摘要
为探究后疫情时代居民出行方式选择行为,运用选择实验的方法,基于问卷调查获得选择行为数据,构建出行方式选择的混合Logit模型和潜在类别条件Logit模型。采用Stata软件标定模型参数,得到后疫情时代影响居民出行方式选择的主要因素。结果表明,两种模型均体现了个体出行方式选择的异质性,潜在类别条件Logit模型与混合Logit模型相比拟合优度提高了13%,预测精度提高了3.03%,为突发公共卫生事件下分析出行行为的个体异质性提供了一种有效工具。潜在类别条件Logit模型根据居民所处低、中风险区两种情景,分别将居民划分为4类、5类人群。从出行方式属性上看,等待时间和在途时间成为居民选择出行方式最重要的影响因素。从个人社会经济属性上看,在后疫情时代收入更高的女性更倾向选择私家车出行,年龄越大对行程费用越敏感,男性更愿意选择公交、地铁出行。
In order to explore the choice behavior of residents’ travel mode in the post-COVID-19 era,a choice behavior experiment was conducted.A mixed Logit model and a latent class conditional Logit model of travel mode choice were constructed based on the data obtained from questionnaire surveys.Stata software was used to calibrate the model parameters,and the main factors influencing residents’ travel mode choices were obtained.The results show that both models reflect the heterogeneity of individual travel mode choices.Compared with the mixed Logit model,the latent class conditional Logit model has an improvement of 13% in the goodness of fit and an increase of 3.03% in the prediction accuracy,which provides an effective tool for analyzing individual heterogeneity of travel behavior under public health emergencies.The latent class conditional Logit model divides residents into four and five groups according to the two scenarios of low and medium risk areas.From the perspective of travel mode attributes,the waiting time and the traveling time have become the most important influencing factors for residents to choose the travel modes.From the perspective of personal socio-economic attributes,women with higher incomes are more inclined to choose private cars to travel.The older are more sensitive to travel costs,and men are more willing to choose bus and subway travel.
作者
杨亚璪
唐浩冬
彭勇
YANG Ya-zao;TANG Hao-dong;PENG yong(School of Traffic&Transportation,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2022年第3期15-24,共10页
Journal of Transportation Systems Engineering and Information Technology
基金
教育部人文社科基金(17YJCZH220)
国家自然科学基金(61803057)
重庆交通大学研究生科研创新项目(2022S0037)。
