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Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand

Yıl 2024, Cilt: 9 Sayı: 2, 298 - 310, 19.11.2024
https://doi.org/10.26650/JTL.2024.1429951

Öz

Taxis play a pivotal role in urban mobility by offering passengers flexible, comfortable, and door-to-door services. Despite the advent of the sharing economy, ensuring the continuity of traditional taxi services necessitates profitability analyses. For this reason, this study focuses on the economic profitability of trips made at a taxi stand serving the urban arteries of Istanbul. With the aim this, a survey was conducted among the drivers of a taxi stand. Subsequently, a model was developed, incorporating factors such as the number of trips (TRP), total trip distance (DST) and efficiency (EFF), which impact the profitability of taxi services. The modeling approach employed in this study is Response Surface Methodology (RSM). Additionally, contour plots were utilized to provide a more accurate assessment of the effects of factors. The results indicate that the EFF factor is the crucial factor influencing the profitability of traditional taxi services. This underscores the significance of the distance covered with passengers, highlighting its importance for both the economic success of taxi services and the broader network context of urban transportation.

Etik Beyan

There is no conflict of interest

Destekleyen Kurum

There is no external financing

Teşekkür

There is no acknowledgement

Kaynakça

  • Aarhaug, J., & Skollerud, K. (2014). Taxi: different solutions in different segments. Transportation Research Procedia, 1(1), 276-283. DOI: 10.1016/j.trpro.2014.07.027 google scholar
  • Berger, T., Chen, C., & Frey, C. B. (2018). Drivers of disruption? Estimating the Uber effect. European Economic Review, 110, 197-210. DOI: 10.1016/j.euroecorev.2018.05.006 google scholar
  • Bi, H., & Ye, Z. (2021). Exploring ridesourcing trip patterns by fusing multi-source data: A big data approach. Sustainable Cities and Society, 64, 102499. DOI: 10.1016/j.scs.2020.102499 google scholar
  • Brodeur, A., & Nield, K. (2018). An empirical analysis of taxi, Lyft and Uber rides: Evidence from weather shocks in NYC. Journal of Economic Behavior & Organization, 152, 1-16. DOI: 10.1016/j.jebo.2018.06.004 google scholar
  • Chen, F., Yin, Z., Ye, Y., & Sun, D. J. (2020). Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data. Transport Policy, 97, 73-84. DOI: 10.1016/j.tranpol.2020.04.001 google scholar
  • Choi, Y., Guhathakurta, S., & Pande, A. (2022). An empirical Bayes approach to quantifying the impact of transportation net-work companies (TNCs) operations on travel demand. Transportation Research Part A: Policy and Practice, 161, 269-283. DOI: https://doi.org/10.1016/j.tra.2022.04.008 google scholar
  • Christoforou, Z., Milioti, C., Perperidou, D., & Karlaftis, M. G. (2012). Investigation of taxi travel time characteristics. Advances in Transportation Studies, (27). google scholar
  • Cochran, W. G. (1977). Sampling techniques. Canada: John Wiley & Sons. google scholar
  • Cramer, J., & Krueger, A. B. (2016). Disruptive change in the taxi business: The case of Uber. American Economic Review, 106(5), 177-182. DOI: 10.1257/aer.p20161002 google scholar
  • Ghaffar, A., Mitra, S., & Hyland, M. (2020). Modeling determinants of ridesourcing usage: A census tract-level analysis of Chicago. Transportation Research Part C: Emerging Technologies, 119, 102769. DOI: https://doi.org/10.1016/j.trc.2020.102769 google scholar
  • IBB (2023a), Istanbul Transportation Bulletin. Istanbul Metropolitan Municipality, Retrieved May 21, 2024. https://cdn-uym.ibb.gov.tr/cdn/uym/Bultenlerimiz/istanbul-ulasim-bulteni-2023-2.pdf google scholar
  • IBB (2023b), Taxi Transportation Segments and Fee Tariff. Istanbul Metropolitan Municipality, Retrieved May 21, 2024. https://tuhim.ibb.gov.tr/media/2573/taksi-yay%C4%B1nlama.pdf google scholar
  • Jiang, S., Chen, L., Mislove, A., & Wilson, C. (2018, April). On ridesharing competition and accessibility: Evidence from uber, lyft, and taxi. In Proceedings of the 2018 World Wide Web Conference (pp. 863-872). DOI: https://doi.org/10.1145/3178876.3186134 google scholar
