AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (844.1 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Comparison of Travel Mode Choice Between Taxi and Subway Regarding Traveling Convenience

Li Li, Shuofeng Wang, Meng Li( ), Jiyuan Tan
Department of Automation, BNRist, Tsinghua University, Beijing 100084, China
Department of Automation, Tsinghua University, Beijing 100084, China.
Department of Civil Engineering, Tsinghua University, Beijing 100084, China.
Beijing Key Laboratory of Urban Road Intelligent Traffic Control, North China University of Technology, Beijing 100144, China.
Show Author Information

Abstract

In this study, we investigate travel mode choice behavior between taxi and subway with an emphasis on the influence of traveling convenience. In the first stage, we examine the Origin-Destination (OD) points of Beijing taxi trips and compare these locations with the respective nearest subway station. Statistics reveal several interesting conclusions. First, for approximately 24.89% of all trips, no convenient subway connections exist between the OD pairs. As such, a taxi becomes the only viable choice. Second, for 80.23% of the remaining 75.11% of trips (equivalent to 60.26% of all trips), access distance from either the origin or the destination to the nearest subway station is greater than 500 meters. This phenomenon indicates that walking distance plays an important role in travel mode choice. In the second stage, we examine groups of taxi trips with similar travel distances and travel times to reveal common features. We establish a preference rule in terms of travel distance and travel time. This determines whether an individual driver will take a taxi or the subway, using a pairwise comparison-based preference regression model. Tests indicate that more than 95% of taxi trips can be correctly predicted by this preference rule. This conclusion reveals that traveling convenience dominates the travel model choice between taxi and subway. All these findings shed light on the factors that influence travel mode choice behavior.

