The Methodology And Implementation Of Car Ownerships Forecast Technique

With the rapid development of China's economy steady, the cars got into Chinese families, began to spread all over our country. The rapid growth of car ownership as a double-edged sword, on the one hand, the expansion of domestic demand and promote economic development and improving living standards, changes in other aspects of urban spatial structure have played a positive role; the other hand, car ownership rapid growth has also brought the city traffic congestion, energy shortage, environmental pollution, and many other negative effects. Therefore, in order to protect the social and economic sustainable development, in-depth study of the growth in car ownership rules, forecast the development trend of car ownership is essential.Forecast the development of understanding of things changes in the laws, based on its rule modeling, analog now infer the future. Currently, people used the time series, neural network, gray prediction, Logistic curve prediction method of car ownership, most of the data based only on data projections, the growth of car ownership less understanding of the laws, and its explanation does not forecast the results obtained strong, practical poor.Thus, this paper was to explore time series, regression analysis and other traditional prediction methods, summarizes the advantages and disadvantages of these methods and the applicability of vehicle population forecast. Car then "return" to the consumer durables category, and analysis of reference of refrigerators, washing machines, bicycles and other consumer durables growth in ownership rules, rules are abstracted reference for prediction of automobiles; longer summary of the growth of car ownership in countries Law and the analysis of the characteristics of automobile ownership and use; and then create the income distribution function, auto demand coefficient, the resistance function of car ownership (car critical point), composed of car ownership forecasting model; Finally, the prediction model built Comment factors relevant policies and, in response to car ownership trends in the future development of reference.