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A Counterfactual-Based Approach to Prevent Crowding in Intelligent Subway Systems
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  • Alberto Carlevaro ,
  • Marta Lenatti ,
  • Alessia Paglialonga ,
  • Maurizio Mongelli
Alberto Carlevaro

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Marta Lenatti
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Alessia Paglialonga
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Maurizio Mongelli
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Today, the cities we live in are far from being truly smart: overcrowding, pollution, and poor transporta- tion management are still in the headlines. With wide-scale deployment of advanced Artificial Intelligence (AI) solutions, however, it is possible to reverse this course and apply appro- priate countermeasures to take a step forward on the road to sustainability. In this research, explainable AI techniques are applied to provide public transportation experts with suggestions on how to control crowding on subway platforms by leveraging interpretable, rule-based models enhanced with counterfactual explanations. The experimental scenario relies on agent-based simulations of the De Ferrari Hitachi subway station of Genoa, Italy. Numerical results for both prediction of crowding and counterfactual (i.e., countermeasures) properties are encouraging. Moreover, an assessment of the quality of the proposed explainable methodology was submitted to a team of experts in the field to certify and validate the model.