A Mathematical Framework for Logit Model in Transportation Mode Choice Analysis

Traditional transportation demand forecasting has relied on massive zone-specific aggregations, which assume a linear demand increase.Such models may lack the flexibility needed to perform dynamic and context-sensitive analyses.Recently, disaggregated behavioral models have gained prominence for requiring less data and enabling sensitivity analyses in policy decisions.This study explores the feasibility of a model focusing on the mathematical formulation and validation of the transportation mode choice model.The study uses the logit model with a non-linear probability distribution function represented by a logistic curve and incorporates a linear combination of independent predictor variables.The mathematical model is examined for its ability to estimate choice probabilities.The methodology is formulated to be adaptable to diverse contexts that provide an analytical framework for transportation systems independently of geographic or demographic considerations.