Quantifying Significance of Young Traveler Characteristics in Travel Mode Choices Impacted by E-Hailing Services
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 3
Abstract
Traditional travel modes have been slowly losing their market share to e-hailing services, especially among younger generations. This paper examines characteristics of young travelers’ travel mode choice, with various e-hailing modes described and modeled. A stated preference survey was conducted in Nanjing, China. A total of 314 valid responses were collected from young travelers from 16 to 35 years old. Descriptive statistics illustrate the transition details of the mode choices for interviewees of different genders, income levels, and educational levels. A cross-nested logit (CNL) model was employed to analyze the relationship between impact factor and young travelers’ mode choice behaviors, compared with two nested logit models. Factors, including personal profile and different scenario conditions, were examined. Comparison results indicated that the CNL model was statistically superior to the other two models. It was found that young travelers who were male, higher education level, lower income, rigid trip purpose, and long distance were more likely to choose e-hailing services. The modeling results also revealed the expected negative impacts to factors such as male, lower education, rigid trip purpose, and long distance on bus service, and adverse weather on taxi service.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors would like to thank the graduate assistants at the School of Transportation, Southeast University, for their assistance in data collection. This research was sponsored by the National Natural Science Foundation of China (Grant No. 61803083); the China Postdoctoral Science Foundation (Grant No. 2018M630497).
References
Ashalatha, R., V. S. Manju, and A. B. Zacharia. 2013. “Mode choice behavior of commuters in Thiruvananthapuram city.” J. Transp. Eng. 139 (5): 494–502. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000533.
Bai, L., P. Liu, C.-Y. Chan, and Z. Li. 2017. “Estimating level of service of mid-block bicycle lanes considering mixed traffic flow.” Transp. Res. Part A: Policy Pract. 101 (Jul): 203–217. https://doi.org/10.1016/j.tra.2017.04.031.
Berchtold, A. 2010. “Sequence analysis and transition models.” In Encyclopedia of animal behavior, edited by M. Breed, and J. Moore, 139–145. Oxford: Academic Press.
Byrnes, J. P., D. C. Miller, and W. D. Schafer. 1999. “Gender differences in risk taking: A meta-analysis.” Psychol. Bull. 125 (3): 367. https://doi.org/10.1037/0033-2909.125.3.367.
Donmez, B., L. N. Boyle, and J. D. Lee. 2010. “Differences in off-road glances: Effects on young drivers’ performance.” J. Transp. Eng. 136 (5): 403–409. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000068.
Enoch, M. P. 2015. “How a rapid modal convergence into a universal automated taxi service could be the future for local passenger transport.” Technol. Anal. Strategic Manage. 27 (8): 910–924. https://doi.org/10.1080/09537325.2015.1024646.
Etminani-Ghasrodashti, R., M. Paydar, and S. Hamidi. 2018. “University-related travel behavior: Young adults’ decision-making in Iran.” Sustainable Cities Soc. 43 (May): 495–508. https://doi.org/10.1016/j.scs.2018.09.011.
Forinash, C. V., and F. S. Koppelman. 1993. “Application and interpretation of nested logit models of intercity mode choice.” Transp. Res. Rec. 1413: 98–106.
He, F., and Z.-J. M. Shen. 2015. “Modeling taxi services with smartphone-based e-hailing applications.” Transp. Res. Part C: Emerging Technol. 58 (Part A): 93–106. https://doi.org/10.1016/j.trc.2015.06.023.
Heiss, F. 2002. “Structural choice analysis with nested logit models.” Stata J. 2 (3): 227–252. https://doi.org/10.1177/1536867X0200200301.
Hess, S., M. Fowler, T. Adler, and A. Bahreinian. 2012. “A joint model for vehicle type and fuel type choice: Evidence from a cross-nested logit study.” Transportation 39 (3): 593–625. https://doi.org/10.1007/s11116-011-9366-5.
Joo, Y., M. T. Wells, and G. Casella. 2010. “Model selection error rates in nonparametric and parametric model comparisons.” In Borrowing strength: Theory powering applications—A Festschrift for Lawrence D. Brown, 166–183. Beachwood, OH: Institute of Mathematical Statistics.
Konrad, K., and D. Wittowsky. 2017. “Virtual mobility and travel behavior of young people—Connections of two dimensions of mobility.” Res. Transp. Econ. 68 (Nov): 11–17. https://doi.org/10.1016/j.retrec.2017.11.002.
Koppelman, F. S., and C. R. Bhat. 2006. A self instructing course in mode choice modeling: Multinomial and nested logit models. Washington, DC: Federal Transit Administration.
Leng, B., H. Du, J. Wang, L. Li, and Z. Xiong. 2016. “Analysis of taxi drivers’ behaviors within a battle between two taxi apps.” IEEE Trans. Intell. Transp. Syst. 17 (1): 296–300. https://doi.org/10.1109/TITS.2015.2461000.
Li, Y., T. Xia, and H. Duan. 2014. “The impact on taxi industry of taxi-calling mobile apps in shanghai.” In Proc., CD-ROM of Transportation Research Board 93rd Annual Meeting, edited by T. R. Board. Washington, DC.
Moeckel, R., R. Fussell, and R. Donnelly. 2014. “Mode choice modeling for long-distance travel.” Transp. Lett. 7 (1): 35–46. https://doi.org/10.1179/1942787514Y.0000000031.
Nie, Y. M. 2017. “How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China.” Transp. Res. Part C: Emerging Technol. 79 (Jun): 242–256. https://doi.org/10.1016/j.trc.2017.03.017.
Obile, W. 2016. Ericsson mobility report. Stockholm, Sweden: Ericsson.
Papola, A. 2004. “Some developments on the cross-nested logit model.” Transp. Res. Part B: Methodol. 38 (9): 833–851. https://doi.org/10.1016/j.trb.2003.11.001.
Peng, L., H. Wang, X. He, D. Guo, and Y. Lin. 2014. “Exploring factors affecting the user adoption of call-taxi App.” In Proc., 25th Australasian Conf. on Information Systems. Auckland, New Zealand: Auckland Univ. of Technology.
Prensky, M. 2001. “Digital natives, digital immigrants part 1.” Horizon 9 (5): 1–6. https://doi.org/10.1108/10748120110424816.
Rayle, L., D. Dai, N. Chan, R. Cervero, and S. Shaheen. 2016. “Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco.” Transp. Policy 45 (Jan): 168–178. https://doi.org/10.1016/j.tranpol.2015.10.004.
Vovsha, P. 1997. “Application of cross-nested logit model to mode choice in Tel Aviv, Israel, metropolitan area.” Transp. Res. Rec. 1607 (1): 6–15. https://doi.org/10.3141/1607-02.
Wang, X., F. He, H. Yang, and H. O. Gao. 2016. “Pricing strategies for a taxi-hailing platform.” Transp. Res. Part E: Logist. Transp. Rev. 93 (Sep): 212–231. https://doi.org/10.1016/j.tre.2016.05.011.
Wen, C.-H., and F. S. Koppelman. 2001. “The generalized nested logit model.” Transp. Res. Part B: Methodol. 35 (7): 627–641. https://doi.org/10.1016/S0191-2615(00)00045-X.
Zhang, D., T. He, S. Lin, S. Munir, and J. A. Stankovic. 2016. “Taxi-passenger-demand modeling based on big data from a roving sensor network.” IEEE Trans. Big Data 99 (1): 1.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 3 • March 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Dec 18, 2018
Accepted: Jul 23, 2019
Published online: Jan 8, 2020
Published in print: Mar 1, 2020
Discussion open until: Jun 8, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.