  • Kattan, L., de Barros, A., & Wirasinghe, S. C. (2010). Analysis of work trips made by taxi in Canadian cities. Journal of Advanced Transportation, 44(1), 11-18. DOI: 10.1002/atr.102 google scholar
  • Kong, H., Zhang, X., & Zhao, J. (2020). Is ridesourcing more efficient than taxis?. Applied Geography, 125, 102301. DOI: 10.1016/j.apgeog.2020.102301 google scholar
  • Liu, F., Gao, F., Yang, L., Han, C., Hao, W., & Tang, J. (2022). Exploring the spatially heterogeneous effect of the built environment on ride-hailing travel demand: A geo.graphically weighted quantile regression model. Travel Behaviour and Society, 29, 22-33. DOI: 10.1016/j.tbs.2022.05.004 google scholar
  • Milioti, C., Kepaptsoglou, K., Kouretas, K., & Vlahogianni, E. I. (2022). Driver perceptions on taxi-sharing and dynamic pricing in taxi services: Evidence from Athens, Greece. Journal of Public Transportation, 24, 100022. DOI: 10.5038/2375-0901.23.2.4 google scholar
  • Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons. google scholar
  • Nguyen-Phuoc, D. Q., Tran, P. T. K., Su, D. N., Oviedo-Trespalacios, O., & Johnson, L. W. (2021). The formation of passenger loyalty: Differences between ride-hailing and traditional taxi services. Travel Behaviour and Society, 24, 218-230. DOI: 10.1016/j.tbs.2021.04.006 google scholar
  • Nian, G., Huang, J., & Sun, D. (2022). Exploring built environment influence on taxi vacant time in megacities: A case study of Chongqing, China. Journal of Advanced Transportation, 2022, 1-14. DOI: 10.1155/2022/3096901 google scholar
  • Pan, Y., Chen, S., Li, T., Niu, S., Tang, K., 2019a. Exploring spatial variation of the bus stop in fl uence zone with multi-source data: a case study inZhenjiang, China. J. Transp. Geogr. 76, 166-177. DOI: 10.1016/j.jtrangeo.2019.03.012 google scholar
  • Shaheen, S., Cohen, A., Chan, N., & Bansal, A. (2020). Sharing strategies: carsharing, shared micromobility (bikesharing and scooter sharing), transportation network companies, microtransit, and other innovative mobility modes. In Transportation, land use, and environmental planning (pp. 237-262). Elsevier. DOI: 10.1016/B978-0-12-815167-9.00013-X google scholar
  • Szeto, W. Y., Wong, R. C. P., & Yang, W. H. (2019). Guiding vacant taxi drivers to demand locations by taxi-calling signals: A sequential binary logistic regression modeling approach and policy implications. Transport Policy, 76, 100-110. DOI: 10.1016/j.tranpol.2018.06.009 google scholar
  • Qian, X., Ukkusuri, S.V., 2015. Spatial variation of the urban taxi ridership using GPS data. Appl. Geogr. 59, 31-42. DOI: 10.1016/j.apgeog.2015.02.011 google scholar
  • Qian, X., & Ukkusuri, S. V. (2017). Time-of-day pricing in taxi markets. IEEE Transactions on Intelligent Transportation Systems, 18(6), 1610-1622. DOI: 10.1109/TITS.2016.2614621 google scholar
  • Yuan, J., Zheng, Y., Zhang, L., Xie, X., & Sun, G. (2011, September). Where to find my next passenger. In Proceedings of the 13th international conference on Ubiquitous computing (pp. 109-118). DOI: 10.1145/2030112.2030128 google scholar
  • Welch, T. F., Gehrke, S. R., & Widita, A. (2020). 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), 43-55. DOI: 10.1080/23249935.2018.1523250 google scholar
  • Wu, T., Shen, Q., Xu, M., Peng, T., & Ou, X. (2018). Development and application of an energy use and CO2 emissions reduction evaluation model for China’s online car hailing services. Energy, 154, 298-307. DOI: 10.1016/j.energy.2018.04.130 google scholar
  • Zhang, W., Ukkusuri, S. V., & Lu, J. J. (2017). Impacts of urban built environment on empty taxi trips using limited geolocation data. Transportation, 44, 1445-1473. DOI: https://doi.org/10.1007/s11116-016-9709-3 google scholar
  • Zong, F., Wu, T., & Jia, H. (2018). Taxi drivers’ cruising patterns—Insights from taxi GPS traces. IEEE Transactions on Intelligent Transportation Systems, 20(2), 571-582. DOI: 10.1109/TITS.2018.2816938 google scholar
Yıl 2024, Cilt: 9 Sayı: 2, 298 - 310, 19.11.2024
https://doi.org/10.26650/JTL.2024.1429951

