Abstract: The study of shipper behavior is essential for understanding mode choices within the context of national commodity flow analysis. Many shipment characteristics are important for behavioral analysis, yet obtaining relevant data remains a challenge for modeling. This thesis develops an interpretable mode choice model using the 2017 Commodity Flow Survey (CFS) Public Use File (PUF) data. A multinomial logit model is calibrated via maximum likelihood estimation. This structure represents shipment characteristics through indicators that influence the decision maker's utility.

The model is assessed across five major US freight corridors using standard performance measures, viz., confusion matrices, disaggregate accuracy, and out-of-sample errors. Evaluated model achieved about 80% disaggregate accuracy and a 1.0% aggregate RMSE while maintaining generality. Sensitivity checks confirm the stability of these parameters.

The proposed discrete-choice framework yields precise predictions and meaningful insights that reinforce applications in freight network analysis, demand planning, and policy interventions. This study provides a strong baseline for higher-resolution network data and advanced choice structures.

I am Mihir Vidyadhar Dharap, a graduate student in Civil and Environmental Engineering (Transportation) at Texas A&M University. My research focuses on freight transportation and shipper behavior, using national microdata from the 2017 Commodity Flow Survey. In this project, I develop logit-based mode choice models to better understand how shipment characteristics influence decisions among truck, rail, and multimodal options. My broader interests include freight planning, logistics systems, and data-driven decision-making for transportation. 

I work primarily in Python for data analysis, model development, and evaluation, and I have experience with GIS tools for spatial analysis and visualization. Through this poster, I share key findings, model limitations, and directions for improving freight mode-choice data and modeling. I am interested in opportunities in operations research and transportation systems analysis where I can apply large-scale data to real-world decision-making problems.