Editing Trip distribution (section)

== Issues ==

=== Congestion ===

One of the key drawbacks to the application of many early models was the inability to take account of congested travel time on the road network in determining the probability of making a trip between two locations. Although Wohl noted as early as 1963 research into the feedback mechanism or the “interdependencies among assigned or distributed volume, travel time (or travel ‘resistance’) and route or system capacity”, this work has yet to be widely adopted with rigorous tests of convergence, or with a so-called “equilibrium” or “combined” solution (Boyce et al. 1994). Haney (1972) suggests internal assumptions about travel time used to develop demand should be consistent with the output travel times of the route assignment of that demand. While small methodological inconsistencies are necessarily a problem for estimating base year conditions, forecasting becomes even more tenuous without an understanding of the feedback between supply and demand. Initially heuristic methods were developed by Irwin and Von Cube <ref> Florian M., Nguyen S., and Ferland J. 1975 On the Combined Distribution-Assignment of Traffic", Transportation Science, Vol. 9, pp.&nbsp;43&ndash;53, 1975</ref> and others, and later formal mathematical programming techniques were established by Suzanne Evans.<ref>* Evans, Suzanne P. 1976 . Derivation and Analysis of Some Models for Combining Trip Distribution and Assignment. Transportation Research, Vol. 10, PP 37&ndash;57 1976</ref> 

=== Stability of travel times ===
A key point in analyzing feedback is the finding in earlier research<ref> Levinson, D. and A. Kumar 1994 The Rational Locator: Why Travel Times Have Remained Stable, Journal of the American Planning Association, 60:3 319&ndash;332</ref> that commuting times have remained stable over the past thirty years in the Washington Metropolitan Region, despite significant changes in household income, land use pattern, family structure, and labor force participation. Similar results have been found in the Twin Cities<ref>Barnes, G. and Davis, G. 2000. ''Understanding Urban Travel Demand: Problems, Solutions, and the Role of Forecasting'', University of Minnesota Center for Transportation Studies: Transportation and Regional Growth Study</ref>

The stability of travel times and distribution curves over the past three decades{{When|date=May 2016}} gives a good basis for the application of aggregate trip distribution models for relatively long term forecasting. This is not to suggest that there exists a constant [[Marchetti's constant|travel time budget]].