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Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (8): 1628-1635    DOI: 10.3785/j.issn.1008-973X.2024.08.010
    
Modeling low-carbon travel mode choice by incorporating carbon incentive latent variable
Yan HE1,2(),Yilin SUN1,3,4,*(),Zhijian ZHAO4,Yang SHU1
1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. Center for Balanced Architecture, Zhejiang University, Hangzhou 310000, China
3. The Architectural Design and Research Institute of Zhejiang University Limited Company, Hangzhou 310013, China
4. Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
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Abstract  

The latent variables affecting travelers’ intention to low-carbon travel were comprehensively considered based on the theory of planned behavior and the theory of value-belief-norm in order to analyze travelers’ low-carbon travel behaviors and internal mechanisms. A multi-cause and multi-indicator model was constructed by using the empirical data of Hangzhou, and the values of latent variables were calibrated. A hybrid choice model and a Logit model without latent variables were constructed to compare and analyze travelers’ weekday mode choice behavior. A binary hybrid choice model was constructed to analyze the travel behavior shift of high carbon people under carbon incentives. Results show that the latent variables such as attitude, perceived behavioral control, and view of incentive significantly affect the travelers' willingness to travel in a low-carbon way and the choice of travel modes. The hybrid choice model has a better goodness-of-fit than the Logit model without latent variables, and the prediction accuracy improves by 4.6%. 64.4% of the high-carbon individuals tends to choose low-carbon modes under carbon incentives, showing that the carbon incentives can effectively promote travel modes shift behavior. There is some heterogeneity of the variables attitude and travel time in travel mode shift behavior.



Key wordstransportation engineering      low-carbon travel      mode choice      carbon incentive      hybrid choice model     
Received: 19 July 2023      Published: 23 July 2024
CLC:  U 491  
Fund:  浙江省“尖兵”“领雁”研发攻关计划资助项目(2023C01240);国家自然科学基金资助项目(52131202).
Corresponding Authors: Yilin SUN     E-mail: yanhe@zju.edu.cn;yilinsun@zju.edu.cn
Cite this article:

Yan HE,Yilin SUN,Zhijian ZHAO,Yang SHU. Modeling low-carbon travel mode choice by incorporating carbon incentive latent variable. Journal of ZheJiang University (Engineering Science), 2024, 58(8): 1628-1635.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.08.010     OR     https://www.zjujournals.com/eng/Y2024/V58/I8/1628


融合碳激励潜变量的低碳出行方式选择模型

以计划行为理论与价值-信念-规范理论为理论基础,综合考虑影响出行者低碳出行意愿的潜变量,以分析出行者低碳出行行为及内在机理. 基于杭州市实证数据,建立多原因多指标模型,标定潜变量适配值. 构建混合选择模型与无潜变量的Logit模型,对比分析出行者工作日方式的选择行为. 建立二元混合选择模型,分析碳激励下高碳人群的出行行为转变. 结果表明,态度、知觉感知控制、激励态度等潜变量显著影响出行者的低碳出行意愿与出行方式选择,混合选择模型比无潜变量的Logit模型具有更好的拟合优度,预测精度提升了4.6%,碳激励下64.4%的高碳人群倾向于选择低碳出行,碳激励可以有效促进公众的出行方式转变,变量态度、出行时间在公众出行方式转变上存在一定的异质性.


