Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai

Abstract

After the rapid expansion of the subway system over the past two decades, some cities are preparing to build more suburban railways. The emergence of suburban railways is bound to change the choice of suburban passenger transportation. This paper studies the factors that affect the choice of travel mode at the construction stage of suburban railways, aiming to design a more rational suburban railway network and urban public transport service system. Taking Shanghai as an example, this study first surveyed revealed preference (RP) and stated preference (SP) among urban-suburban travelers. Then, we used discrete choice models (DCM) and machine learning algorithms to build a travel mode choice model based on data collection and analysis. Furthermore, the importance of each factor was analyzed, and the effects were predicted under several traffic demand management schemes. Finally, this study proposed some strategies for increasing the share of public transport. On the one hand, it is suggested that Shanghai should continue to develop suburban railways and maintain low pricing of public transport services. Considering the construction and operation costs, the government needs to provide certain subsidies to stabilize prices. On the other hand, as passengers are very sensitive to the “last mile” trips in their suburban railway travel, transport planners should strengthen the connection from and to the suburban railway stations by developing services such as shared bikes and shuttle buses. In addition, the results indicated that some traffic demand management measures can also contribute to a larger share of public transport.

Keywords

Travel mode choice / Urban-suburban transportation corridors / Discrete choice models / Influencing factors / Machine learning algorithm / RP and SP survey

References

[1] As urbanization enters the era of metropolitan area, railway construction in big cities is booming. CBNWeekly. https://www.yicai.com/news/100043552.html

[2] Espino R, de Dios OJ, Román C. Understanding suburban travel demand: flexible modelling with revealed and stated choice data. Trans Res Part A Policy Pract, 2007, 41: 899-912

[3] Monzon A and Gonzales J (2000) Travel demand impacts of a new privately operated suburban rail in the Madrid n-iii corridor. Planning & Management of Public Transport Systems Proceedings of Seminar E.

[4] Tiantian J, Yaodong Z. Research on the development of beijing suburban public transportation based on SEM. J Phys Conf Ser, 2020, 1549(2): 022027

[5] Danapour M Competition between high-speed rail and air transport in Iran: the case of Tehran-Isfahan. Case Stud Transport Policy, 2018, 6(4): 456-461

[6] Dahlan A, Fraszczyk A. Public perceptions of a new MRT service: a pre-launch study in Jakarta. Urban Rail Transit, 2019, 5: 278-288

[7] Fraszczyk A, Weerawat W, Kirawanich P. Commuters' willingness to shift to metro: a case study of Salaya, Thailand. Urban Rail Transit, 2019, 5: 240-253

[8] Ali Aden W, Zheng J, Ullah I, Safdar MN. Public preferences towards car sharing service: the case of Djibouti. Front Environ Sci, 2022, 10: 449

[9] Shamshiripour A, Rahimi E, Shabanpour R, Mohammadian A. How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Trans Res Interdiscip Perspect, 2020, 7: 100216-100216.

[10] Calastri C, Pawlak J, Batley R. Participation in online activities while travelling: an application of the MDCEV model in the context of rail travel. Transportation, 2022, 49: 1-27

[11] Han Y, Li W, Wei S, Zhang T. Research on passenger's travel mode choice behavior waiting at bus station based on SEM-logit integration model. Sustainability, 2018, 10(6): 1996

[12] Toorzani A, Rassafi A. Pro-environmental attitude and adherence to a travel mode in an integrated choice and latent variable (ICLV) model: results from a revealed preference survey. Int J Civ Eng, 2022, 21(2): 235-249

[13] Cortes C, Vapnik V. Support-vector networks. Mach Learn, 1995, 20: 273-297

[14] Cheng L, Chen X, Lam HKW, Yang S and Lei D (2017) Public transit market research of low-income commuters using attitude-based market segmentation approach: Case study of Fushun, China. In Social Economic, Sustainability, and Human Factors in Transit, vol 2671(1). National Research Council, pp. 10–19 https://doi.org/10.3141/2671-02

[15] Yang S, Deng W, Deng Q, Fu P. The research on prediction models for urban family member trip generation. KSCE J Civ Eng, 2016, 20: 2901

[16] Qian Y, Aghaabbasi M, Ali M, Alqurashi M, Salah B, Zainol R, Moeinaddini M, Hussein EE. Classification of imbalanced travel mode choice to work data using adjustable SVM model. Appl Sci, 2021, 11(24): 11916

[17] Breiman L. Random forests. Mach Learn, 2001, 45: 5-32

[18] Cheng L, Lai X, Chen X, De Vos J, Witlox F. Applying a random forest method approach to model travel mode choice behavior. Trwaissavel Behav Soc, 2019, 14: 8415234

[19] National development and reform commission (2021). Planning of multi-level rail transit system in the Yangtze river delta region. https://www.ndrc.gov.cn/xxgk/zcfb/ghwb/202107/P020210702639387154556.pdf

[20] Shanghai urban planning and land resource administration bureau (2018) SHANGHAI MASTER PLAN 2017–2035. https://www.shanghai.gov.cn/newshanghai/xxgkfj/2035002.pdf

[21] Ministry of housing and urban-rural development, China academy of urban planning and design & Baidu map. (2022). The 2022 commuting monitoring report of major cities in China. https://huiyan.baidu.com/cms/report/2022tongqin/

[22] Zhang Z, Guan H, Zhu H, Zhu J. Analysis of travel mode choice behavior considering the indifference threshold. Sustainability, 2019, 11(19): 5495

[23] Hu H, Xu J, Shen Q, Shi F, Chen Y. Travel mode choices in small cities of China: A case study of Changting. Trans Res Part D Transport Environ, 2018, 59: 361-374

[24] Zhu H, Guan H, Han Y, Li W. A study of tourists' holiday rush-hour avoidance travel behavior considering psychographic segmentation. Sustainability, 2019, 11(13): 3755

[25] Orme B (1998) Sample size issues for conjoint analysis studies. Inc S S. Sequim

[26] Johnson R and Orme B (2003) Getting the Most from CBC. Sawtooth software research paper series. http://www.sawtoothsoftware.com/education/techpap.shtml

Funding

Science and Technology Research and Development Program of China State Railway Group Company Ltd.(2021F023)