Evaluating Profitability of Traditional Taxi Services in Urban Arteries

Abstract:

Traditional taxis have an important place in urban mobility because taxis provide flexible, comfortable and door-to-door service to passengers. However, for the continuity of traditional taxi services, profitability analyzes were needed despite the sharing economy. For this reason, the focus of this study is traditional taxis. This study aims to analyze the profitability of traditional taxi services in the urban arteries of Istanbul. For this purpose, a survey was conducted with 35 taxis and 70 taxi drivers. Then a model was then developed consisting of the independent variables number of trips (TRP), total trip distance (DST) and efficiency (EFF) that affect the profitability of taxi services. Additionally, contour plots were used to more accurately evaluate the effect of independent variables. As a result, it was concluded that the most important variable affecting the profitability of traditional taxi services is the efficiency (EFF) independent variable.

• Periodical: Construction Technologies and Architecture (Volume 15)
• Pages: 123-130
• DOI: https://doi.org/10.4028/p-mKn4oX
• Online since: January 2025
• Authors: Nihat Can Karabulut*, Halit Ozen
• Keywords: Contour Plot, Multiple Linear Regression, Profitability, Sustainability Cities, Traditional Taxis

References

[1] Y. Dong, S. Wang, L. Li, and Z. Zhang, An empirical study on travel patterns of internet based ride-sharing, Transportation research part C: Emerging Technologies, 86 (2018) 1-22.

DOI: 10.1016/j.trc.2017.10.022

[2] Information on https://cdn-uym.ibb.gov.tr/cdn/uym/Bultenlerimiz/istanbul-ulasim-bulteni-2023-2.pdf

[3] X. Qian and S. V. Ukkusuri, Time-of-day pricing in taxi markets, IEEE Transactions on Intelligent Transportation Systems, 18(6) (2017) 1610-1622.

DOI: 10.1109/tits.2016.2614621

[4] W. Y. Szeto, R. C. P. Wong and W. H. Yang, Guiding vacant taxi drivers to demand locations by taxi-calling signals: A sequential binary logistic regression modeling approach and policy implications, Transport Policy, 76 (2019) 100-110.

DOI: 10.1016/j.tranpol.2018.06.009

[5] T. Wu, Q. Shen, M. Xu, T. Peng, and X. Ou, Development and application of an energy use and CO2 emissions reduction evaluation model for China's online car hailing services, Energy, 154 (2018) 298-307.

DOI: 10.1016/j.energy.2018.04.130

[6] P. S. Lavieri, F. F. Dias, N. R. Juri, J. Kuhr and C. R. Bhat, A model of ridesourcing demand generation and distribution, Transportation Research Record, 2672(46) (2018) 31-40.

DOI: 10.1177/0361198118756628

[7] Y. Si, H. Guan and Y. Cui, Research on the choice behavior of taxis and express services based on the SEM-Logit Model, Sustainability, 11(10) (2019) 2974.

DOI: 10.3390/su11102974

[8] A. Brodeur and K. Nield, An empirical analysis of taxi, Lyft and Uber rides: Evidence from weather shocks in NYC, Journal of Economic Behavior & Organization, 152 (2018) 1-16.

DOI: 10.1016/j.jebo.2018.06.004

[9] T. Berger, C. Chen, and C. B. Frey, Drivers of disruption? Estimating the Uber effect, European Economic Review, 110 (2018) 197-210.

DOI: 10.1016/j.euroecorev.2018.05.006

[10] M. W. Conway, D. Salon and D. A. King, Trends in taxi use and the advent of ride-hailing, 1995–2017: Evidence from the US National Household Travel Survey, Urban Science, 2(3) (2018) 79.

DOI: 10.3390/urbansci2030079

[11] R. Grahn, C. D. Harper, C. Hendrickson, Z. Qian and H. S. Matthews, Socioeconomic and usage characteristics of transportation network company (TNC) riders, Transportation, 47 (2020) 3047-3067.

DOI: 10.1007/s11116-019-09989-3

[12] K. N. Habib, Mode choice modelling for hailable rides: An investigation of the competition of Uber with other modes by using an integrated non-compensatory choice model with probabilistic choice set formation, Transportation Research Part A: Policy and Practice, 129 (2019) 205-216.

DOI: 10.1016/j.tra.2019.08.014

[13] P. S. Lavieri, and C. R. Bhat, Investigating objective and subjective factors influencing the adoption, frequency, and characteristics of ride-hailing trips, Transportation Research Part C: Emerging Technologies, 105 (2019) 100-125.

DOI: 10.1016/j.trc.2019.05.037

[14] Á. Aguilera-García, J. Gomez, G. Velázquez and J. M. Vassallo, Ridesourcing vs. traditional taxi services: Understanding users' choices and preferences in Spain, Transportation Research Part A: Policy and Practice, 155 (2022) 161-178.

DOI: 10.1016/j.tra.2021.11.002

[15] J. Aarhaug and K. Skollerud, Taxi: different solutions in different segments, Transportation Research Procedia, 1(1) (2014) 276-283.

DOI: 10.1016/j.trpro.2014.07.027

[16] T. F. Welch, S. R. Gehrke and A. Widita, Shared-use mobility competition: a trip-level analysis of taxi, bike share, and transit mode choice in Washington, DC, Transportmetrica A: Transport Science, 16(1) (2020) 43-55.

DOI: 10.1080/23249935.2018.1523250

[17] L. Kattan, A. de Barros, and S. C. Wirasinghe, Analysis of work trips made by taxi in Canadian cities, Journal of Advanced Transportation, 44(1) (2010) 11-18.

DOI: 10.1002/atr.102

[18] Information on https://tuhim.ibb.gov.tr/media/2573/taksi-yay%C4%B1nlama.pdf

[19] A. Hosseinzadeh, M. Algomaiah, R. Kluger, Z. Li, Spatial analysis of shared e-scooter trips, Journal of Transport Geography, 92 (2021) 103016.

DOI: 10.1016/j.jtrangeo.2021.103016