摘要
为了提高出行者选择行为的预测准确度,分别构建了城市轨道交通客流转移预测的NL模型(Nested Logit model)及ML模型(Mixed Logit model),并综合考虑交通信息、出行选择习惯及交通环境等因素对客流转移的影响,从拟合优度比和信息准则2个方面对模型进行检验。结果表明,考虑出行者异质性的ML模型能更好地反映客流向城市轨道交通转移行为,在自变量相同的情况下,ML模型比NL模型具有更好的解释能力;ML模型能够反映出个体选择喜好的随机特性;交通信息、交通环境及居民出行选择习惯等因素显著影响出行者向城市轨道交通转移的选择。
In order to make precise prediction on traveler′s choice behavior,a Nested Logit model and a Mixed Logit model are presented to forecast the passenger flow shift caused by newly built urban rail transit separately.Taking traffic information,traffic environment and residents′travel habits into full account,two models are tested from aspects of goodness of fit ratio and information criterion.Results show that the ML model outperforms the NL model in urban rail transit passenger flow shift forecast with same independent variables in regarding the traveler′s heterogeneity;the ML model could reflect the random characteristics of individual preference;and the traffic information,traffic environment and residents′travel habits usually have significant effect on passenger flow shift to urban rail transit.
作者
王立晓
曹建青
左志
孙小慧
WANG Lixiao;CAO Jianqing;ZUO Zhi;SUN Xiaohui(Architecture and Civil Engineering College,Xinjiang University,830047,Urumqi,China)
出处
《城市轨道交通研究》
北大核心
2018年第9期75-79,83,共6页
Urban Mass Transit
基金
国家自然科学基金(71861032)
