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Passenger Counting

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Nomad Digital’s AI-driven passenger counting solution gives operators valuable insights into train loading and passenger behaviour to improve efficiency and passenger experience. Advances in technology, including WiFi tracking and image processing, along with expertise in delivering WiFi solutions, enable Nomad to provide a solution without the need for sensors.

Our WiFi device tracking solution monitors passenger numbers by counting WiFi-enabled devices, even if they are not connected to the onboard WiFi. AI-driven analytics refine this data to account for passengers with multiple or no WiFi devices to present an accurate relative load across the train.

AI-Driven Image Processing technology offers real-time load monitoring by utilising onboard CCTV cameras to deliver precise passenger occupancy in each coach. The system processes data locally on the train, eliminating the need for facial recognition, and storing or transmitting data off the train. It also detects other objects, including bicycles and luggage, helping prevent prolonged blockages in aisles and vestibules.

Building detailed insights into passenger behaviour improves operational efficiency and enhances passenger service, ensuring a smoother and more enjoyable travel experience. The solution leverages the power of edge computing and advanced cloud-based analytics to process vast amounts of data cost-effectively.

Data monitoring


A Cost-effective Solution: Combines edge computing with cloud analytics for efficient data processing utilising existing WiFi solutions.

Improved Operational Efficiency: Gain insights into passenger behaviour and train loading to optimise operations.

Assurance of Privacy: Respect passenger privacy with local data processing and no use of facial recognition.

Enhanced Passenger Experience: Using analytical data to aid in passenger movement and seat availability, improving overall passenger service.

See how our customers have benefited

Metro Trains Melbourne, improving efficiencies

The Challenge

To implement the Remote Online Condition Monitoring technology fleet-wide, on a total of more than 400 units, providing the basis for the introduction of preventative and intelligence-driven maintenance practices.

The Solution

Nomad implemented a comprehensive solution which includes train embedded software and hardware, a back-office application for data analysis and visualisation, and a railway engineering support solution.

The Remote Online Condition Monitoring system (ROCM) provides real-time condition and diagnostic based information across key operational elements, and creates a scalable framework for the future introduction of both predictive and remote condition based maintenance methodologies.