Brief Detail: Dr Amir’s PhD thesis reviews the theoretical assumptions underlying blackspot identification in transportation research, identifies gaps in the literature, and proposes a multiple risk source methodology for detecting motor vehicle crash blackspots. The proposed approach involves decomposing the total crash count into its constituent components, separating the risk sources, and incorporating crash severity into the overall framework. The methodology is tested on a comprehensive dataset for state-controlled roads in Queensland, Australia, and found to outperform traditional approaches in terms of prediction performance and goodness of fit.

Dr Amir has been involved in road safety research for more than a decade and has published many peer-reviewed articles in the leading journals in transport engineering and social sciences, including Analytic Methods in Accident Research, Journal of Choice Modelling, Accident Analysis and Prevention, Travel Behaviour and Society, Sustainable Cities and Society, Traffic 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.

Dr Amir is an assistant professor of probabilistic methods in transport safety at the Delft University of Technology. His research interest is at the intersection of transport safety, behavioural modelling and data science. His expertise is in advanced statistical modelling techniques, latent variable and structural equation models, generalised linear models, discrete choice models, and Bayesian hierarchical models.

Google Scholar: https://scholar.google.com/citations?user=0LWSBv0AAAAJ&hl=en&oi=ao