Portal de Periódicos da CAPES

Toward a Route Optimization Modular System

2023; Springer International Publishing; Linguagem: Inglês

10.1007/978-981-19-9331-2_32

2367-3370

José Pinto, Manuel Filipe Santos, Filipe Portela,

Transportation Planning and Optimization

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.