关键词: 交通工程,  低碳出行,  方式选择,  碳激励,  混合选择模型 
Fig.1 Framework of hybrid choice model
潜变量题项观测变量文献
态度(AT)AT1我认为低碳出行是安全的文献[6]
AT2我认为低碳出行经济实惠
AT3我认为低碳出行方便
主观准则(SN)SN1我的朋友或同事鼓励我使用低碳出行文献[20]
SN2我的朋友或同事经常选择低碳出行
SN3我的家人经常选择低碳出行
知觉感知控制(PBC)PBC1我认为实施低碳出行行为并不困难文献[6]
PBC2实施低碳出行几乎不会耽误我的时间
责任意识(AR)AR1政府和企业有责任保护环境,降低空气污染文献[22]
AR2无论别人如何出行,环保价值观和责任引导我使用低碳出行
AR3在日常出行中,我有责任选择低碳出行
后果意识(AC)AC1低碳出行可以降低污染文献[23]
AC2低碳出行可以节约能源
AC3低碳出行可以缓解城市交通拥堵
AC4低碳出行可以保护环境
激励态度(VI)VI1若存在低碳出行奖励,未来的日常出行我愿意优先使用低碳出行
VI2我认为低碳出行奖励可以鼓励我在未来选择低碳出行
VI3我认为低碳出行奖励对于鼓励低碳出行是有必要的
新生态范式(NEP)NEP1使用新能源汽车出行可以节能减排文献[14]
NEP2车辆突然的加速或减速将增加能源消耗
NEP3全球变暖将增加极端天气出现的频率和强度
NEP4使用公共交通代替开车出行可以降低碳排放
出行意向(BI)BI1在日常出行中我有强烈的低碳出行意向文献[6]
BI2未来的日常出行中我愿意经常使用低碳出行
BI3未来的日常出行中我愿意推荐他人选择低碳出行
Tab.1 Latent variables scale
Fig.2 Distribution of socio-economic attributes
潜变量观测变量因子载荷Cronbach’s AlphaCRAVE
ATAT10.8370.8720.8720.695
AT20.846
AT30.818
SNSN10.7940.8810.8590.671
SN20.776
SN30.883
PBCPBC10.8690.8100.8130.686
PBC20.785
ARAR10.8760.8810.8830.715
AR20.802
AR30.857
ACAC10.8920.9260.9350.782
AC20.826
AC30.908
AC40.909
VIVI10.8380.8660.8700.691
VI20.856
VI30.799
NEPNEP10.8560.9050.9140.723
NEP20.813
NEP30.841
NEP40.900
BIBI10.7500.8110.8160.596
BI20.739
BI30.825
Tab.2 Sample reliability and convergent validity tests
Fig.3 Sample convergent validity test
出行方式Chi/DFRMSEATLICFI
小汽车1.4980.0530.9240.935
公共交通1.7270.0470.9580.964
非机动交通1.2070.0480.9060.919
Tab.3 Goodness-of-fit indicator for MIMIC
Fig.4 Result of MIMIC model for car
Fig.5 Result of MIMIC model for public transit
Fig.6 Result of MIMIC model for non-motorized transport
变量无潜变量方式选择模型混合选择模型
公共交通非机动交通公共交通非机动交通
常数项6.079***5.552***7.801***4.004*
30岁以下1.920**
45-60岁?2.142**?2.866***
拥有驾照?2.351***?1.570*?2.359***?1.640*
初中及以下1.289***3.501***1.520***3.681***
高中0.934**2.519***1.456***2.881***
家庭汽车数?1.581***?1.455**?1.656***?1.357**
家庭非机动车数0.434**0.509***0.564*
<9999元?2.619***?1.421**?2.703***?1.383*
10000~
19999元
?2.056***?1.232**?2.136***?1.201**
家庭人数3人?0.603*?0.690*
成年人家庭?2.112***?1.468**
出行时间?0.033***?0.122***?0.029***?0.121***
新生态范式?0.542***?0.539**
后果意识?0.493***
态度0.819***
责任意识?0.758**
出行意向0.546***1.005**
Cox–Snell’s R20.4800.520
McFadden’s R20.5600.607
Nagelkerke’s R20.3360.377
命中率69.8%74.4%
Tab.4 Parameter estimation result of mode choice model
Fig.7 Travel mode shifts under carbon incentives
变量系数OR
<9999元?2.852**0.058
10000~19999元?2.913***0.054
出行时间?0.240***0.786
知觉感知控制0.759**2.136
主观准则?1.751***0.174
激励态度0.990*2.691
出行意向0.942*2.565
有孩子家庭×态度0.461***1.586
出行时间×责任意识0.036*1.037
Cox–Snell’s R20.508
Nagelkerke’s R20.698
命中率88.1%
Tab.5 Estimation results of binary hybrid choice model
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