Abstract
Urban mobility and routes planning are one of the biggest problems of cities. In the context of smart cities, researchers want to help overcome this issue and help citizens decide on the best transportation method, individual or collective. This work intends to research a modular solution to optimize the route planning process, i.e., a model capable of adapting and optimizing its previsions even when given different source data. Through artificial intelligence and machine learning, it is possible to develop algorithms that help citizens choose the best route to take to complete a trip. This work helps to understand how Networkx can help transportation companies to optimize their routes. This article presents an algorithm able to optimize their routes using only three variables starting point, destination, and distance traveled. This algorithm was tested using open data collected from Cascais, a Portuguese City, following the General Transit Feed Specification (GTFS) and achieved a density score of 0.00786 and 0.00217 for the two scenarios explored.
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Acknowledgements
This work has also been developed under the scope of the project NORTE-01-0247-FEDER-045397, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER).
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Pinto, J., Santos, M.F., Portela, F. (2023). Toward a Route Optimization Modular System. In: Anwar, S., Ullah, A., Rocha, Á., Sousa, M.J. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 614. Springer, Singapore. https://doi.org/10.1007/978-981-19-9331-2_32
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DOI: https://doi.org/10.1007/978-981-19-9331-2_32
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