Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (1): 158-169.doi: 10.13229/j.cnki.jdxbgxb.20240661

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Heterogeneity analysis of residents’ transfer intentions under transit transfer preferential policy

Zhuang-lin MA1(),Yu-ming BI2,Bei ZHOU1(),Ya-juan DENG1,Xue ZHAO3   

  1. 1.School of Transportation Engineering，Chang'an University，Xi'an 710064，China
    2.BYD Automotive Industry Co. ，Ltd. ，Shenzhen 518118，China
    3.School of Science，Xi'an Shiyou University，Xi'an 710065，China
  • Received:2024-06-14 Online:2026-01-01 Published:2026-02-03
  • Contact: Bei ZHOU E-mail:zhuanglinma@chd.edu.cn;bzhou3@chd.edu.cn

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Abstract:

In order to investigate the factors influencing urban residents’ transfer intention and their interaction mechanisms under the transit transfer preferential （TTP） policy， this paper integrates behavioral habits based on the theory of planned behavior （TPB） and technology acceptance model （TAM）， and develops a residents' transfer intention model under the TTP using the structural equation model（SEM）. The questionnaire was designed using the revealed preference（RP） method. A total of 365 valid samples were obtained through an online survey. The path relationships between psychological latent variables were explored， and the mediating effect of the model was examined using the Bootstrap method. Additionally， a one-way analysis of variance（ANOVA） was conducted to investigate the heterogeneity of latent variables across different groups. The results show that attitude， subjective norms， perceived usefulness， and behavioral habits have a direct significant impact on transfer intention. Attitude， perceived usefulness， and perceived ease of use are mediating variables， but they do not play a mediating effect in all paths. The total effect values of latent variables on transfer intention， in descending order， are behavioral habits （0.457）， perceived usefulness （0.366）， attitude （0.326）， subjective norms （0.312）， and perceived ease to use （0.096）. Significant differences were found between groups with different socio-economic attributes and travel characteristics on the latent variables. The findings of this study not only contribute to the theoretical framework of behavioral intention research， but also provide theoretical support for urban transportation management authorities in formulating the TTP policy.

Key words: engineering of communications and transportation system, transit transfer preferential policy, structural equation modeling, transfer intention, theory of planned behavior, technology acceptance model, behavioral habits

CLC Number: 

  • U491

Fig.1

Theoretical model framework"

Fig.2

Hypothetical relationships for resident transfer intentions"

Table 1

Questions items for psychological latent variables"

潜变量题项
态度（ATT）ATT1： 公交换乘优惠政策是一个好主意
ATT2： 公交换乘优惠政策很有吸引力
ATT3： 支持公交换乘优惠政策
主观规范（SN）SN1： 家人和朋友的鼓励会让我更愿意接受
SN2： 如果周围的人接受换乘优惠政策，我也会接受
SN3： 政府的引导会让我愿意接受换乘优惠政策

感知行为控制

（PBC）

PBC1： 如果我几乎不换乘，我不会支持公交换乘优惠政策
PBC2： 换乘距离过长，我不会支持换乘优惠政策
PBC3： 优惠幅度越大，我会接受换乘优惠政策
感知有用性（PU）PU1： 公交换乘优惠政策可以降低出行费用
PU2： 公交换乘优惠政策可以减少出行时间
PU3： 公交换乘优惠政策可以提高出行便捷性

感知易用性

（PEU）

PEU1： 我认为公交换乘很便捷
PEU2： 我认为公交换乘信息很容易获取
PEU3： 我可以熟练地进行公交换乘
行为习惯（BH）BH1： 我能熟练使用各种公共交通工具
BH2： 公共交通是我日常出行的常用方式之一
换乘意向（TI）TI1： 我在日常生活中经常进行公交换乘
TI2： 我会在日常生活中推荐他人选择公交换乘

Table 2

Results of reliability and convergent validity test"

潜变量名称Cronbach's αAVECR
态度0.8790.7100.880
主观规范0.8780.7160.883
感知行为控制0.8960.7430.897
感知有用性0.8960.7410.896
感知易用性0.8740.6970.873
行为习惯0.8700.7720.871
换乘意向0.8810.7880.881

Fig.3

Results of discriminant validity test"

Table 3

Results of model fitting test"

