%0 Journal Article
%T 基于支持向量机的公交满意度特征分析<br>Analysis of Bus Satisfaction Characteristics Based on Support Vector Machine
%A 周正
%A 饶明华
%A 曹飞
%J Open Journal of Transportation Technologies
%P 336-341
%@ 2326-344X
%D 2020
%I Hans Publishing
%R 10.12677/OJTT.2020.94041
%X 公交满意度决定了公交的服务水平，是实施优先公交发展的保障。本文剖析影响公交满意度的众多因素，从个人属性、出行信息、公交信息化、配套设施和服务质量等五个方面出发，基于支持支持向量机，揭示它们与公交满意度之间关系，并计算这些影响因素的重要性程度。使用SPSS Modeler对南通调查数据进行整合，发现新城区和老城区的社会经济属性、出行目的和公交满意度具有显著地差异。最后，将支持向量机与Logistic回归分析模型相对比，发现支持向量机对于分类数据有较好的拟合能力，对样本分类的准确率也更高。<br />
Public transport satisfaction determines the service level of public transport, which is the guar-antee for the implementation of priority public transport development. This paper analyzes many factors that affect bus satisfaction, starting from personal attributes, travel information, bus informatization, supporting facilities and service quality, and based on support vector machine, reveals the relationship between them and bus satisfaction, and calculates the importance of these factors. SPSS Modeler was used to integrate the survey data of Nantong, and it was found that there were significant differences between the socio-economic attributes, travel purpose and bus satisfaction of the new and old urban areas. Finally, by comparing support vector machine with Logistic regression analysis model, it is found that support vector machine has better fitting ability for classification data and higher classification accuracy for samples.
%K 公交满意度，支持向量机，SPSS Modeler<br>Public Transport Satisfaction
%K Support Vector Machines
%K SPSS Modeler
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=36728