Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (8): 2116-2126.doi: 10.13229/j.cnki.jdxbgxb.20250085

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Analysis of residents' transfer behavior mechanisms under transit transfer preferential policy

Zhuang-lin MA1(),Yu-ming BI2,Wen-jian JIA1,Ya-juan DENG1,Xiao-wei TAN3,4()   

  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 Automobile，Chang'an University，Xi'an 710064，China
    4.Shanxi Key Laboratory of Intelligent Comprehensive Drivage and Green Mining Equipment for Coal Mine，Taiyuan 030032，China
  • Received:2025-01-28 Online:2026-08-01 Published:2026-09-02
  • Contact: Xiao-wei TAN E-mail:zhuanglinma@chd.edu.cn;tanxw@chd.edu.cn

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Abstract:

In order to analyze the impact of transit transfer preferential （TTP） policy on residents' transfer behavior， a questionnaire of combining Revealed Preference （RP） and Stated Preference （SP） was designed to collect data on respondents' socio-economic attributes， travel characteristics， psychological perceptions， and preferences in different transfer scenarios. Using an integrated choice and latent variable （ICLV） model， a transfer behavior model was established to explore the decision-making mechanism of residents under the TTP policy. The analysis results show that the fitting effect of the ICLV model is better that that of the SEM-Logit model. In the ICLV model， individuals' socio-economic attributes and travel characteristics can directly influence transfer behavior and indirectly influence it through psychological latent variables. The perceived ease of use and travel habits among the psychological latent variables have positive and negative impacts on residents' transfer behavior， respectively. Attitude acts as a moderating variable， where a better attitude towards the TTP policy enhances the positive impact of preferential magnitude on transfer behavior. When perceived transfer convenience and public transportation travel habits under the TTP policy increase 1%， the probability of choosing to transfer increase by 4.67% and decrease by 4.62%， respectively. By introducing variables such as individuals' socio-economic attributes and travel characteristics to estimate latent variables， the method overcomes the measurement error problem inherent in using questionnaire items to estimate latent variables thereby improving the explanatory power of the model estimation. The conclusions can provide theoretical support and reference value for government departments in planning， formulating， and implementing the TTP policy.

Key words: engineering of communications and transportation system, transfer behavior, transit transfer preferential policy, integrated choice and latent variable model, psychological latent variables

CLC Number: 

  • U491

Table 1

Items corresponding to psychological latent variables"

潜变量代码描述参考来源
态度（ATT）ATT1我认为公交换乘优惠政策是一个好的想法Acharya等［22］和Dirgahayani等［23］
ATT2我认为公交换乘优惠政策非常具有吸引力
ATT3我非常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影响我接受公交换乘优惠政策的意愿Le等［24］和Panagiotopoulos等［25］
SN2如果周围的人都接受公交换乘优惠政策，我更有可能接受
SN3政府的引导会增强我接受公交换乘优惠政策的意愿
感知行为控制（PBC）PBC1如果我很少换乘，我可能不会支持公交换乘优惠政策Le等［24］和Panagiotopoulos等［25］
PBC2如果换乘距离过长，我可能不会支持公交换乘优惠政策
PBC3优惠幅度越大，我就越有可能接受公交换乘优惠政策
感知有用性（PU）PU1我认为换乘优惠可以降低出行费用Acharya等［22］和Panagiotopoulos等［25］
PU2我认为换乘优惠可以减少出行时间
PU3我认为换乘优惠可以提高出行的便捷性
感知易用性（PEU）PEU1我认为公交换乘政策非常便捷Acharya等［22］和Panagiotopoulos等［25］
PEU2我认为获得公交换乘优惠政策的信息非常容易
PEU3我可以熟练地进行公交换乘
行为习惯（BH）HAB1我能熟练地使用各种公共交通方式，并熟悉它们之间的换乘Yen等［26］和Korkmaz等［27］
HAB2公共交通是我日常出行的主要交通方式之一
换乘意向（TI）BIU1日常生活中我会经常选择公共交通换乘Dirgahayani等［23］和Madigan等［28］
BIU2日常生活中我会推荐他人选择公共交通换乘