Öz

Kaynakça

  • Aarhaug, J., & Skollerud, K. (2014). Taxi: different solutions in different segments. Transportation Research Procedia, 1(1), 276-283. DOI: 10.1016/j.trpro.2014.07.027 google scholar
  • Berger, T., Chen, C., & Frey, C. B. (2018). Drivers of disruption? Estimating the Uber effect. European Economic Review, 110, 197-210. DOI: 10.1016/j.euroecorev.2018.05.006 google scholar
  • Bi, H., & Ye, Z. (2021). Exploring ridesourcing trip patterns by fusing multi-source data: A big data approach. Sustainable Cities and Society, 64, 102499. DOI: 10.1016/j.scs.2020.102499 google scholar
  • Brodeur, A., & Nield, K. (2018). An empirical analysis of taxi, Lyft and Uber rides: Evidence from weather shocks in NYC. Journal of Economic Behavior & Organization, 152, 1-16. DOI: 10.1016/j.jebo.2018.06.004 google scholar
  • Chen, F., Yin, Z., Ye, Y., & Sun, D. J. (2020). Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data. Transport Policy, 97, 73-84. DOI: 10.1016/j.tranpol.2020.04.001 google scholar
  • Choi, Y., Guhathakurta, S., & Pande, A. (2022). An empirical Bayes approach to quantifying the impact of transportation net-work companies (TNCs) operations on travel demand. Transportation Research Part A: Policy and Practice, 161, 269-283. DOI: https://doi.org/10.1016/j.tra.2022.04.008 google scholar
  • Christoforou, Z., Milioti, C., Perperidou, D., & Karlaftis, M. G. (2012). Investigation of taxi travel time characteristics. Advances in Transportation Studies, (27). google scholar
  • Cochran, W. G. (1977). Sampling techniques. Canada: John Wiley & Sons. google scholar
  • Cramer, J., & Krueger, A. B. (2016). Disruptive change in the taxi business: The case of Uber. American Economic Review, 106(5), 177-182. DOI: 10.1257/aer.p20161002 google scholar
  • Ghaffar, A., Mitra, S., & Hyland, M. (2020). Modeling determinants of ridesourcing usage: A census tract-level analysis of Chicago. Transportation Research Part C: Emerging Technologies, 119, 102769. DOI: https://doi.org/10.1016/j.trc.2020.102769 google scholar
  • IBB (2023a), Istanbul Transportation Bulletin. Istanbul Metropolitan Municipality, Retrieved May 21, 2024. https://cdn-uym.ibb.gov.tr/cdn/uym/Bultenlerimiz/istanbul-ulasim-bulteni-2023-2.pdf google scholar
  • IBB (2023b), Taxi Transportation Segments and Fee Tariff. Istanbul Metropolitan Municipality, Retrieved May 21, 2024. https://tuhim.ibb.gov.tr/media/2573/taksi-yay%C4%B1nlama.pdf google scholar
  • Jiang, S., Chen, L., Mislove, A., & Wilson, C. (2018, April). On ridesharing competition and accessibility: Evidence from uber, lyft, and taxi. In Proceedings of the 2018 World Wide Web Conference (pp. 863-872). DOI: https://doi.org/10.1145/3178876.3186134 google scholar
  • Kattan, L., de Barros, A., & Wirasinghe, S. C. (2010). Analysis of work trips made by taxi in Canadian cities. Journal of Advanced Transportation, 44(1), 11-18. DOI: 10.1002/atr.102 google scholar
  • Kong, H., Zhang, X., & Zhao, J. (2020). Is ridesourcing more efficient than taxis?. Applied Geography, 125, 102301. DOI: 10.1016/j.apgeog.2020.102301 google scholar
  • Liu, F., Gao, F., Yang, L., Han, C., Hao, W., & Tang, J. (2022). Exploring the spatially heterogeneous effect of the built environment on ride-hailing travel demand: A geo.graphically weighted quantile regression model. Travel Behaviour and Society, 29, 22-33. DOI: 10.1016/j.tbs.2022.05.004 google scholar
  • Milioti, C., Kepaptsoglou, K., Kouretas, K., & Vlahogianni, E. I. (2022). Driver perceptions on taxi-sharing and dynamic pricing in taxi services: Evidence from Athens, Greece. Journal of Public Transportation, 24, 100022. DOI: 10.5038/2375-0901.23.2.4 google scholar
  • Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons. google scholar
  • Nguyen-Phuoc, D. Q., Tran, P. T. K., Su, D. N., Oviedo-Trespalacios, O., & Johnson, L. W. (2021). The formation of passenger loyalty: Differences between ride-hailing and traditional taxi services. Travel Behaviour and Society, 24, 218-230. DOI: 10.1016/j.tbs.2021.04.006 google scholar
  • Nian, G., Huang, J., & Sun, D. (2022). Exploring built environment influence on taxi vacant time in megacities: A case study of Chongqing, China. Journal of Advanced Transportation, 2022, 1-14. DOI: 10.1155/2022/3096901 google scholar
  • Pan, Y., Chen, S., Li, T., Niu, S., Tang, K., 2019a. Exploring spatial variation of the bus stop in fl uence zone with multi-source data: a case study inZhenjiang, China. J. Transp. Geogr. 76, 166-177. DOI: 10.1016/j.jtrangeo.2019.03.012 google scholar
  • Shaheen, S., Cohen, A., Chan, N., & Bansal, A. (2020). Sharing strategies: carsharing, shared micromobility (bikesharing and scooter sharing), transportation network companies, microtransit, and other innovative mobility modes. In Transportation, land use, and environmental planning (pp. 237-262). Elsevier. DOI: 10.1016/B978-0-12-815167-9.00013-X google scholar
  • Szeto, W. Y., Wong, R. C. P., & Yang, W. H. (2019). Guiding vacant taxi drivers to demand locations by taxi-calling signals: A sequential binary logistic regression modeling approach and policy implications. Transport Policy, 76, 100-110. DOI: 10.1016/j.tranpol.2018.06.009 google scholar
  • Qian, X., Ukkusuri, S.V., 2015. Spatial variation of the urban taxi ridership using GPS data. Appl. Geogr. 59, 31-42. DOI: 10.1016/j.apgeog.2015.02.011 google scholar
  • Qian, X., & Ukkusuri, S. V. (2017). Time-of-day pricing in taxi markets. IEEE Transactions on Intelligent Transportation Systems, 18(6), 1610-1622. DOI: 10.1109/TITS.2016.2614621 google scholar
  • Yuan, J., Zheng, Y., Zhang, L., Xie, X., & Sun, G. (2011, September). Where to find my next passenger. In Proceedings of the 13th international conference on Ubiquitous computing (pp. 109-118). DOI: 10.1145/2030112.2030128 google scholar
  • Welch, T. F., Gehrke, S. R., & Widita, A. (2020). 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), 43-55. DOI: 10.1080/23249935.2018.1523250 google scholar
  • Wu, T., Shen, Q., Xu, M., Peng, T., & Ou, X. (2018). Development and application of an energy use and CO2 emissions reduction evaluation model for China’s online car hailing services. Energy, 154, 298-307. DOI: 10.1016/j.energy.2018.04.130 google scholar
  • Zhang, W., Ukkusuri, S. V., & Lu, J. J. (2017). Impacts of urban built environment on empty taxi trips using limited geolocation data. Transportation, 44, 1445-1473. DOI: https://doi.org/10.1007/s11116-016-9709-3 google scholar
  • Zong, F., Wu, T., & Jia, H. (2018). Taxi drivers’ cruising patterns—Insights from taxi GPS traces. IEEE Transactions on Intelligent Transportation Systems, 20(2), 571-582. DOI: 10.1109/TITS.2018.2816938 google scholar
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ulaşım, Lojistik ve Tedarik Zincirleri (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Nihat Can Karabulut 0000-0002-4294-0215

Halit Özen 0000-0003-4031-7283

Yayımlanma Tarihi 19 Kasım 2024
Gönderilme Tarihi 1 Şubat 2024
Kabul Tarihi 17 Temmuz 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 9 Sayı: 2

Kaynak Göster

APA Karabulut, N. C., & