Connecting Heterogeneous Actors in Urban Mobility with Distributed Artificial Intelligence and Machine Learning Techniques
The impact of urban traffic in societies is twofold. Quantitatively speaking, the loss of time in traffic corresponds to billions of dollars per year. Qualitatively, a better mobility is an important force for the alleviation of poverty.
The technology around the Internet is already mature for a scenario where cars and other traffic participants are able to exchange information. Traffic authorities could then collect, perform analytics and use such information in various ways (e.g., for short-term management and for policy-making). Extracting this potential from all the elements in the traffic system, as well as from diverse data sources requires new paradigms related to distributed artificial intelligence and machine learning.
This project proposes the development of methods and tools for investigating the use of Internet in various traffic-related contexts, such as route choice by connected vehicles and intelligent traffic signal controllers, targeting scientific publications and practical applications.
Reference:
Bazzan, Ana L. C. Improving urban mobility: using artificial intelligence and new technologies to connect supply and demand. https://arxiv.org/abs/2204.03570, 2022.
Main Research Topics
- Artificial Intelligence
- Reinforcement learning
- Urban mobility
- Smart cities
- Intelligent Transportation Systems
Curriculum Vitae
- 2016-2024: Full Professor, Computer Sc. Institute, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
- 2018 (SoSe): Guest professor, Humboldt University (Berlin)
- 1999-2016: Adjunct and Associated Professor, Computer Sc. Institute, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
- 2006 and 2019: Research Fellow Alexander von Humboldt / George Foster (sabbaticals at Univ. of Würzburg, and TU Berlin, Germany)
Publications and Presentations
- Ana L. C. Bazzan (2009). Opportunities for multiagent systems and multiagent reinforcement learning in traffic control. Autonomous Agents and Multiagent Systems, 18(3):342–375.
- Ana L. C. Bazzan and Franziska Klügl (2013). Introduction to Intelligent Systems in Traffic and Transportation, volume 7 of Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool.
- Ana L. C. Bazzan (2019). Aligning individual and collective welfare in complex socio-technical systems by combining metaheuristics and reinforcement learning. Eng. Appl. of AI, 79:23–33.
- Mohammad Noaeen, Atharva Naik, Liana Goodman, Jared Crebo, Taimoor Abrar, Behrouz Far, Zahra S H Abad, and Ana L. C. Bazzan (2022). Reinforcement learning in urban network traffic signal control: A systematic literature review. Expert Systems With Applications, 199.