Table 2

Scenario factors of the SP survey"

情景因素因素水平值
123
不换乘步行距离/m0~800800~1 500>1500
换乘的步行距离/m0~5050~150>150
优惠幅度5折免费—
换乘模式地铁+公交公交+公交公交+地铁

Table 3

Eighteen hypothetical scenarios"

编号不换乘步行距离/m换乘步行距离/m优惠幅度换乘模式
1>1 5000~505折地铁+公交
2>1 50050~150免费地铁+公交
3>1 500>150免费地铁+公交
4800~1 5000~505折地铁+公交
5800~1 500>150免费地铁+公交
6>1 50050~1505折地铁+公交
7>1 5000~505折公交+公交
8>1 50050~150免费公交+公交
9>1 500>150免费公交+公交
10800~1 5000~505折公交+公交
11800~1 500>150免费公交+公交
12>1 50050~1505折公交+公交
13800~1 5000~505折公交+地铁
14>1500>150免费公交+地铁
15800~1 500>150免费公交+地铁
16>1 5000~505折公交+地铁
17>1 50050~1505折公交+地铁
18800~1 50050~1505折公交+地铁

Fig.1

Socio-economic attributes of the respondents"

Fig.2

Travel characteristics of the respondents"

Fig.3

Respondents′ psychological perceptions of transfer preferential policies"

Fig.4

Residents′ transfer behavior model based on the ICLV model"

Table 4

Results of reliability and validity tests"

潜变量名称Cronbach's αCRAVE
态度0.8790.8800.710
主观规范0.8780.8830.716
感知行为控制0.8960.8970.743
感知有用性0.8960.8960.741
感知易用性0.8740.8730.697
行为习惯0.8700.8710.772
换乘意向0.8810.8810.788

Fig.5

Results of discriminant validity tests"

Table 5

Results of the SEM-Logit and ICLV models"

自变量SEM-Logit模型ICLV模型
截 距-0.920-0.019
社会经济属性年龄：25岁以下（参照类：26~45岁）0.843***0.526***
学历：高中及以下（参照类：硕士及以上）-0.890***0.672***
月收入：5 000~15 000元（参照类：15 000元以上）1.002***0.637***
是否有驾照：是（参照类：否）-0.648***-0.405**
家庭结构：夫妻二人家庭（参照类：单人家庭）0.588*0.152**
出行特征出行时长：60~90 min（参照类：<60 min）0.657**0.638***
出行时长：>90 min（参照类：<60 min）1.148***0.982***
换乘方式：公交+公交（参照类：公交+地铁）0.427**0.609***
心理感知态度0.200*-
感知易用性0.163*0.196*
行为习惯--0.194*
假设情景不换乘步行距离：>1 500 m（参照类：800~1 500 m）0.225*0.242*
换乘步行距离：50~150 m（参照类：<50 m）-0.428**-0.404**
换乘步行距离：>150 m（参照类：<50 m）-1.262***-1.188***
优惠幅度：免费换乘（参照类：五折优惠）0.785***0.749***
换乘模式：公交+公交（参照类：公交+地铁）0.354**0.246*
交互项优惠幅度×态度-0.158*
AIC2 709.7013 474.58
BIC2 863.3713 708.57
LL（0）-1 493.43-6 976.58
LL（β）-1 327.85-6 677.29

Table 6

Estimation results of the latent variable part in the ICLV model"

变量态度感知易用性行为习惯
回归系数z回归系数z回归系数z
性别0.1321.53-0.109-1.180.0270.27
有驾照-0.258***-4.03-0.269***-4.07-0.219*-2.64
家庭结构：夫妻家庭0.247*2.280.448***4.01-0.250-1.77
出行距离：5 km以内-0.513***-4.42-0.326**-3.27-0.558***-3.89

Table 7

Influence of personal attributes on transfer behavior in the ICLV model"

