SEM-Logit Integration Model of Travel Mode Choice Behaviors

doi:10.3969/j.issn.1000-565X.2013.02.009

Journal of South China University of Technology (Natural Science Edition) ›› 2013, Vol. 41 ›› Issue (2): 51-57,65.doi: 10.3969/j.issn.1000-565X.2013.02.009

• Traffic & Transportation Engineering • Previous Articles Next Articles

SEM-Logit Integration Model of Travel Mode Choice Behaviors

Chen Jian^1,2 Yan Qi-peng¹ Yang Fei¹ Hu Ji¹

1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, China;2. School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China

Received:2011-12-16 Revised:2012-09-14 Online:2013-02-25 Published:2013-01-05
Contact: 陈坚(1985-),男,博士,讲师,主要从事交通运输系统分析与决策、交通行为理论与实证研究. E-mail:chenjian525@126.com
About author:陈坚(1985-),男,博士,讲师,主要从事交通运输系统分析与决策、交通行为理论与实证研究.
Supported by:
国家自然科学基金资助项目(50908195);西南交通大学中央高校基本科研业务费专项资金资助项目(2010XS24)

Abstract

Abstract:

In the existing model of travel mode choice behaviors, only the observable characteristics of the traveler's socioeconomic status and travel plan are considered, while the latent variable (LV) affecting the travel mode choice is ignored. In order to solve this problem, the concept of LV in travel behaviors is proposed, and the causal rela-tionships between the LV and the manifest variable as well as between the LV and its measurement variables are de-scribed by using a structural equation model (SEM). Then, based on the maximum utility theory, a SEM-Logit in-tegration model containing both the LV and the manifest variable is constructed by improving the utility function of travel mode in the Logit model. The results show that, as compared with the traditional Logit model, SEM-Logit in-tegration model improves the goodness and the maximum likelihood value respectively by 0. 201 and 20. 607, which means that the LV plays an important role in travel mode choice behaviors and that the proposed model is of higher explanatory ability and accuracy.

Key words: transportation, travel mode choice, structural equation model, factor analysis approach, latent varia-ble, service environment

CLC Number:

U491.1

Chen Jian Yan Qi-peng Yang Fei Hu Ji. SEM-Logit Integration Model of Travel Mode Choice Behaviors[J]. Journal of South China University of Technology (Natural Science Edition), 2013, 41(2): 51-57,65.

[1]	ZHAO Xiaomei, ZHU Xiangyuan, WANG Qin, et al. Operational Reliability Optimization Strategies of Multi-type Bus Lines [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 32-39.
[2]	CHEN Yanyan, WANG Zifan, SUN Haodong, et al. Stuby on the Activity Patterns and Regularity of Public Transport Passengers [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(8): 40-50.
[3]	MA Shuhong, YANG Lei, CHEN Xifang. Resilience Evolution of Multi-mode Transportation Network in Urban Agglomeration Based on Risk Diffusion [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(6): 42-51.
[4]	AO Yibin, ZHENG Junjie, XIAO Shan, et al. Review of Research Methods, Trends and Hotspots on Rural Residents’ Transportation Behavior [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 31-45.
[5]	BIE Yiming, ZHU Aoze, CONG Yuan. Electric Bus Scheduling Method Considering Differences in the State of Health of Batteries [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 11-21.
[6]	WANG Yuanqing, LIN Siyu, XIE Minghui, et al. Dynamic Reservation and Allocation Optimization Model of Shared Parking Slots in Multiple Parking Lots [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(9): 12-21.
[7]	WENG Jiancheng, WANG Maolin, LIN Pengfei, et al. Cross-line Combined Bus Scheduling Optimization Method Based on Passenger Flow Characteristic Identification [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(9): 39-48.
[8]	ZHENG Yajing LI Yaohui JIN Wenzhou. Study on the Optimization of EMU Turnover with Considering Reconnection and Empty Return [J]. Journal of South China University of Technology (Natural Science Edition), 2021, 49(4): 80-89.
[9]	YAO Shushen WENG Xiaoxiong LI Feiyu. Analysis of Commuting Mode Based on Behavior Dynamics of Time Characteristics [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(9): 53-60.
[10]	ZHANG Nian LUO Xia ZHANG Ke. Bus Travel Choice Reversals Based on the Equate-to-Differentiate Theory [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(9): 61-67.
[11]	MA Guangtong YANG Wenjiao WANG Zhitao YE Changqing LI Jing. Research Development of Superconducting Maglev Transportation [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(7): 68-74,82.
[12]	. Incorporating Heterogeneity and Subjective Evaluation into Travel Mode Choice [J]. Journal of South China University of Technology(Natural Science Edition), 2019, 47(10): 124-129,150.
[13]	GAO Qiang ZHOU Tan CHEN Xin. Redistribution Method for Slack Time of Flight Based on Propagated Delay#br# [J]. Journal of South China University of Technology(Natural Science Edition), 2019, 47(10): 151-156.
[14]	MA Yingying LU Siyuan. A Decision Making Model of College Students' Red Light Behavior Based On The Theory Of Planned Behavior [J]. Journal of South China University of Technology (Natural Science Edition), 2018, 46(9): 73-81.
[15]	CHEN Peizhu CHEN Guohua QI Shuai ZHOU Zhihang . Quantitative Analysis of Ｒisk Ｒeduction of Natural Gas Transmission Pipeline with Explosion Proof Wall [J]. Journal of South China University of Technology (Natural Science Edition), 2018, 46(2): 124-130.

SEM-Logit Integration Model of Travel Mode Choice Behaviors

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments