Alstom unveiled on 30 June a new version of Mastria, its multimodal traffic organisation system, based on artificial intelligence, to help operators and transport authorities around the world better manage passenger flows and adapt to changing demand in the face of the Covid-19 crisis.
Eager to position itself in more sustainable and intelligent mobility Alstom solutions, France’s Alstom has developed a system with the Institute for Technological Research (IRT) SystemX (located in Palaiseau, Essonne) to improve the efficiency and productivity of networks and infrastructures. This “conductor of all modes of public transport in the city” is now offered to operators and transport authorities around the world, after being tested in a few cities.
The algorithm targets transport nodes, where different modes of transport interact with each other. In the current context of the Covid-19, it would also allow a better understanding of user flows in order to limit the occupancy rate in transport.
Sensors, surveillance cameras…
Through big data and machine learning, Mastria provides a better view of the flow and distribution of passengers. It operates from train weight sensors, ATMs, traffic management and signalling systems, surveillance cameras and mobile networks, providing information on passenger demand. The algorithm analyzes millions of data in real time so that it can suggest alternatives in case of excessive demand, incidents on the tracks, or a restriction on the number of passengers. “Mastria may suggest increasing the frequency of trains, redirecting passengers to the nearest station, strengthening alternative transport systems, restricting access to certain stations, or even managing the distribution of passengers on the platform to align them with the less-filled train cars,” Alstom explains.
Already tested in the Panama metro
At the end of 2019, Alstom has set up Mastria in the metro of Panama, the capital of the eponymous American country, to propose a solution to saturation occurring in unpredictable ways. Thanks to THE AI, localized saturations could be anticipated up to 30 minutes before their appearance, allowing measures to be taken to reduce the waiting time at the station by 12%, according to Alstom. This technology can also now limit the occupancy rate of trains to the 40% recommended because of the health crisis.
Pilot tests have also yielded results in Zaragoza (Spain), Florence(Italy)and even Paris, Alstom says, although the RATP already seems to have its own system. The company also conducts research projects with System X, start-ups Clio and Deepomatic,as well as other technology partners to developfeatures that can also be used to act effectively in the event of an incident on the public transport network.
This video in English helps you understand how AI interacts in the event of an incident: