The three major techniques used for Trip Generation Analysis are Cross-Classification, Multiple Regression Analysis, and Experience Based Analysis. Cross-Classification procedures measure the changes in one variable (trips) when other variables (land use etc.) are accounted for. Cross-Classification resembles multiple regression techniques. Cross-Classification is essentially non-parametric, since no account is taken of the distribution of the individual values. One problem with the Cross-Classification technique is that the "independent" variables may not be truly independent, and the resultant relationships and predictions may well be invalid.
The FHWA Trip Production Model uses Cross-Classification and has the following sub-models.
A considerable amount of research and development has focused on the area of disaggregate models for improved travel demand forecasting. The difference between the aggregate and disaggregate techniques is mainly in the data efficiency. Aggregate models are usually based upon home interview origin and destination data that has been aggregated into zones; then the "average" zonal productions and attractions are derived. The disaggregate approach is based on large samples of household types and travel behaviors and uses data directly. There are savings in the amount of data required and some of the data can be transferred to other applications. The disaggregate approach expresses non-linear relationships and is more easily understood. The tables shown below show several steps of a cross-classification analysis.
The above figures are from: Paul Wright, Highway Engineering, 6th ed. Wiley, 1996.pp55, 56, and 58