Dr. A.P. (Amir Pooyan) Afghari

Dr. A.P. (Amir Pooyan) Afghari



Amir is an assistant professor (tenure-track) of probabilistic methods for transport safety at the Safety and Security Science section of the Technology, Policy, and Management faculty at TU Delft. His research interest is at the intersection of transport engineering, behavioural modelling and data science: understanding and analyzing human behaviour in transport domain using observational data, statistical and econometric methods and machine learning algorithms.

Prior to joining TU Delft, Amir worked at several research centres around the world, including Queensland University of Technology (Australia), University of Queensland (Australia), McGill University (Canada), École Polytechnique Montréal (Canada), and Concordia University (Canada). He completed his PhD in road safety at the University of Queensland in Brisbane, Australia.


Amir's expertise is in advanced statistical modelling such as likelihood-based structural equation modelsgeneralized linear modelsdiscrete choice models, and Bayesian hierarchical models.  

He has been involved in global road safety research for more than a decade, and has published many peer-reviewed articles in the leading journals in transport engineering and social science including Analytic Methods in Accident ResearchJournal of Choice ModellingAccident Analysis and PreventionTravel Behaviour and SocietySustainable Cities and SocietyTraffic Injury Prevention and Transportation Research Records. He has also contributed to a textbook on safe mobility, co-authored by the world’s leading academics in the field of road safety.

Some examples of Amir’s research in transport science include: sleepiness and driving performance, road users’ receptivity towards autonomous vehicles, vehicle occupants’ seatbelt use behaviour, distracted drivers’ risk-compensating behaviour, missing data analysis in transport safety, Bayesian inference in transport safety, analysis of crash data with excess zero observations, crash blackspot identification, bicycle sharing systems, and automated data analytics for vulnerable road users.


Amir’s research received international recognition in 2016, when his fundamental work on crash blackspot identification was selected to receive the Outstanding Paper Award by the Transportation Research Board (TRB) committee on Safety Data, Analysis and Evaluation (ANB20) in the United States. 

Prospective students

I am always looking for enthusiastic students who are passionate about learning and are interested in critical thinking. Please contact me only if you are interested in and devoted to doing a PhD related to transport safety. Before contacting me, please have a look on my recent articles to see if you like the type of research I am doing. 

Econometrics codes in R

Please use this link to access my GitHub repository, and the codes I have developed for advanced statistical and econometrics models in my work:


These codes have been produced as part of my research during my academic career. Please cite the corresponding articles if you use them in any kind.

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