类别检验指标接受范围拟合值效果
绝对适配统计量χ2/df1<χ2/df <31.292较好
SRMRSRMR<0.050.041较好
RMSEARMSEA<0.050.028较好
GFIGFI>0.90.953较好
增值适配度统计量NFINFI>0.90.963较好
TLITLI>0.90.989较好
IFIIFI>0.90.991较好
CFICFI>0.90.991较好
简约适配统计量CNCN>200341较好
PGFIPGFI>0.50.677较好

Fig.4

Influence paths between latent variables"

Table 4

Results of hypotheses test"

假设路径p值假设检验
H11： 态度→换乘意向***接受
H12： 感知行为控制→换乘意向0.298拒绝
H13： 主观规范→换乘意向**接受
H14： 感知有用性→换乘意向***接受
H15： 行为习惯→换乘意向**接受
H21： 感知行为控制→态度→换乘意向0.693拒绝
H22： 主观规范→态度→换乘意向***接受
H23： 主观规范→感知行为控制→换乘意向0.254拒绝
H24： 主观规范→感知有用性→换乘意向***接受
H25： 感知有用性→态度→换乘意向***接受
H26： 感知易用性→态度→换乘意向0.192拒绝
H27： 感知易用性→感知有用性→换乘意向***接受
H28： 行为习惯→态度→换乘意向***接受
H29： 行为习惯→感知行为控制→换乘意向0.485拒绝
H210： 行为习惯→感知易用性→换乘意向***接受
H211： 行为习惯→感知有用性→换乘意向***接受

Table 5

Results of mediating effects test"

路径效应值标准误临界比p值Lower 2.5%Upper 2.5%
感知行为控制→态度→换乘意向-0.0060.016-0.3530.724-0.0440.021
主观规范→态度→换乘意向0.0980.0402.4550.0140.0360.197
主观规范→感知有用性→态度→换乘意向0.0170.0101.6390.1010.0040.050
主观规范→感知有用性→换乘意向0.0570.0272.1320.0330.0180.127
感知有用性→态度→换乘意向0.0840.0402.1160.0340.0270.184
感知易用性→态度→换乘意向0.0270.0300.9080.364-0.0180.102
感知易用性→感知有用性→换乘意向0.0740.0372.0280.0430.0220.171
感知易用性→感知有用性→态度→换乘意向0.0220.0141.5910.1120.0050.064
行为习惯→态度→换乘意向0.1060.0452.3450.0190.0360.213
行为习惯→感知有用性→换乘意向0.0830.0352.3270.0200.0290.173
行为习惯→感知有用性→态度→换乘意向0.0240.0141.7720.0760.0070.066
行为习惯→感知易用性→态度→换乘意向0.0170.0200.8950.371-0.0110.069
行为习惯→感知易用性→感知有用性→换乘意向0.0480.0241.9760.0480.0150.117
行为习惯→感知易用性→感知有用性→态度→换乘意向0.0140.0091.5510.1210.0040.042

Table 6

Effect values of each latent variable on transfer intention"

路径直接效应间接效应总效应
态度→换乘意向0.326—0.326
主观规范→换乘意向0.1400.1720.312
感知有用性→换乘意向0.2820.0840.366
感知易用性→换乘意向—0.0960.096
行为习惯→换乘意向0.1820.2750.457

Table 7

Results of one-way ANOVA"

