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Challenge / Goal

When Metro de Panama opened a new line in 2019, it faced high and fluctuating passenger numbers, which generated saturation, long waiting times and safety issues. Subsequently, due to the emergence of the Covid-19 pandemic, a new and important conditioning factor had to be taken into account: passenger loads on trains had to be adapted to 40% of their maximum capacity, as recommended by the country's health authorities.

Solution

The Operator used the "Mastria" solution to build dynamic passenger flow models. In real time, they could know 30 minutes in advance the exact point of future saturation and adapt train frequencies and capacities to the expected situation.

The solution is based on the following innovative features:

1) Using existing data sources (ticketing, weight sensors, video analysis) without implementing any additional hardware to monitor and model passenger flow.

2) Use real-time predictive machine learning techniques to anticipate passenger saturation and boarding failures with high accuracy.

3) Use a real-time decision support approach for dynamic scheduling (adapting train movements to traffic fluctuations).

4) Adapt the solution and models during the COVID crisis to monitor occupancy levels (social distance) and trigger real-time predictive alerts.

5) Simulate services and traffic (and the number of doors open at each station) based on the latest passenger data during the COVID crisis to keep the occupancy rate below 40%.

Images


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Implementers

ALSTOM

Service providers

Panama Subway

End users

All citizens

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