变量态度感知易用性行为习惯间接影响直接影响总影响
性别——————
是否有驾照-0.031-0.0530.042-0.042-0.405-0.447
家庭结构：夫妻家庭0.0290.0880.0490.1660.4140.580
出行距离：5 km以内-0.061-0.064-0.103-0.228—-0.228

Table 8

Marginal effects of psychological latent variables"

变量边际效应
感知易用性0.046 7
行为习惯-0.046 2
[1] 刘玉华, 韩丽飞, 孙小丽. 国内外城市公共客运交通换乘优惠政策分析[J]. 城市轨道交通研究, 2016, 19(): 4-9.
Liu Yu-hua, Han Li-fei, Sun Xiao-li. Analysis of preference policies in Chinese and foreign urban public transport interconnection[J]. Urban Mass Transit, 2016, 19(Sup.1): 4-9.
[2] Hirsch L R, Jordan J D, Hickey R L, et al. Effects of fare incentives on New York city transit ridership[J]. Transportation Research Record, 2000, 1735: 147-157.
[3] 杨利强, 黄卫, 张宁. 基于广义费用的公共交通联乘优惠研究[J]. 交通信息与安全, 2009, 27(3): 20-23, 27.
Yang Li-qiang, Huang Wei, Zhang Ning. Transfer preferential benefit of public transport based on generalized cost[J]. Journal of Transport Information and Safety, 2009, 27(3): 20-23, 27.
[4] 齐瑞臻, 高悦尔, 王丽霞. 基于机器学习的换乘优惠政策对不同收入水平乘客的实施效果评估[J]. 地理与地理信息科学, 2022, 38(5): 72-78.
Qi Rui-zhen, Gao Yue-er, Wang Li-xia. Effect evaluation of transfer preferential policy on passengers with various income based on machine learning[J]. Geography and Geo-Information Science, 2022, 38(5): 72-78.
[5] Chowdhury S, Ceder A. A psychological investigation on public-transport users' intention to use route with transfers[J]. International Journal of Transportation, 2013, 1(1): 1-20.
[6] Cheng Y, Tseng W. Exploring the effects of perceived values, free bus transfer, and penalties on intermodal metro-bus transfer users' intention[J]. Transport Policy, 2016, 47: 127-138.
[7] 王蓉, 杜鹏. 基于PLS-SEM的换乘政策满意度模型[J]. 交通运输系统工程与信息, 2018, 18(): 10-15.
Wang Rong, Du Peng. Satisfaction model of transfer policy based on PLS-SEM[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(Sup.1): 10-15.
[8] Young H, Kim S, Ko S. Prediction of transferring demand on discounting the transit fare of public transport[J]. Proceedings of the Eastern Asia Society for Transportation Studies, 2003, 4: 1707-1719.
[9] Sharaby N, Shiftan Y. The impact of fare integration on travel behavior and transit ridership[J]. Transport Policy, 2012, 21: 63-70.
[10] Paulssen M, Temme D, Vij A, et al. Values, attitudes and travel behavior: a hierarchal latent variable mixed logit model of travel mode choice[J]. Transportation, 2014, 41: 873-888.
[11] 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 & Practice, 2017, 106: 364-373.
[12] Chen C, Fu C, Siao P. Exploring electric moped sharing preferences with integrated choice and latent variable approach[J]. Transportation Research Part D: Transport and Environment, 2023, 121: No.103837.
[13] 傅玉玲, 孙小慧, 张佳欣. 考虑心理潜变量的城市轨道站点接驳方式选择行为研究[J]. 交通运输工程与信息学报, 2024, 22(1): 160-174.
Fu Yu-ling, Sun Xiao-hui, Zhang Jia-xin. Choice behavior of urban rail station connection mode considering influence of psychological latent variables[J]. Journal of Transportation Engineering and Information, 2024, 22(1): 169-174.
[14] Ashok K, Dillon W R, Yuan S. Extending discrete choice models to incorporate attitudinal and other latent variables[J]. Journal of Marketing Research, 2002, 39(1): 31-46.
[15] Morikawa T, Ben-akiva M, Mcfadden D. Discrete choice models incorporating revealed preferences and psychometric data[J]. Econometric Models in Marketing, 2002, 16: 29-55.
