Rate My Professor Neema Nassir

Neema Nassir is an Associate Professor in Transport Engineering in the Department of Infrastructure Engineering at the University of Melbourne, where he also serves as the program coordinator for the Master of Civil Engineering. He earned his BSc and MSc in Civil and Transportation Engineering from Sharif University of Technology in Tehran, Iran, and his PhD in Transportation Systems from the University of Arizona in 2013. Following his doctorate, he conducted research at the Centre for Transport Strategy at the University of Queensland. From 2016 to 2018, he held a Senior Postdoctoral Associate position at the MIT Urban Mobility Lab, also known as the JTL-Transit Lab. Nassir joined the University of Melbourne in October 2018 as a Lecturer and advanced to Associate Professor.

His research focuses on transportation engineering, including transportation modelling, public transport operations and network accessibility, origin-destination estimation, activity detection in transit fare data, traffic signal control, transit-oriented autonomous vehicle operations, crowdshipping for urban logistics, and sustainable urban mobility solutions. Nassir's publications have garnered over 1983 citations according to Google Scholar. Notable works include 'Transit-oriented autonomous vehicle operation with integrated demand-supply interaction' (2018, Transportation Research Part C: Emerging Technologies, 201 citations), 'Transit stop-level origin–destination estimation through use of transit schedule and automated data collection system' (2011, Transportation Research Record, 192 citations), 'A utility-based travel impedance measure for public transit network accessibility' (2016, Transportation Research Part A: Policy and Practice, 160 citations), 'Activity detection and transfer identification for public transit fare card data' (2015, Transportation, 152 citations), and 'Intelligent vehicle pedestrian light (IVPL): A deep reinforcement learning approach for traffic signal control' (2023, Transportation Research Part C: Emerging Technologies, 104 citations). He contributes to projects such as AI optimisation for corridor performance, integrated connected data for traffic management, and predictive analytics for urban corridors. His work advances equitable, safe, and efficient transport systems, particularly integrating AI, machine learning, and multimodal considerations.