Santa Maiti

Dr. Santa Maiti is a Research Fellow in Operational Transport Modelling and Simulation at the Bartlett School of Environment, Energy & Resources, University College London since February 2024. Previously, she was a Research Fellow at the Technical University of Munich from January 2022 to January 2024. Her research focuses on transportation systems modeling and simulation, including conventional and emerging transportation systems, connected and autonomous vehicles, and shared mobility.

Dr. Maiti's educational background includes:

• PhD in Intelligent Transportation from the University of Melbourne
• Master by Research in Information Technology from the Indian Institute of Technology Kharagpur
• Bachelor's degree in Computer Science from West Bengal University of Technology

Her research interests span transportation systems modeling with a focus on applying machine learning and artificial intelligence to solve complex transportation problems. She has conducted international, interdisciplinary, and inter-sectoral research in this field.

Dr. Maiti has authored more than 20 scientific publications in peer-reviewed journals, conferences, and patents. Her recent work focuses on dynamic ridesharing, platoon formation strategies for multi-lane highways, and flexible platoon operations, demonstrating a consistent research trajectory in intelligent transportation systems and vehicle automation.

Her notable awards include:

• Melbourne Research Scholarship grant (MIFRS and MIRS) during her PhD
• EuroTechPostdoc Marie Sklodowska-Curie research grant for postdoc research

Dr. Maiti has supervised multiple students, including bachelor and master's students at IIT Kharagpur and Technical University of Munich. She has taught various subjects including ITS System Architectures and Applications, Algorithms and Complexity, and Design of Algorithms at the University of Melbourne, Monash University, and Technical University of Munich. Currently, she is involved in the EU-funded project SYNCHROMODE, which aims to develop data-driven ICT tools for improving the management of transport operations from a multimodal perspective.