Comparison of techniques for estimating zonal trip productions and attractions
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Comparison of techniques for estimating zonal trip productions and attractions a supplement to the final report by Peter S. Parsonson

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Published by Highway Research Program, North Carolina State University at Raleigh in [Raleigh] .
Written in English



  • North Carolina,
  • Raleigh metropolitan area.


  • Origin and destination traffic surveys -- North Carolina -- Raleigh metropolitan area.

Book details:

Edition Notes

Statementby Peter S. Parsonson [and J. W. Horn], in cooperation with the North Carolina State Highway Commission and the United States Department of Commerce, Bureau of Public Roads.
ContributionsHorn, John W., joint author., North Carolina. State University, Raleigh. Dept. of Engineering Research.
LC ClassificationsHE372 .R34 1966 Suppl.
The Physical Object
Paginationx, 162 p. :
Number of Pages162
ID Numbers
Open LibraryOL4283127M
LC Control Number78309511

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  Trip Attractions The values for trip attraction rates for motorized trips, shown in Table , were used as a trip attraction model for Schultzville. Model 1 from this table was used for each trip purpose. An example calculation is provided for home-based   List of symbols Course Notes CT v List of symbols Utility theory P = probability N = utility of activity V = systematic utility component Z = disutility K = monetary budget T = time budget Trip generation models T = predicted number of zonal trips X = zonal explanatory variable Y = household explanatory variable Z = personal explanatory variable N = number of units (households or persons   Attractions • trip production models are more reliable than trip attraction • RESULT: force total trip attractions to equal total trip productions • Pi = trips produced by zone i • Ai = total trips attracted by zone i Matching Generations and Attractions (cont.) • The adjusting factor to adjust the attractions i Ai T f A Ai f adjusted i *   of Non-Residential Trip Geneat.;ion," by Thomas, Horton, and Dickey •II 'I, 'i' i dated November , discusses the findings of this research. Regression models for non-residential trip generation productions and attractions, as well as origins and destinations, were developed and analyzed in

Trip Attractions Accurately estimating trip attractions can be significantly more difficult and problematic than estimating trip produc- tions. Whether trip attraction model parameters are estimated from local data or are transferred, they are usually derived from household survey data, which collects travel information at the production end of The Indian freight transport market is steadily growing, with road-based freight transport constituting 63% of total freight volumes. The average spee    Trip Balancing Factors Balancing total trip productions and attractions provides an initial check on the quality of the socioeconomic data and the trip rates. The ratio of region-wide productions to attractions by trip purpose should be in the range of to 1. 10 prior to any ://   Trip productions and attractions may be estimated for up to 10 trip purposes and 9, zones. The program includes such features as user-specified trip production and attraction models, input of user-developed disaggregate data at the zone level, and/or the disaggregation of the zonal data using default models within the ://

  Generally speaking there are three methods to estimate trip generation– regression model, cross-classification and trip rates. Some transportation planning agencies use cross-classification models based on samples of household travel behavior data to estimate zonal trip productions, and they use regression models to estimate zonal trip;sequence=1.   This requires using the ITE trip generation rates to convert the socio-economic and land use information to zonal productions and zonal attractions. The trip generation rates published by ITE are expressed in the unit of vehicle trips for: (1) an average weekday, Saturday and Sunday, (2) weekday morning and evening peak hours of the generator   O. A. Nielsen, Chapter Two new methods for estimating trip matrices, in Travel Behaviour Research: Updating the State of Play, Elsevier Science Ltd, , pp. [15] E. Almasri and M. Al-Jazzar, TransCAD and GIS technique for estimating traffic demand and its application in Gaza City, Open Journal of Civil Engineering, vol. 3, pp micro level analysis, trip productions; attractions and impedance between the sub-zones are considered as inputs for the developed model. The outcome of the model is frequency of the tr ips