Data driven operations
26.04.2017 UITP 2017Share
Go with the flow
Metro networks are awash with data. What if you could use that data to transform the passenger experience?
Understanding passenger flows is vital for metro operators. Knowing where, when and how their networks are being used enables them to deliver a wide range of passenger benefits. Among these are reductions in overcrowding and quality-of-service improvements.
To meet the need for a deeper understanding of passenger flows, Thales has developed Smart Mobility Analytics based on its in-house multi-purpose big data platform for public transport authorities and operators.
Actionable insights are gained by analysing existing data in new ways. Ticketing, train scheduling, signalling, GPS – even weather data – can all be used to provide insights into passenger flows that would otherwise be impossible to obtain.
Naia – Thales Passenger Flow Analytics - allows operators to answer complex questions, such as: how crowded are my platforms? How crowded are my trains? How many trains passengers have to wait before getting in?
Answers to these questions are the starting point for solutions. Insights are obtained through analysis of existing data using ground-breaking big data techniques and mathematics modelisation, aiming at reconstructing passenger journeys.. No additional surveys are required.
Naia – Passenger Flow Analytics helps operators to:
- Transform the passenger experience with support for demand management strategies to combat overcrowding.
- Make better decisions across a wide area of operations, from train timetabling to fare policy, fraud prevention, marketing and advertising.
- Drive insight discovery with tools that can identify everything from friction points in traffic flows to new passenger behaviours.
- Reduce risk by anticipating overcrowd and allowing operators to simulate solutions before they’re implemented, a vital capability on complex metro systems where even a small change can have network-wide implications.
- Improve ROI by getting the most out of data that’s already there, rather than having to rely on expensive and error-prone human surveys.