Forecasting Travel Demand When the Explanatory Variables Are Highly Correlated
- 1 April 1980
- journal article
- other
- Published by SAGE Publications in Journal of Travel Research
- Vol. 18 (4) , 31-34
- https://doi.org/10.1177/004728758001800405
Abstract
This paper discusses the problem of multicollinearity among explanatory variables commonly encountered in travel demand forecasting by using ridge regression. The authors demonstrate that when severe multicollinearity exists and the pattern of collinearity among regressors changes over time, ridge regression models yield forecasts with significantly lower forecast error than ordinary least squares models.Keywords
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