AI4BUS EURECAT

Artificial intelligence applied to the Barcelona metropolitan bus network.

AI4Bus develops tools that allow for flexibility, adaptation and improvement of the bus network service in the Barcelona Metropolitan Area.

Based on demand analysis and historical patterns, AI4Bus develops artificial intelligence models capable of predicting passenger flow, allowing the operator to anticipate changing circumstances and adjust the frequencies of the main transport lines to provide a more efficient service.

AI4Bus offers a comprehensive solution with interconnected components through the integration of heterogeneous data sources to capture the complexity of the transport system. The project’s predictive models provide information on the future behaviour of the network to facilitate infrastructure management.

The explainability module allows for a better understanding of the role of external and internal variables in predictions, favours the interpretability of the models and reveals which factors have a greater impact on transport demand.

A more efficient public transport fleet will have the capacity to reduce travel and waiting times, which is why this project will have a positive impact on the environment, encouraging the use of buses over private vehicles.

The AI4Bus consortium is formed by Eurecat and AMB Informació i Serveis. On the part of Eurecat, the Big Data & Data Science Unit develops passenger flow prediction models, frequency optimisation and the demonstrator. On the other hand, the Technology Consultancy Department supports the company in the management of the project.

General details

Project

AI4Bus – Artificial intelligence applied to the metropolitan bus network

Project reference

CPP2023-010526

Programme and call for tender

Project funded by the Ministry of Science, Innovation and Universities through the call for public-private collaboration projects of the State Plan for Scientific, Technical and Innovation Research 2021-2023

Related ODS

Development of artificial intelligence models to optimise public transport management by improving infrastructure through innovation.

A more efficient public transport network mitigates inequalities, as it improves the population’s accessibility to services and jobs.

Development of algorithms for the optimisation of the public transport network, which has a potential impact on improving service, increasing the number of passengers on public transportation and reducing the use of private vehicles.

Reducing waiting times can have an impact on reducing private vehicle use, contributing to a reduction in emissions and a cleaner environment.