Quantifying the competitiveness of transit relative to taxi with multifaceted data

Abstract

This paper proposes an assessment framework to quantify the competitiveness of transit relative to a taxi-like service. The framework centers on a transit route builder, which searches, using a hyperpath-based algorithm, for the best available transit route that matches the origin and the destination of a given taxi trip. Based on the optimal transit route, we then measure the relative competitiveness of the transit service according to the preference of a rational traveler, which is determined by the generalized cost defined by fare, in-vehicle travel time and other service attributes. The framework is evaluated using a case study constructed with multifaceted data sources collected in Shenzhen, China. The results show that, while 90% of all taxi trips are faster than its best alternative transit option, only about 36% is shorter. Also, the relative competitiveness of transit decreases with the passenger's value of time, and increases with the average trip distance. We also find that the preference of the middle-income passengers for transit is the most sensitive to the changes in trip distance, mode (bus or rail) and fare.

Keywords:

• Transit
• competitiveness
• generalized cost
• hyperpath
• value of time

Acknowledgments

The authors are grateful to the two referees for their constructive comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Clearly, our definition of competitiveness would not apply to travellers to whom transit is not a physically feasible option (Webster, Weiner, and Wells 1974; Wohl 1975)

2 Shenzhen Transit Development Report in 2016, presented by the Transport Commission of Shenzhen Municipality.

3 See http://www.szmc.net/, in Chinese.

4 A stop-line combination corresponds to a transit node in Figure 1.

5 http://www.sztj.gov.cn/, in Chinese.

6 Assuming that a full-time job implies 2000 hours of work per year (40 h/week × 50 weeks/year).

Additional information

Funding

This research is jointly funded by National Nature Science Foundation of China (Grant No. 71971178), the Key Research and Development Plan of the Ministry of Science and Technology, China (No. 2018YFB1601402), the Fundamental Research Funds for the Central Universities of China and the GAIA Collaborative Research Funds for Young Scholars. The work of the last author was partially supported by the US National Science Foundation under the award number CMMI 1922665.