ARTIFICIAL INTELLIGENCE TO ENSURE PASSENGER DISTANCING AND SAFETY
Alstom has launched a new version of Mastria, a multimodal supervision and mobility orchestration solution, that uses artificial intelligence (AI) to provide operators and transport authorities with enhanced passenger flow management tools. The solution developed to manage passenger flows in trains and stations, should allow operators to adapt, easily and in real time, their offer to the various social distancing and public gathering requirements that have arisen due to the Covid-19 pandemic.
Mastria uses big data and machine learning to give operators higher visibility on passenger distribution and flow in stations, as well as enhanced predictive capabilities. Matching the supply of trains with the demand optimises operating conditions and is according to Alstom especially useful for managing fluctuating demand peaks, such as during rush hours or special events, or special mobility restrictions, as in the case of Covid-19. Mastria is based on four main standard functions: multimodal supervision, traffic management, coordination of operations and predictive analysis. These functions are highly configurable and can be combined according to the needs of operators and the global mobility network environment.
The new implementation of Mastria aggregates information on passenger demand from train weight sensors, ticketing machines, traffic signalling, management systems, surveillance cameras and mobile networks in order to offer a real-time picture of passenger flows. Mastria can suggest increasing trains frequency, redistributing the flow of people to particular stations, readjustments to other transport systems that impact the subway, restricting entry to stations or even managing the distribution of passengers on the platform to align them with cars with more space on a given train. Mastria’s powerful prediction algorithms anticipate these situations, allowing proper planning of the entire system.
Alstom implemented Mastria for Panama Metro at the end of last year with as objective to analyse traveller flows and offer a way to avoid the saturation that appeared at unpredictable times and only in certain stations. Alstom states that in only three months localized saturation could be predicted up to 30 minutes before it could be visibly observed, thereby allowing remedial action that reduced waiting times in stations by 12%. Currently, in response to the Covid-19 situation, the same technology is being used to adapt the operational actions that maintain the train's load to 40% of its maximum capacity, as recommended by the country's health authorities.