  Ongoing traffic changes, including those triggered by the COVID-19 pandemic,
reveal the necessity to adapt our public transport systems to the ever-changing
users' needs. This work shows that single and multi objective stances can be
synergistically combined to better answer the transit network design problem
(TNDP). Single objective formulations are dynamically inferred from the rating
of networks in the approximated (multi-objective) Pareto Front, where a
regression approach is used to infer the optimal weights of transfer needs,
times, distances, coverage, and costs. As a guiding case study, the solution is
applied to the multimodal public transport network in the city of Lisbon,
Portugal. The system takes individual trip data given by smartcard validations
at CARRIS buses and METRO subway stations and uses them to estimate the
origin-destination demand in the city. Then, Genetic Algorithms are used,
considering both single and multi objective approaches, to redesign the bus
network that better fits the observed traffic demand. The proposed TNDP
optimization proved to improve results, with reductions in objective functions
of up to 28.3%. The system managed to extensively reduce the number of routes,
and all passenger related objectives, including travel time and transfers per
trip, significantly improve. Grounded on automated fare collection data, the
system can incrementally redesign the bus network to dynamically handle ongoing
changes to the city traffic.
