Precisely forecasting the growth passcnger cars usually plays an important role on policy planning in the t1'ansportation sector. Other public sectors may also need such forecasted data to develop related policies such as national energy poliey land-use policy, and vehicle industry development. This paper constructs a multple regression model to forecast the growth of passenger cars in Taiwan Area. Time series data frorn 1952 through 1987 including the nurnber of passenger cars and the socio-economic factors affecting the ownership of passenger cars are used to calibrate this model. Details of the model construction procedures are described, including priori causal relationship analysis, function form identification. correlation analysis, model estimation, homoscedasticity test, and autocorrelation test. A BLUE forecasting model is finally obtained in which four explanatory variables-length of paved highway, car purchasing ability (GNP/car price), car tariffs. and expectance of requiring private parking space are included. It was found that the number of passenger cars and each of the explanatory variables are nonlinearly related. Furthemore, anther forecast model is built based on the stepwise regression technique in conjunction with priori knowledge. In order to justify the forecasting power. the true values of car number of 1988 and 1989 are used to compare with the theoretical ones forecasted by these two models and by other previously developed models. It shows that these two models are much better than others in terms of the forecasted erros.