Keywords

travel mode choicebehaviorstaxisubwaypreference modeling

References

[1]
Knoppers P. and Muller T., Optimized transfer opportunities in public transport, Transportation Science, vol. 29, no. 1, pp. 101–105, 1995.
Crossref Google Scholar
[2]
Spencer A. and Wang A., Light rail or busway? A comparative evaluation for a corridor in Beijing, Journal of Transport Geography, vol. 4, no. 4, pp. 239–251, 1996.
Crossref Google Scholar
[3]
Nie C., Wen H. Z., and Fan X. F., The spatial and temporal effect on property value increment with the development of urban rapid rail transit: An empirical research, Geographical Research, vol. 29, no. 5, pp. 801–810, 2010.
Google Scholar
[4]
Salon D., Wu J., and Shewmake S., Impact of bus rapid transit and metro rail on property values in Guangzhou, China, Transportation Research Record, no. 2452, pp. 36–45, 2014.
Crossref Google Scholar
[5]
Chiou Y. C., Jou R. C., and Yang C. H., Factors affecting public transportation usage rate: Geographically weighted regression, Transportation Research Part A: Policy and Practice, no. 78, pp. 161–177, 2015.
Crossref Google Scholar
[6]
Eagling J. and Ryley T., An investigation into the feasibility of increasing rail use as an alternative to the car, Transportation Planning and Technology, vol. 38, no. 5, pp. 552–568, 2015.
Crossref Google Scholar
[7]
Bohte W. and Maat K., Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands, Transportation Research Part C: Emerging Technologies, vol. 17, no. 3, pp. 285–297, 2009.
Crossref Google Scholar
[8]
Tang D. N., Yang M., and Zhang M. H., Travel mode choice modeling: A comparison of Bayesian networks and neural networks, Applied Mechanics and Materials, vols. 209–211, pp. 717–723, 2012.
Crossref Google Scholar
[9]
Li Z. and Hensher D., Prospect theoretic contributions in understanding traveller behaviour: A review and some comments, Transport Reviews, vol. 31, no. 1, pp. 97–115, 2011.
Crossref Google Scholar
[10]
Bolbol A., Cheng T., Tsapakis I., and Haworth J., Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification, Computers, Environment and Urban Systems, vol. 36, no. 6, pp. 526–537, 2012.
Crossref Google Scholar
[11]
Gong H., Chen C., Bialostozky E., and Lawson C. T., A GPS/GIS method for travel mode detection in New York City, Computers, Environment and Urban Systems, vol. 36, no. 2, pp. 131–139, 2012.
Crossref Google Scholar
[12]
Liu Y., Kang C., Gao S., Xiao Y., and Tian Y., Understanding intra-urban trip patterns from taxi trajectory data, Journal of Geographical Systems, vol. 14, no. 4, pp. 463–483, 2012.
Crossref Google Scholar
[13]
Xiao G., Juan Z., and Zhang C., Travel mode detection based on GPS track data and Bayesian networks, Computers, Environment and Urban Systems, vol. 54, pp. 14–22, 2015.
Crossref Google Scholar
[14]
Kusakabe T. and Asakura Y., Behavioural data mining of transit smart card data: A data fusion approach, Transportation Research Part C: Emerging Technologies, vol. 46, pp. 179–191, 2014.
Crossref Google Scholar
[15]
McDonald N., Active transportation to school: Trends among US schoolchildren, 1969-2001, American Journal of Preventive Medicine, vol. 32, no. 6, pp. 509–516, 2007.
Crossref Google Scholar
[16]
Anable J. and Gatersleben B., All work and no play? The role of instrumental and affective factors in work and leisure journeys by different travel modes, Transportation Research Part A Policy and Practice, vol. 39, nos. 2&3, pp. 163–181, 2005.
Crossref Google Scholar
[17]
Wang Z. J., Li X. H., and Chen H., Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data, Transportation Research Part A: Policy and Practice, vol. 77, pp. 213–224, 2015.
Crossref Google Scholar
[18]
Leng B., Du H., Wang J., Li L., and Xiong Z., Analysis of taxi drivers’ behaviors within a battle between two taxi apps, IEEE Transaction on Intelligent Transportation Systems, vol. 17, no. 1, pp. 296–300, 2016.
Crossref Google Scholar
[19]
Lee C., Zhu X., Yoon J., and Varni J., Beyond distance: Children’s school travel mode choice, Ann. Behav. Med., vol. 45, no. S1, pp. 55–67, 2013.
Crossref Google Scholar
[20]
Lanken B., Aarts H., Knippenberg A. V., and Knippenberg C. V., Attitude versus general habit: Antecedents of travel mode choice, Journal of Applied Social Psychology, vol. 24, no. 4, pp. 285–300, 1994.
Crossref Google Scholar
[21]
Mann E. and Abraham C., The role of affect in UK commuters’ travel mode choices: An interpretative phenomenological analysis, British Journal of Psychology, vol. 97, no. 2, pp. 155–176, 2006.
Crossref Google Scholar
[22]
Murtagh N., Gatersleben B., and Uzzell D., Multiple identities and travel mode choice for regular journeys, Transportation Research Part F Traffic Psychology and Behaviour, vol. 15, no. 5, pp. 514–524, 2012.
Crossref Google Scholar
[23]
Lois D., Moriano J. A., and Rondinella G., Cycle commuting intention: A model based on theory of planned behaviour and social identity, Transportation Research Part F Traffic Psychology and Behaviour, vol. 32, pp. 101–113, 2015.
Crossref Google Scholar
[24]
Flügel S., Halse A. H., Ortúzar J. D. D., and Rizzi L. I., Methodological challenges in modelling the choice of mode for a new travel alternative using binary stated choice data - The case of high speed rail in Norway, Transportation Research Part A: Policy and Practice, vol. 78, pp. 438–451, 2015.
Crossref Google Scholar
[25]
Arias-Nicolás J. P., Pérez C. J., and Martín J., A logistic regression-based pairwise comparison method to aggregate preferences, Group Decision and Negotiation, vol. 17, no. 3, pp. 237–247, 2008.
Crossref Google Scholar
[26]
Lehmann E. L. and Casella G., Theory of Point Estimation. Springer, 1999.
Google Scholar
[27]
Casella G. and Berger R. L., Statistical Inference, 2nd edition. Pacific Grove, CA, USA: Duxbury and Thomson Learning, 2002.
Tsinghua Science and Technology
Volume 23 Issue 2,
April 2018
Pages 135-144
DOI: 10.26599/TST.2018.9010059
Cite this article:
Li L, Wang S, Li M, et al. Comparison of Travel Mode Choice Between Taxi and Subway Regarding Traveling Convenience. Tsinghua Science and Technology, 2018, 23(2): 135-144. https://doi.org/10.26599/TST.2018.9010059

650

Views

59

Downloads

15

Crossref

N/A

Web of Science

8

Scopus

0

CSCD

Altmetrics
Received: 20 November 2016
Revised: 21 April 2017
Accepted: 21 May 2017
Published: 02 April 2018
© The author(s) 2018
Return