This paper presents a data analysis and modeling framework tailored to B2C and C2B goods transport demand, based on carrier data composed on the entire daily set of deliveries in a 9-months period. The proposed framework integrates two primary data sources. First, company delivery records provide detailed insights into demand patterns and organizational characteristics. Second, postal code registries enable the spatial aggregation of delivery addresses into zones, complemented by area-specific socioeconomic attributes. From these sources, a series of constructed variables is developed, primarily related to efficiency and accessibility, derived through combinations of existing variables. The analysis begins by examining a representative week to uncover temporal patterns, which is then extended to the entire 9-month period to identify seasonality, peak and off-peak dates, and other factors influencing demand intensity and modeling structure. Using dispersion and correlation analyses, followed by generalized linear regression techniques, key determinants of demand are identified, culminating in a robust commodity and trip generation model. The results, based on data from Seville (Spain), show carrier specificities, city specificities and general demand generation characteristics. The study also explores the implications of these models for public policy, particularly in demand management strategies for infrastructure utilization and access regulation. Additionally, the findings have practical applications in route optimization (vehicle routing) and urban traffic management, including freight vehicle flow estimation