The ability to predict passenger flow in transport networks is an important aspect of public transport management. It helps improve transport services, aids those responsible for management to obtain early warning signals of emergencies and unusual circumstances and, in general, makes cities smarter and safer.
This project develops a long short-term memory-based (LTSM-based) deep learning model to predict short-term passenger flow on metro routes in Medellín, Colombia. This prediction model has been created using a dataset that included the number of people who used different metro routes in Medellín at one-hour intervals between January 2020 and April 2024.