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Freight transportation can be defined as the movement of goods and services to customers to obtain a monetary reward. Poor quality transport infrastructure implies higher travelling times and costs. This indirectly affects the productivity of a region since transportation costs are directly related to sales prices. Therefore, infrastructure investments become important for improving the competitiveness of a region. The problem with these investments is that they take time and require a large amount of money. Consequently, it is extremely important to prioritise this type of investment. This paper will first explain whether transportation investment or a sustainable transportation method affect the exported freight accessibility and if it also affects regional productivity using a linear regression model with the aid of a data-driven geographical information system. It uses spatial separation, gravity, and cumulative opportunity measures to calculate accessibility. Finally, the paper denotes which regions are highly affected by improvements in road, river, and railway networks using Colombia as a case study. The comparison considers travelling time and costs savings under each scenario. The results indicate that the gravity measure was the most appropriate accessibility measure for analysing the Gross Domestic Product (GDP). The scenario analyses suggest that zones farthest from the seaports are more sensitive to accessibility changes; consequently, they will receive higher improvements in their regional GDP with a national-level implementation of transport infrastructure investments. Thus, project prioritisation should be performed in regions where the investments lead to a decreased travel cost between regions and ports. [ABSTRACT FROM AUTHOR]

We revisit the practice of combining revealed (RP) and stated preference (SP) data (i.e., the data enrichment, DE, paradigm) in discrete choice models using secondary data obtained from emerging sources; these facilitate access to massive information about travel choices and can be used to improve transport models. Even though the benefits of the DE paradigm have been known for years, there is a large gap between the state of practice and the state of the art, particularly in Global South countries (but also in many industrialized nations). We use a SP dataset considering two new transport alternatives (train and metro) and a RP dataset based on a large mobility survey in Bogotá, Colombia, complemented with fairly precise level-of-service data obtained using GIS utilities and the Distance Matrix API by Google. Our results allow us to discuss good practice, identify barriers and challenges to the paradigm's application, and draw recommendations for forecasting the demand for new alternatives using joint RP and SP data. [ABSTRACT FROM AUTHOR]