潜变量态度主观规范感知行为控制感知有用性感知易用性行为习惯换乘意向
性别0.644（***）0.527（***）0.0490.620（***）0.521（***）0.3610.644（***）
年龄0.0000.0400.569（0.463）0.058（***）0.000（***）0.0190.000
学历0.522（***）0.479（***）0.388（0.073）0.259（***）0.052（***）0.591（***）0.195（***）
职业0.0460.399（0.366）0.106（0.173）0.170（0.177）0.116（0.335）0.0350.002
月收入0.392（***）0.057（***）0.076（0.605）0.255（***）0.847（***）0.122（***）0.103（***）
私家车拥有量0.353（**）0.248（**）0.620（0.478）0.0030.706（**）0.0320.118（**）
是否有驾照0.893（***）0.820（***）0.652（0.874）0.110（***）0.239（***）0.352（***）0.050（***）
家庭结构0.214（0.079）0.322（**）0.050（0.237）0.842（*）0.100（0.594）0.340（*）0.086（**）
出行目的0.737（**）0.609（**）0.190（0.167）0.581（**）0.833（***）0.227（***）0.804（**）
出行时间0.347（**）0.121（***）0.167（0.128）0.801（***）0.574（***）0.098（***）0.995（***）
出行时长0.0000.0390.0350.0000.0000.0000.000
出行距离0.0320.475（***）0.085（0.515）0.363（***）0.961（***）0.486（***）0.243（***）
出行费用0.512（***）0.773（0.156）0.293（0.965）0.229（***）0.351（***）0.256（***）0.015
是否换乘0.328（0.692）0.497（0.154）0.077（0.233）0.687（0.735）0.566（0.386）0.297（0.903）0.524（0.722）
换乘方式0.255（0.730）0.642（0.545）0.257（0.658）0.675（0.476）0.324（0.562）0.178（0.227）0.697（0.641）
换乘距离0.117（0.768）0.197（0.185）0.049（0.685）0.541（0.878）0.910（0.797）0.822（0.988）0.125（0.697）

Fig.5

Comparison of means of latent variables for different groups"

