Neema Nassir

Neema Nassir's picture
Senior Postdoctoral Associate

Neema is a Senior Postdoctoral Associate at MIT Transit Lab and Urban Mobility Lab and he serves in research, research supervision and lecturing roles in the area of urban transportation systems at MIT. He earned his PhD in Transportation Systems from University of Arizona (2013), and his BSc. and MSc. in Civil/Transportation Engineering from Sharif University of Technology, Tehran, Iran.

Neema's research is focused on the new generation of modeling tools and techniques, designed to understand, plan for and manage the urban transportation systems of the 21st century. There are two dominant streams in Neema’s research; 1) working with the emerging wealth of mobility sensing big data to capture the most accurate picture of mobility patterns, needs and preferences in our cities; and 2) simulating and exploring the new concepts, technologies and opportunities continuously introduced in transportation systems and widely welcomed in our societies, such as ride-hailing and ride-sharing services, autonomous and connected vehicles, and smart or personalized transit services.

In his doctoral dissertation research at UA, Neema developed innovative network modeling solution methods to optimize the traffic evacuation and intersection control plan for no-notice disasters in real-size networks. He did a PhD internship at San Francisco County Transportation Authority in 2012, where he explored utility-based accessibility measures for the SFCTA’s activity-based travel demand model. After his PhD studies, he went to Australia and joined the Centre for Transport Strategy at University of Queensland (UQ). His research at UQ was mainly focused on modeling the travel behavior of public transit passengers. He has developed advanced network modeling algorithms and statistical methods to model complex travel behaviors in dense and multimodal transit networks.

Research Projects: Transport for London-MIT PartnershipChicago Transit Authority-MIT Partnership

Research Interests: Automated Vehicle Analytics, Mobility Sensing and Prediction