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Commodity-based and vehicle-trip-based freight demand modeling is discussed. The characteristics of the trip length distributions (TLDs) are examined, defined in terms of tons, as required in commodity-based modeling, and in vehicle trips, as required in trip-based modeling. With data used from a major transportation study in Guatemala, the TLDs are estimated for both tons and vehicle trips. The analysis revealed that ( a) the shape of the TLDs depends upon the type of movements being considered; ( b) TLDs defined in terms of tonnage differ significantly from those defined in terms of vehicle trips; ( c) TLDs for different types of vehicles, transporting similar commodities, reflect the range of use of each type of vehicle; ( d) though tons TLDs and vehicle TLDs are different, the relationship between them seems to follow a systematic pattern that, if successfully identified, would enable transportation planners to estimate one type of TLD given the other; and ( e) major freight generators affect the shape of the TLDs, so complementary models may be needed to provide meaningful depictions of freight movements.

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Implications of modeling commercial vehicle empty trips are discussed, a theoretical derivation for parameter estimation is provided, and insight is given into the order of magnitude of estimation errors because of the improper modeling of commercial vehicle empty trips. A set of relatively simple cases was designed to illustrate the most important implications. Also addressed are estimation errors from using naïve approaches to compensate for the lack of explicit modeling of empty trips and the errors associated with more advanced empty trip models. In the simplest simulation, directional errors for a basic complementary model were from three to six times fewer than those for the naïve models. In the more complex case, a more sophisticated complementary model performed slightly better than the basic model and both complementary models were considerably better than the naïve approaches. The directional errors for the naïve models were four to seven times greater than those for the complementary models. Moreover, an analysis of the statistical distributions of the errors indicated that the complementary models had higher probabilities of producing accurate results, whereas the naïve approaches had higher probabilities of producing very large errors. These analyses indicate that the naïve approaches translate into significant errors in directional-traffic estimates. For that reason, their use should be discontinued in favor of the more advanced models presented.