[1] 中国政府网. 中共中央 国务院印发《交通强国建设纲要》[EB/OL]. [2024-03-23]. .
[2] Nayum A, Nordfjærn T. Predictors of public transport use among university students during the winter: A MIMIC modelling approach[J]. Travel Behaviour and Society, 2021, 22: 236-243.
[3] 吴文静, 孙刃超, 宗芳, 等. 居民低碳通勤出行的主观态度识别及影响分析[J]. 重庆交通大学学报: 自然科学版, 2021, 40(5): 53-58.
Wu Wen-jing, Sun Ren-chao, Zong Fang, et al. Subjective attitude identification and impact analysis of residents' low-carbon commuting travel[J]. Journal of Chongqing Jiaotong University(Natural Science), 2021, 40(5): 53-58.
[4] Zhang X, Zhong S, Ling S, et al. How to promote the transition from solo driving to mobility services delivery? an empirical study focusing on ridesharing[J]. Transport Policy, 2022, 129: 176-187.
[5] 胡含, 李昕光, 孙崇效, 等. 城市居民低碳出行意向异质性分析[J]. 交通运输工程与信息学报, 2024, 22(1): 128-138.
Hu Han, Li Xin-guang, Sun Chong-xiao, et al. Heterogeneity analysis of urban residents' low-carbon travel intention[J]. Journal of Transportation Engineering and Information, 2024, 22(1): 128-138.
[6] Kaye S, Lewis I, Buckley L, et al. To share or not to share: A theoretically guided investigation of factors predicting intentions to use fully automated shared passenger shuttles[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2020, 75: 203-213.
[7] Rejali S, Aghabayk K, Mohammadi A, et al. Assessing a priori acceptance of shared dockless e-scooters in Iran[J]. Transportation Research Part D: Transport and Environment, 2021, 100: No.103042.
[8] 马壮林, 崔姗姗, 胡大伟. 限行政策下城市居民低碳出行意向[J]. 吉林大学学报: 工学版, 2022, 52(11): 2607-2617.
Ma Zhuang-lin, Cui Sha-shan, Hu Da-wei. Urban residents' low-carbon travel intention after implementation of driving restriction policy[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2607-2617.
[9] Bollen K A. Structural Equation with Latent Variables[M]. New York: Wiley, 1989.
[10] 王玉茹. 居民低碳出行意向模型及其影响因素研究[J]. 云南民族大学学报: 自然科学版, 2016, 25(3): 280-285.
Wang Yu-ru. A study of the factors influencing Tianjin residents' intention of low-carbon travel[J]. Journal of Yunnan Minzu University(Natural Science Edition), 2016, 25(3): 280-285.
[11] 乔婧, 孙立山, 刘小明, 等. 基于结构方程的公共交通换乘影响因素研究[J]. 科学技术与工程, 2017, 17(6): 296-300.
Qiao Jing, Sun Li-shan, Liu Xiao-ming, et al. Analysis on public transportation transfer influencing factors based on structural equation model[J]. Science Technology and Engineering, 2017, 17(6): 296-300.
[12] Li J, Shen J, Jia B. Exploring intention to use shared electric bicycles by the extended theory of planned behavior[J]. Sustainability, 2021, 13(8): 1-13.
[13] Ajzen I. The theory of planned behavior[J]. Organizational Behavior & Human Decision Processes, 1991, 50(2): 179-211.
[14] Davis F D, Bagozzi P R, Warshaw P. User acceptance of computer technology: a comparison of two theoretical models[J]. Management Science, 1989(35): 982-1003.
[15] Neal D T, Wood W, Labrecque J S, et al. How do habits guide behavior? Perceived and actual triggers of habits in daily life[J]. Journal of Experimental Social Psychology, 2012, 48(2): 492-498.
[16] Fatmi M R, Habib M A. Modelling mode switch associated with the change of residential location[J]. Travel Behaviour Society, 2017, 9: 21-28.
[17] Verplanken B, Orbell S. Reflections on past behavior: a self-report index of habit strength[J]. Journal of Applied Social Psychology, 2003, 33(6): 1313-1330.
[18] 张昱, 孙岩, 刘学敏. 基于TPB的北京市居民低碳通勤选择机制研究[J]. 北京师范大学学报: 自然科学版, 2020, 56(6): 831-837.
Zhang Yu, Sun Yan, Liu Xue-min. Theory of planned behavior applied to choice of low carbon commuting of Beijing residents[J]. Journal of Beijing Normal University(Natural Science), 2020, 56(6): 831-837.
[19] 景鹏, 黄芳, 徐刚, 等. 自动驾驶支付意愿及影响因素分析[J]. 长安大学学报: 自然科学版, 2021, 41(1): 90-102.
Jing Peng, Huang Fang, Xu Gang, et al. Analysis of autonomous driving payment willingness and influencing factors[J]. Journal of Chang'an University(Natural Science Edition), 2021, 41(1): 90-102.
[20] Liu D, Du H, Southworth F, et al. The influence of social-psychological factors on the intention to choose low-carbon travel modes in Tianjin, China[J]. Transportation Research Part A: Policy and Practice, 2017, 105: 42-53.
[21] Nieves-Pavón S, López-Mosquera N, Jiménez-Naranjo H. The role emotions play in loyalty and WOM intention in a Smart Tourism Destination Management[J]. Cities, 2024, 145: No.104681.
[22] Samadzad M, Nosratzadeh H, Karami H, et al. What are the factors affecting the adoption and use of electric scooter sharing systems from the end user's perspective?[J]. Transport Policy, 2023, 136: 70-82.
[23] 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.
[24] 盛光华, 岳蓓蓓, 龚思羽. 共享单车用户持续使用意愿研究——基于TAM理论的拓展模型[J]. 东北大学学报: 社会科学版, 2019, 21(6): 567-574.
Sheng Guang-hua, Yue Bei-bei, Gong Si-yu. A research on consumers' willingness to use shared bicycles continuously——Based on the extended model of TAM theory[J]. Journal of Northeastern University (Social Science), 2019, 21(6): 567-574.
[25] 李振龙, 邢冠仰, 李佳, 等. 基于扩展TAM的车路协同系统驾驶人接受度研究[J]. 中国公路学报, 2021, 34(7): 188-200.
Li Zhen-long, Xing Guan-yang, Li Jia, et al. Driver's acceptance of cooperative vehicle infrastructure system based on extended TAM[J]. China Journal of Highway and Transport, 2021, 34(7): 188-200.
[26] 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 Behaviour, 2011, 14(2): 128-137.
[27] Yen Y S, Wu F S. Predicting the adoption of mobile financial services: the impacts of perceived mobility and personal habit[J]. Computers in Human Behavior, 2016, 65: 31-42.
[28] 吴明隆. 结构方程模型——AMOS的操作与应用[M]. 重庆: 重庆大学出版社, 2009: 40-53.
[29] Fu X, Juan Z. Exploring the psychosocial factors associated with public transportation usage and examining the "gendered" difference[J]. Transportation Research Part A: Policy and Practice, 2017, 103: 70-82.
[30] Greenland S, Senn S J, Rothman K J, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations[J]. European Journal of Epidemiology, 2016, 31: 337-350.
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