[16] Bolduc D, Ben-akiva M, Walker J, et al. Hybrid choice models with logit kernel: applicability to large scale models[J]. Integrated Land-use and Transportation Models: Behavioural Foundations, 2005: 275-302.
[17] Johansson V M, Heldt T, Johansson P. The effects of attitudes and personality traits on mode choice[J]. Transportation Research Part A: Policy and Practice, 2006, 40(6): 507-525.
[18] 付学梅, 隽志才. 基于ICLV模型的通勤方式选择行为[J]. 系统管理学报, 2016, 25(6): 1046-1050.
Fu Xue-mei, Zhi-cai Juan. Commuting mode choice behavior based on ICLV model[J]. Journal of Systems & Management, 2016, 25(6): 1046-1050.
[19] Lizana M, Tudela A, Tapia A. Analysing the influence of attitude and habit on bicycle commuting[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2021, 82: 70-83.
[20] 原雅丽, 杨小宝, 李虹慧, 等. 突发事件下城市群内旅客城际出行方式选择行为[J]. 清华大学学报: 自然科学版, 2022, 62(7): 1142-1150.
Yuan Ya-li, Yang Xiao-bao, Li Hong-hui, et al. Intercity travel model choice behavior of travelers in large urban regions during emergencies[J]. Journal of Tsinghua University (Science & Technology), 2022, 62(7): 1142-1150.
[21] Kim J, Lee B. Campus commute mode choice in a college town: an application of the integrated choice and latent variable (ICLV) model[J]. Travel Behaviour and Society, 2023, 30: 249-261.
[22] Acharya S, Mekker M. Public acceptance of connected vehicles: an extension of the technology acceptance model[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 88: 54-68.
[23] Dirgahayani P, Sutanto H. The effect of transport demand management policy on the intention to use public transport: a case in Bandung, Indonesia[J]. Case Studies on Transport Policy, 2020, 8(3): 1062-1072.
[24] Le T P L, Leung A, Kavalchuk I, et al. Age-proofing a traffic saturated metropolis—Evaluating the influences on walking behaviour in older adults in Ho Chi Minh City[J]. Travel Behaviour and Society, 2021, 23: 1-12.
[25] Panagiotopoulos I, Dimitrakopoulos G. An empirical investigation on consumers' intentions towards autonomous driving[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 773-784.
[26] Yen Y, Wu F. Predicting the adoption of mobile financial services: the impacts of perceived mobility and personal habit[J]. Computers in Human Behavior, 2016, 65: 31-42.
[27] Korkmaz H, Fidanoglu A, Ozcelik S, et al. User acceptance of autonomous public transport systems: extended UTAUT2 model[J]. Journal of Public Transportation, 2022, 24: No.100013.
[28] Madigan R, Louw T, Wilbrink M, et al. What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems[J]. Transportation research part F: Traffic Psychology and Behavior, 2017, 50: 55-64.
[29] Jeffrey D, Boulange C, Giles-Corti B, et al. Using walkability measures to identify train stations with the potential to become transit oriented developments located in walkable neighborhoods[J]. Journal of Transport Geography, 2019, 76: 221-231.
[30] Ermagun A, Samimi A, Rashidi T H, et al. How far is too far? Providing safe and comfortable walking environments[J]. Transportation Research Record: Journal of the Transportation Research Board, 2016, 2586: 72-82.
[31] , 城市综合交通体系规划标准 [S].
[32] , 城市公共汽电车车站设施功能要求 [S].
[33] 西安市统计局, 国家统计局西安调查队. 西安统计年鉴2022[M]. 北京: 中国统计出版社, 2023.
[34] 西安市自然资源和规划局. 2022西安市城市交通发展年度报告[R/OL]. [2025-01-05]..
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