The multidimensional nature of freight demand has given rise to two majormodeling platforms: vehicle-trip based and commodity based (cargo value is only used in Input-Output models). Vehicle-based models focus on modeling the actual number of vehicle trips, which has several advantages. Among them are the relative ease and high-quality with which traffic data can beobtained; and, since the model focuses on vehicle trips, no distinction is made between empty and loaded trips. A key limitation of vehicle-trip modes is that they cannot be applied to multimodal systems because the vehicle-trip is already the result of a mode choice that already took place. Furthermore since the models assume that the vehicle-trip is the unit of demand, as opposed to the commodity being transported, there is no way to consider the economic characteristics of the shipments. This is a rather serious limitation because the commodity type has been found to be a very important explanatory variable of a number of choice processes involving freight. Commodity based models, as the name points out, focus on modeling the flow of goods between zones (measured in a unit of weight). Since the cargo's weight is the unit of demand, the consideration of cargoes' attributes(e.g., value, weight, type) is straightforward. In this platform, the loaded trips are estimated by dividing the total flow from one region to the other by a suitable payload for all loaded trucks. The problem with commodity-based models is that they are unable to model empty trips, which can make up about 30 to 40 percent of the total trips in a region (Holguín-Veras and Thorson, 2003a). This occurs because the commodity flow in one direction determines the corresponding loaded trips, but does not bear a directrelationship with the empty trips. To resolve this, complementary empty trip models have been developed, such as Noortman and van Es' In this context, the empty trip models use the commodity flows estimated by a freight demand model as an input for the estimation of the corresponding empty trips. Having done that, the empty trips are added to the loaded trips to obtain the total vehicle trips that would be used in the traffic assignment process. Far from being of purely academic interest, the correct estimation of commercial vehicle empty trips is very important for transportation planning purposes because not doing correctly will lead to severe directionalerrors in the estimation of commercial vehicle traffic, as shown in. This, in turn, may have important implications in terms of determining road capacity improvement needs. The main objective of this paper is to contribute to freight transportation modeling by enhancing the methodologies used to estimate empty trips from previously estimated commodity flow matrices. The paper builds on the developments of The consideration of a variable p significantly improved the relative performance of the models. For each data subset, the relative error for the models with constant p was higher than that for the models with p as a function of either commodity flow or distance. For small trucks, the relative error for HVT 2, HVT 3, and HVT 4 ranged from 9.7% to 19.3% greater than that for the best variable p model. For large trucks, the relative error for the models with constant p rangedfrom 5.8% to 11.4% greater than that for the best variable p model, Similarly, for semi-trailers, the relative error for the models with constant pranged from 4.2% to 6.7% greater than that for the best variable p model.In spite of the acknowledged limitations of the work, it is clear that considering variable p functions holds the potential to significantly improve the performance of empty trips models, which would hopefully facilitate the development of new paradigms of freight transportation modeling. For the covering abstract see ITRD E135582.
Abstract