top of page

Research 

Dr. Bhowmik's research primarily focus on developing data-driven quantitative approaches including econometric models and optimization techniques to uncover causal relationships within the transportation field and address different transportation related issues. His research expertise encompasses transportation planning and safety, sustainable urban transportation, disaster management, energy demand analysis and adoption of emerging technologies and its' impact on transportation system. In his work, he tackled several methodological challenges within the transportation field. These include capturing population heterogeneity, examining the impact of common unobserved factors, effectively pooling data from various sources, spatio-temporal heterogeneity and addressing the dimensionality challenge inherent in transportation data. 

image.png

transportation data. In particular, Dr. Bhowmik has estimated and validated several novel mathematical frameworks including mixed effect model, panel mixed model, fractional split model, a combined analytical-simulation based approach within copula framework, mixed latent segmentation and multidimensional joint models to address several empirical issues within the transportation domain. 

Past Work

image.png
image.png

In road safety research, Dr. Bhowmik's work addressed several road safety issues including: a) crash frequency analysis; b) crash severity analysis and c) real time crash risk analysis. Specifically, his work focused on developing advanced econometric models for analyzing crash factors and frequency, including the fractional split model and copula model. In one of his work. he had simplified multivariate crash frequency models by addressing the address the dimensionality challenge in the traditional multivariate crash frequency models. He developed a unified framework that is not influenced by the number of dependent variables as opposed to the explosion in complexity with increasing dimensions in traditional models. Through his integrated approach, he analyzed different crash types and the corresponding crash severity simultaneously (resulting in total 24 dimensions) to gain a more accurate understanding of the safety dynamics. Additionally, he had pioneered innovative crash severity analysis, considering driver errors, crash types, vehicle damage, and injury severity within a unified framework, as well as high-resolution injury severity models based on medical professionals' assessments.  Click the images to see the paper.

image.png
image.png
bottom of page