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A city-wide artificial society for mobility studies, using real GTFS data and Python data analysis tools.

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Modelling and Simulating Disruptions in Porto’s Public Transports

Done in the context of the Modelling and Simulation curricular unit, FEUP 2023/24.

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Abstract

This study introduces a sophisticated model for Porto's public transportation, utilizing the STCP and Metro do Porto (MDP) networks, represented as a graph. With it we propose calculating the shortest paths between centroids of statistical sections from Census data, providing a nuanced view of the network's efficiency. Our analysis confirmed the critical role of the Trindade station and highlighted challenges in data consistency and quality. The discussions in our report focus on the implications of network modifications and the importance of coherent data sources in accurate modelling. Future enhancements will involve leveraging route data for decision-making, optimizing journey planning, and enhancing network resilience.

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A city-wide artificial society for mobility studies, using real GTFS data and Python data analysis tools.

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