Project Team

Dr. Bidisha Ghosh (PI) is an Associate Professor in the School of Engineering, TCD. She is an expert in traffic & air pollution prediction using statistical methods, time-series modelling, artificial intelligence algorithms, sustainable transportation, and health impact assessment of active travel. She has published over 100 peer-reviewed journal and conference articles with >1000 citations. She is the Chair of the Irish Transport Research Network (ITRN). She has been and is currently involved in multiple EPA projects on traffic and air pollution, health impact assessment of active travel, & effects of transport fleet modernisation etc.

Dr. Fionn Rogan (CO-PI) is a Senior Research Fellow in UCC and MaREI. Fionn has a PhD in energy systems modelling and has worked with simulation and optimisation energy system models to support climate action policy making in Ireland.

Dr. Mounisai Siddartha Middela is a postdoctoral research fellow in the School of Engineering, Trinity College Dublin. He completed his PhD in Civil Engineering from Indian Institute of Technology (IIT), Madras in 2022. His research interests in are sustainable transport planning, freight transportation, and econometric modelling. He has experience working on various projects such as developing a roadmap for electrification of freight vehicles, and electric vehicle battery discharge level and range prediction models in Chennai, India. He has also developed spatial demand prediction models for freight systems and emergency response systems.

Dr. Vahid Aryanpur is a post-doctoral researcher at MaREI, UCC. He has been involved in various energy systems modelling projects focusing on sustainable and net-zero decarbonisation target. Vahid has a significant ability to develop and use simulation and optimisation models to inform the decision-making process. His publications mainly address long-term transition scenarios for the power and transportation sectors. He also investigates how higher spatial resolution, energy-economy linkage, structural and parametric uncertainties, and heterogeneity of consumers impact model development and findings.