CAJ | 학술논문

汽车保有量预测对城市交通的发展方向、城市交通的控制管理、城市道路的建设情况等都有直接的参考意义。本文通过分析影响城市汽车保有量的因素，通过参考部分参考文献，城区人口总数人均GDP、公路客运量等8个指标，首先采用主成分分析法将8个因素进行分析，然后建立BP神经网络模型对湖南省2006到2008年汽车保有量进行预测，预测结果分别为98．93万辆、122．18万辆、137．03万辆，与汽车保有量实际值94．64万辆、121．72万辆、142．67万辆很接近，预测精度比较高。这表明BP神经网络具有很强的学习与泛化能力，用于汽车保有量预测的可行性与有效性。
기차보유량예측대성시교통적발전방향、성시교통적공제관리、성시도로적건설정황등도유직접적삼고의의。본문통과분석영향성시기차보유량적인소，통과삼고부분삼고문헌，성구인구총수인균GDP、공로객운량등8개지표，수선채용주성분분석법장8개인소진행분석，연후건립BP신경망락모형대호남성2006도2008년기차보유량진행예측，예측결과분별위98．93만량、122．18만량、137．03만량，여기차보유량실제치94．64만량、121．72만량、142．67만량흔접근，예측정도비교고。저표명BP신경망락구유흔강적학습여범화능력，용우기차보유량예측적가행성여유효성。
Prediction of car ownership has a direct reference significance for the the development of urban transportation and con struction of urban roads. By analyzing the impact factors of urban auto possession,this paper first analyzes 8 indicators such as urban population,GDP, road passenger traffic and so on determined by some references, then establish BP neural network model to predicts the vehicles possession in Hunan Province from 2006 to 2008. The figures of prediction is 989,300, 1,221,800 and 1,370,300 respectively in 2006, 2007 and 2008, which is very close to the real ownership of 946,400,1,217,200 and 1,426, 700 respectively. It shows the prediction is very accurate. This suggests that the BP neural network has very strong learning and generalization ability and can be employed in prediction of vehicle possession effectively.