Detecção de comunidades em redes complexas para identificar gargalos e desperdício de recursos em sistemas de ônibus
This work addresses urban planning challenges for city administrators by providing a data-driven method to optimize bus resource allocation, though it appears incremental as it applies existing network analysis techniques to transportation data.
The authors tackled the problem of identifying inefficiencies in public transportation systems by analyzing complex networks of supply and demand, using smart card data to reconstruct passenger itineraries and reveal overload and waste.
We propose here a methodology to help to understand the shortcomings of public transportation in a city via the mining of complex networks representing the supply and demand of public transport. We show how to build these networks based upon data on smart card use in buses via the application of algorithms that estimate an OD and reconstruct the complete itinerary of the passengers. The overlapping of the two networks sheds light in potential overload and waste in the offer of resources that can be mitigated with strategies for balancing supply and demand.