Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (11): 2607-2617.doi: 10.13229/j.cnki.jdxbgxb20210388

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Urban residents' low⁃carbon travel intention after implementation of driving restriction policy

Zhuang-lin MA1(),Shan-shan CUI2,Da-wei HU1   

  1. 1.College of Transportation Engineering，Chang'an University，Xi'an 710064，China
    2.Shandong Provincial Communications Planning and Design Institute Group Co. ，Ltd. ，Jinan 250031，China
  • Received:2021-04-30 Online:2022-11-01 Published:2022-11-16

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Abstract:

To explore the influencing factors and interaction mechanism of urban residents' low-carbon travel intention after implementing the driving restriction policy， the multiple indicators multiple cause （MIMIC） model is developed to construct urban residents' low-carbon travel intention model under the driving restriction policy， concerning the theory of planned behavior and technology acceptance model. The revealed preference（RP） survey was used to design the questionnaire， and 637 valid samples was obtained through network survey and field survey. Finally， the empirical test was conducted by the MIMIC model. The results show that policy attitude， policy effect perception and problem perception have a direct and significant impact on travel intention. Policy attitude and policy effect perception are mediating variables， and policy effect perception and subjective norms could indirectly affect travel intention through policy attitude， and problem perception could indirectly affect travel intention through policy effect perception. The total effect value of potential variables on urban residents' low-carbon travel intention after implementing the driving restriction policy rank from large to small as policy effect perception， policy attitude， problem perception， and subjective norms. Socio-economic attributes are moderating variables， which can indirectly affect travel intention by adjusting travelers' psychological perception factors. The research conclusions could provide the theoretical support for traffic management authorities to improve the efficiency of driving restriction policy and guide residents to low-carbon travel.

Key words: engineering of communications and transportation system, driving restriction policy, multiple indicators multiple cause model, travel intention, theory of planned behavior, technology acceptance model

CLC Number: 

  • U491

Fig.1

Framework of TPB"

Fig.2

Framework of TAM"

Fig.3

Framework of hypothesis model"

Fig.4

MIMIC model of urban residents' travel intention after implementation of driving restriction policy"

Table 1

Description of variable"

类别变量代码定义
问题感知对空气污染问题的感知PS1

非常不严重=1，较不严重=2，一般=3，

较严重=4，非常严重=5

对交通拥堵问题的感知PS2
政策效果感知对限行在缓解交通拥堵方面的效果感知PE1

没有效果=1，效果不明显=2，一般=3，

略有效果=4，效果显著=5

对限行在缓解空气污染方面的效果感知PE2
对限行在未来缓解交通拥堵方面的效果感知PE3
对限行在未来缓解空气污染方面的效果感知PE4
对限行收益损失比的感知PE5

损失非常多=1，损失多=2，中立=3

收益多=4，收益非常多=5

主观规范身边的家人和朋友对限行政策的接受度SN1

非常不支持=1，比较不支持=2，中立=3，

比较支持=4，非常支持=5

身边的家人和朋友是否支持你接受限行政策SN2
政策认知对限行政策的了解情况PK

非常不了解=1，不了解=2，

一般=3，了解=4，非常了解=5

政策态度对限行政策的接受度PA

非常不支持=1，比较不支持=2，中立=3，

比较支持=4，非常支持=5

出行意向日常出行中更倾向于选择低碳环保的出行方式TI一定不=1，几乎不=2，偶尔=3，经常=4，一定=5
社会经济属性性别xsex女性=1，男性=0
年龄（＜25岁）xage1＜25岁=1，否则=0
年龄（25~35岁）*xage225~35岁=1，否则=0
年龄（36~45岁）xage336~45岁=1，否则=0
年龄（45岁以上）xage445岁以上=1，否则=0
学历xedu高中及以下=1，本科及以上=0
职业xjob固定职业=1，自由职业=0
低收入（＜5000元）xinc1＜5000元=1，否则=0
中收入（5000~15 000元）xinc25000~15 000元=1，否则=0
高收入（＞15 000元）*xinc3＞15 000元=1，否则=0
是否接送孩子xpic是=1，否=0
私家车拥有量xcar2辆及以上=1，不多于1辆=0

Fig.5

Sample characteristics of the respondents"

Table 2

Results of reliability and validity test"

潜在变量题项KMO检验Bartlett球形度检验Cronbach's αAVECR
问题感知PS10.747<0.0010.6580.5100.661
PS2
效果感知EP10.747<0.0010.7820.5300.796
EP2
EP3
EP4
EP5
主观规范SN10.747<0.0010.7240.6880.763
SN2
政策认知PK0.747<0.0011.0001.0001.000
政策态度PA1.0001.0001.000
出行意图TI1.0001.0001.000

Table 3

Test of goodness-of-fit"

拟合指标χ2/dfRMSEAGFITLI
拟合结果值2.4800.0480.9110.918
拟合成功建议值2~5之间，越小越好<0.08>0.90>0.90

Fig.6

Path relationships between latent variables"

Table 4

Effect values between the latent variables"

