基于模糊聚类与主成分分析的网约车司机综合服务水平分析

H. Chen, Chan Li
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引用次数: 2

摘要

网约车旅游是日常旅游的重要方式之一。运营商对司机服务质量的评价方法单一,缺乏从订单活动满意度的多个维度对服务质量进行综合研究,对乘客的安全和权利造成了高度的隐患。本文提出了一种改进的主成分分析方法,即基于主成分分析的模糊c均值聚类(FCM-PCA)。实验表明,在目标对象评价研究中,可以将目标样本的主成分值和主成分得分作为新的聚类指标,从而在减少信息损失的基础上提高高维数据聚类效率。本研究为重要服务要素的选择提供了思路,为综合分析不同司机服务水平提供了研究方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comprehensive Service Level Analysis of Online Taxi Drivers Based on Fuzzy Clustering Combined with Principal Component Analysis
Online taxi tourism is one of the important ways of daily tourism. The operator carries out a single evaluation method for the driver's service quality, lacking a comprehensive study of service quality from multiple dimensions of order activity satisfaction, resulting in a high degree of hidden danger to passenger safety and rights. In this paper, an improved principal component analysis (PCA) method, namely Fuzzy C-Mean Clustering (FCM-PCA) based on PCA, is proposed. Experiments show that in the research of target object evaluation, the principal component values and principal component scores of target samples can be used as new indicators for clustering, so as to improve the efficiency of high-dimensional data clustering on the basis of reducing information loss. This study provides a way of thinking for the selection of important service components and a research method for the comprehensive analysis of different drivers' service levels.
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