Alert, Your Trip Will Be Crowded: Crowding Prediction at Västtrafik
When the Corona pandemic hit the world Västtrafik wanted a precise way of informing travelers on what trips that were likely to be crowded so the travelers could plan their trips in a safe way. Since Västtrafik is working in a large geographical area, and a heterogenous population landscape, creating warnings only based on time of day wasn’t considered to be good enough. We will present a use case of machine learning in production based on data from the automatic passenger counting system. Focus will be on all those details that makes an ML solution useful live in real life; How the results are communicated, how it is maintained and what model is used in the engine. We will also present results on how it was received by the travelers and how it is continuously evaluated and evolving.
Björn Thalén is a data science consultant at B3Indes, a company helping organizations to create value from data. At one of his clients, Västtrafik, Björn has been the lead developer for an AI-model for crowd predictions. His academic background is in applied mathematics and history of science. In his professional life he has focused on mathematical optimization and transportation planning systems, helping some of the world’s leading transportation companies to optimize their planning. Björn is a coordinator for EURO Practitioners' Forum, a European network of industry mathematicians. He got his M.Sc at Linköping University and lives in Gothenburg since 2010.