项目直接效应间接效应总效应
政策态度→出行意向0.535-0.535
问题感知→政策态度--0.288-0.288
问题感知→出行意向0.655-0.3150.340
政策效果感知→政策态度0.502-0.502
政策效果感知→出行意向0.2810.2690.550
主观规范→政策态度0.195-0.195
主观规范→出行意向-0.1040.104
问题感知→政策效果感知-0.573--0.573

Fig.7

Path relationship of latent factors and causal variable"

1 Grange L D, Troncoso R. Impacts of vehicle restrictions on urban transport flows: the case of Santiago, Chile[J]. Transport Policy, 2011, 18(6): 862-869.
2 Yang Y L, Guo Y Y, Luo S Q. Consumers' intention and cognition for low-carbon behavior: a case study of Hangzhou in China[J]. Energies, 2020, 13(21): 1-19.
3 Fishbein M, Belief Ajzen I., Attitude, Intention and Behavior : an Introduction to Theory and Research[M]. Massachusetts: Addison-Wesley Publishing Company, 1975.
4 Hu X J, Wu N, Chen N. Young people's behavioral intentions towards low-carbon travel: extending the theory of planned behavior[J]. International Journal of Environmental Research and Public Health, 2021, 18(5): 18052327.
5 Schuitema G, Steg L, Forward S. Explaining differences in acceptability before and acceptance after the implementation of a congestion charge in Stockholm[J]. Transportation Research Part A: Policy and Practice, 2010, 44(2): 99-109.
6 Gu Z, Liu Z, Cheng Q, et al. Congestion pricing practices and public acceptance: a review of evidence[J]. Case Studies on Transport Policy, 2018, 6(1): 94-101.
7 Schade J, Schlag B. Acceptability of urban transport pricing strategies[J]. Transportation Research Part F: Traffic Psychology and Behavior, 2003, 6(1): 45-61.
8 Geng J C, Long R Y, Chen H, et al. Exploring the motivation-behavior gap in urban residents' green travel behavior: a theoretical and empirical study[J]. Resources Conservation & Recycling, 2017, 125: 282-292.
9 Vos J D, Witlox F. Travel satisfaction revisited, on the pivotal role of travel satisfaction in conceptualising a travel behaviour process[J]. Transportation Research Part A: Policy and Practice, 2017, 106: 364-373.
10 Gaunt M, Rye T, Allen S. Public acceptability of road user charging: the case of edinburgh and the 2005 referendum[J]. Transport Reviews, 2007, 27(1): 85-102.
11 Eriksson L, Garvill J, Nordlund A M. Acceptability of travel demand management measures: the importance of problem awareness, personal norm, freedom, and fairness[J]. Journal of Environmental Psychology, 2006, 26(1): 15-26.
12 Khademi E, Timmermans H. The long-term effectiveness of a reward scheme in changing daily travel choices[J]. Procedia Social & Behavioral Sciences, 2014, 111: 380-389.
13 Gärling T, Schuitema G. Travel demand management targeting reduced private car use: effectiveness, public acceptability and political feasibility[J]. Journal of Social Issues, 2010, 63(1): 139-153.
14 Jia N, Zhang Y D, He Z B, et al. Commuters' acceptance of and behavior reactions to license plate restriction policy: a case study of Tianjin, China[J]. Transportation Research Part D: Transport and Environment, 2016, 52: 428-440.
15 温旭丽, 杨悦, 叶欣. 大城市居民出行轨道交通选择意向研究[J]. 公路交通科技: 应用技术版, 2012(8): 356-359.
Wen Xu-li, Yang Yue, Ye Xin. Study on urban residents' choice of rail transit in big cities[J]. Journal of Highway and Transportation Search and Development(Application Technology Edition), 2012(8): 356-359.
16 颜冉, 储金龙, 杨新刚. 城市居民使用共享单车意向研究——以合肥为例[J]. 交通信息与安全, 2017, 35(6): 101-107, 114.
Yan Ran, Chu Jin-long, Yang Xin-gang. Intention of urban residents to use public bike sharing system: a case study in hefei[J]. Journal of Transport Information and Safety, 2017, 35(6): 101-107, 114.
17 Liu Y X, Hong Z S, Liu Y. Do driving restriction policies effectively motivate commuters to use public transportation?[J]. Energy Policy, 2016, 90: 253-261.
18 侯现耀,陈学武.基于态度的公交出行信息使用市场细分[J].吉林大学学报:工学版,2018,48(1): 98-104.
Hou Xian-yao, Chen Xue-wu. Use of public transit information market segmentation based on attitudinal factors[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(1): 98-104.
19 刘宇峰, 钱一之, 胡大伟, 等. 基于结构方程模型的不同规模城市居民出行方式选择影响因素的关联性分析[J]. 长安大学学报: 自然科学版, 2018, 38(5): 93-101.
Liu Yu-feng, Qian Yi-zhi, Hu Da-wei, et al. Correlation analysis of travel mode choice for urban residents in different urban size based on structural equation model[J]. Journal of Chang'an University(Natural Science Edition), 2018, 38(5): 93-101.
