In this paper, we developed truck fuel consumption models for the particular assistance of professionals in charge of road project valuation in terms of predicting fuel used by trucks, which is an important topic on vehicle operating costs to be considered in the benefit–cost analysis of road projects. On the other hand, fuel consumption has a direct impact on emissions to the atmosphere, and thus future research can be conducted regarding estimations about emissions by trucks. In this research, we identified the effect of overall vehicle weight on truck fuel consumption in a free-flow regime. The methodology includes the design of experiments and factorial design as statistical techniques to obtain data, as well as linear and non-linear regressions to obtain models for two types of trucks: rigid (three axles) and articulated (six axles). Notably, there is no evidence of research previously conducted on the latter. We used statistical methods for the selection of trucks, equipment, road segments, and other aspects, obtaining good control in tests verifying the appropriate values for factors according to the planned ones. The results satisfy the expectations of the research, and it was demonstrated that the vehicle weight and roadway slope were significantly more important than speed alone, which was typically considered the main variable in other studies. On the other hand, longitudinal slopes higher than 5% were found to not be suitable for freight road corridors. It is recommended that 6-axle trucks instead of 3-axle trucks be used for a 16 t amount of cargo transported on a plain road (longitudinal slope under 3%). The HDM-4 model did not represent fuel consumption adequately for the current vehicle fleet operating on roads. Fuel consumption models must be updated, for instance, every 10 years, such that they can adapt to vehicle technological advances and the energetic improvement of fuels, including the proportion of biofuels and gas.

Characteristics and aspects to be considered in conducting household travel surveys (HTS) in developing countries are discussed. The main differences with the HTS conventional approach (interviewing people by telephone or mail) are highlighted. These topics are covered in the context of a case study that presents the HTS methodology used in the metropolitan area of Medellín, Colombia, in 2011 and 2012 with a sample of 20,000 face-to-face interviews. Results of the HTS are presented and analyzed for the case study. A low number of trips per inhabitant, 1.7, was found. A modal split is relevant in a city that restricts car use on the basis of license plate numbers during all weekday peak periods and that has a large number of taxis, nearly 8,000 per million inhabitants. Also shown are the difficulties that the survey interviewers experienced: planning process, accessibility to households owing to different social aspects (e.g., fear, social events, and international sporting events), and security concerns, among others. Finally, challenges for future modeling are presented.

Purpose: This study aims to analyze travel choices made by air transportation users in multi airport regions because it is a crucial component when planning passenger redistribution policies. The purpose of this study is to find a utility function which makes it possible to know the variables that influence users’ choice of the airports on routes to the main cities in the Colombian territory.Design/methodology/approach: This research generates a Multinomial Logit Model (MNL), which is based on the theory of maximizing utility, and it is based on the data obtained on revealed and stated preference surveys applied to users who reside in the metropolitan area of Aburrá Valley (Colombia). This zone is the only one in the Colombian territory which has two neighboring airports for domestic flights. The airports included in the modeling process were Enrique Olaya Herrera (EOH) Airport and José María Córdova (JMC) Airport. Several structure models were tested, and the MNL proved to be the most significant revealing the common variables that affect passenger airport choice include the airfare, the price to travel the airport, and the time to get to the airport.Findings and Originality/value: The airport choice model which was calibrated corresponds to a valid powerful tool used to calculate the probability of each analyzed airport of being chosen for domestic flights in the Colombian territory. This is done bearing in mind specific characteristic of each of the attributes contained in the utility function. In addition, these probabilities will be used to calculate future market shares of the two airports considered in this study, and this will be done generating a support tool for airport and airline marketing policies.

Seven national freight origin-destination samples collected in Colombia from 1999 to 2005 were used to conduct an empirical investigation of the temporal stability of parameters of freight demand models in the short to medium term. Freight generation, freight distribution, and empty trip models were considered. To identify time-dependent effects, models were estimated with a panel formulation with time-dependent parameters and fixed time effects and then compared with the corresponding cross-sectional models. The results indicate the presence of statistically significant time-dependent effects on all freight generation models (production and attraction), freight distribution models (based on both loaded vehicle trips and commodity flows), and empty trip models. A literature review indicates that few studies are available on the temporal stability of parameters. The results show a remarkably consistent pattern in that the components of freight demand that could change faster (i.e., freight production and attraction) are those that exhibit the largest rates of parameter change. The rates of change for these models are 18.29% and 26.37%, respectively. In contrast, the freight distribution models of loaded trips were found to change less rapidly (10.50% and 1.94%, respectively, depending on the impedance function), while the tonnage distribution model exhibited only fixed time effects. The model that changes least rapidly is the empty trip model, which has a rate of change of 0.83%.