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 The following excerpt was taken from the Transportation Planning Handbook published in 1992 by the Institute of Transportation Engineers (pp. 112-114). Trip distribution models connect the trip origins and destination estimated by the trip generation models to create estimated trips. Different trip distribution models are developed for each of the trip purposes for which trip generation has been estimated. The trip distribution models found most often in practice today are "gravity models," so named because of their basis in Newton’s law. . . . The measure of separation between zones most commonly used for trip distribution is roadway travel time, calculated from the computerized transportation networks. Most transportation planning efforts use peak-period travel times as a measure of zonal separation for home-based work and home-based school models. . . . Recent studies have tried to incorporate travel cost and transit travel time into the separation measure. Cost has been considered in an attempt to estimate effects on trip distribution of parking costs, vehicle operating costs, and tolls. Logit Model Other trip distribution models that have been used include "opportunity" models and logit models, both of which estimate the probability that travelers will accept various destination options available. The logit formulation has recently been used for the Portland, Oregon metropolitan area. As shown in Figure 4.20, the probability of selecting a particular destination zone is based on the number of trip attractions estimated for that destination zone relative to the total attractions in all possible destination zones. The probability is applied to trip productions estimated for the origin zone, making it conceptually similar to the gravity model. Gravity model Those models generally estimate the distribution of trips to be proportional to the number of trip ends estimated by the trip generation models and inversely proportional to a measure of separation between the origin and destination zones. The gravity model has achieved virtually universal use because of its simplicity, its accuracy and due to its support from the U.S. Department of Transportation. . . . Developing a gravity model is a trial-and-error process that requires considerable care. This process, often called calibration, identifies the appropriate decay function or "friction factor", that represents the reluctance or impedance of persons to make trips of various durations or distances. . . . The adjustments are made incrementally with successive iterations of the model until the trip length frequency distribution produced by the model closely matches the frequency distribution from the travel survey or demonstrates an acceptable shape and average trip length. An important consideration in developing trip distribution models is "balancing" productions and attractions. One aspect of balance is to assure that the total productions equal the total attractions in the study area for each trip purpose. Deciding whether the productions or attractions should be the control total depends on whether there is greater confidence in the production (usually population) growth estimate or the attraction (usually employment) growth estimate. It is not unreasonable to average the two (production and attraction) trip estimates. The productions and/or attractions for all zones must then be factored so that their sum matches the control total. . . . (p. 114) At each iteration of the gravity model, the total trips attracted to each zone is adjusted so that the next iteration of the gravity model will send more or fewer trips to that attraction zone, depending on whether the immediately previous total trips attracted to that zone was lower or higher, respectively, than the trip attractions estimated by the trip generation model. . . . Any unacceptable difference between the generation and distribution model estimates after five iterations of the gravity model usually indicates an inconsistency in the assumptions or functions of the trip distribution model and the growth allocation model. One other consideration in developing a trip distribution model is how to handle unexplained and unacceptable differences between observed and estimated travel patterns. Rather than conduct extensive research to try to find an explanation for all such phenomena, the accepted practical approach is to factor the model estimates to match observed patterns. . . . With the gravity model, and often with other models in this situation, the adjustment factors are called "K" factors. The "K" factors are developed for individual trip interchanges and are assigned values that adjust the estimated trips for the interchanges of concern to match the observed values.
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