A Vehicle-by-Vehicle Approach for Solving Real-World Transportation Problems Using Machine Learning and Big Data

Participation in the event is free. Please click here to register.

Franco Basso
Pontifical Catholic University of Valparaíso, Chile

Transportation systems serve as the lifeblood of modern society, enabling the efficient movement of people, goods, and services. However, the escalating complexity and challenges embedded within transportation networks necessitate novel and innovative approaches to tackle real-world problems effectively. Traditional transportation management methods often rely on aggregate data and generic models, which might provide limited insights into the intricate dynamics of transportation systems. In this talk, we will show how the development of vehicle-by-vehicle frameworks harnesses the power of machine learning and big data techniques to address complex transportation issues at a granular level. Specifically, utilizing Santiago, Chile, as our case study, we tackle three real-world problems in the areas of road safety, public transport, and freight transport.

Franco Basso is an Associate Professor at the Pontificia Universidad Católica de Valparaíso (PUCV). His research specializes in applying Operations Research and Data Science to address challenges in transportation and logistics. Since completing his PhD in Engineering Systems at the Universidad de Chile in 2018, he has published 34 papers in Web of Science-indexed journals. Professor Basso has collaborated on applied research projects with major public and private sector organizations, including urban highways, urban and suburban rail companies, logistics providers, mining and wine firms, and governmental agencies.