20 Bollen A. Structural Equations with Latent Variables[M]. New York: John Wiley & Sons Inc., 1989.
21 Allen J, Bellizzi M G, Eboli L, et al. Service quality in a mid-sized air terminal: a SEM-MIMIC ordinal probit accounting for travel, sociodemographic, and user-type heterogeneity[J]. Journal of Air Transport Management, 2020, 84: 101780.
22 Ingvardson J B, Nielsen O A. The relationship between norms, satisfaction and public transport use: a comparison across six european cities using structural equation modelling[J]. Transportation Research Part A: Policy and Practice, 2019, 126: 37-57.
23 舒诗楠, 边扬, 荣建, 等. 短距离下小汽车出行向自行车出行转移意向MIMIC模型[J]. 北京工业大学学报, 2019, 45(10): 998-1008.
Shu Shi-nan, Bian Yang, Rong Jian, et al. Transition from driving to bicycling in short-distance travel based on MIMIC model[J]. Journal of Beijing University of Technology, 2019, 45(10): 998-1008.
24 Zailani S, Iranmanesh M, Masron T A, et al. Is the intention to use public transport for different travel purposes determined by different factors?[J]. Transportation Research Part D: Transport and Environment, 2016, 49: 18-24.
25 胡松, 翁剑成, 周伟, 等. 基于扩展计划行为理论的公共交通出行依赖性影响[J]. 吉林大学学报: 工学版, 2022, 52(5): 1037-1044.
Hu Song, Weng Jian-cheng, Zhou Wei, et al. Influence of travelers' dependence on public transportation based on extended theory of planning behavior[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(5): 1037-1044.
26 Chen S Y. Green helpfulness or fun?Influences of green perceived value on the green loyalty of users and non-users of public bikes[J]. Transport Policy, 2016, 47: 149-159.
27 齐航, 夏嘉祺, 王光超, 等.考虑出行者习惯与利他性偏好的自动驾驶网约车使用意向模型[J]. 交通运输工程与信息学报, 2021, 19(2): 1-10.
Qi Hang, Xia Jia-qi, Wang Guang-chao, et al. A behavioral intention to use model of autonomous vehicle ride-hailing incorporating traveler habit and altruistic preference[J]. Journal of Transportation Engineering and Information, 2021, 19(2): 1-10.
28 王月辉,梁江伟,范实,等.基于技术接受模型(TAM)的网约车出行意向影响因素研究[C]∥第十九届中国管理科学学术年会, 南京,中国, 2017: 694-700.
29 Chen C F, Chao W H. Habitual or reasoned?using the theory of planned behavior, technology acceptance model, and habit to examine switching intentions toward public transit[J]. Transportation Research Part F: Traffic Psychology and Behavior, 2011, 14(2): 128-137.
30 Chen S Y. Using the sustainable modified TAM and TPB to analyze the effects of perceived green value on loyalty to a public bike system[J]. Transportation Research Part A: Policy and Practice, 2016, 88: 58-72.
31 Ajzen I. The theory of planned behavior[J]. Organizational Behavior and Human Decision Processes, 1991, 50(2): 179-211.
32 Davis F D, Bagozzi P R, Warshaw P. User acceptance of computer technology: a comparison of two theoretical models[J]. Management Science, 1989, 35(8): 982-1003.
33 Schlag B, Teubel U. Public acceptability of transport pricing[J]. IATSS Research, 1997, 21(2): 134-142.
34 罗艳菊, 黄宇, 毕华, 等. 基于环境态度的城市居民环境友好行为意向及认知差异——以海口市为例[J]. 人文地理, 2012, 27(5): 75-81.
Luo Yan-ju, Huang Yu, Bi Hua, et al. Difference in urban residents' pro-environmental behavior intention and understanding based on their environmental attitude a case study of haikou[J]. Human Geography, 2012, 27(5): 75-81.
35 Nico D, Yue B, Meng X, et al. Acceptability of a tradable driving credit scheme in the Netherlands and Beijing[J]. Case Studies on Transport Policy, 2018, 6(4): 499-509.
36 Rentziou A, Milioti C, Gkritza K, et al. Urban road pricing: modeling public acceptance[J]. Journal of Urban Planning & Development, 2011, 137(1): 56-64.
37 和占琼, 姜玉婷, 何明卫. 城市通勤者低碳出行选择研究——基于TPB与VBN整合模型[J]. 干旱区资源与环境, 2019, 33(4): 89-95.
He Zhan-qiong, Jiang Yu-ting, He Ming-wei. Study on urban low carbon travel choice based on TPB and VBN integration model[J]. Journal of Arid Land Resources and Environment, 2019, 33(4): 89-95.
38 吴文静, 景鹏, 贾洪飞, 等. 基于K均值聚类与随机森林算法的居民低碳出行意向数据挖掘[J]. 华南理工大学学报: 自然科学版, 2019, 47(7): 105-111.
Wu Wen-jing, Jing Peng, Jia Hong-fei, et al. Low carbon travel intention data mining for residents based on K-means clustering and random forest algorithm[J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(7): 105-111.
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