using video analytics data, Sydney Trains identified the optimal seating configuration
for passenger flow in and out of carriages. Since the modifications, Sydney Trains has
consistently delivered improved peak-service punctuality."

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