基于关联分析的乘客公共交通依赖度识别方法

Identification Method of Passenger's Dependence Level of Public Transportation Based on Correlation Analysis

不同乘客在出行过程中对公共交通的依赖程度具有显著差异,精准识别乘客公共交通依赖度,有助于针对性地引导出行者向公共交通方式转移.本文基于多源数据的关联,获取包含个体属性的公共交通出行链,从出行行为和个体属性两方面提出8个依赖度指标,构建二阶聚类模型,识别乘客公共交通依赖度.结果表明:样本按依赖度高低被划分为4类群组;部分高、较高依赖度乘客在出行决策时受限于收入和车辆拥有量,并有向私家车出行转移的趋势;乘客出行习惯行为较个体属性对公共交通依赖度的影响更大.利用平均命中率(AHR)和平均覆盖率(ACR)指标评估个体属性对识别结果的影响,得出结论,个体属性指标间存在耦合关系,且指标缺失量与模型误差具有非线性关系.研究有助于理解公共交通乘客的需求和选择倾向性,为精准改善公共交通服务提供支撑.

There are significant variations in the dependence degree on public transportation(PT)for passengers in their long-term travel behavior.Accurate identification of the PT dependence level of passengers is conducive to promote the attractiveness of public transportation.The PT travel chain containing individual attributes is obtained from multi-source heterogeneous data,then 8 indicators are proposed to measure the travelers'PT dependence from the aspects of individual travel behaviors and attribute information.Thus,a two-step clustering model is constructed to identify the PT dependence level of passengers.The results show that the respondents are clustered into 4 groups according to the travel dependence levels of public transport.The respondents with the high PT dependence level are limited by their income and vehicle ownership,and there is a trend of transfer to private cars.In addition,travel habit behavior has a greater impact on the individual PT dependence level compared with the individual attributes of passengers.Finally,the evaluation indicators of AHR and ACR are used to further evaluate the impacts of individual attributes on recognition results.The results show that the individual attribute indicators have coupling effects on the model results,and the indicators missing quantity and model error own the nonlinear relationship.This study is conducive to better understanding the passengers'travel rules and demands,and provides support for the improvement of public transport services.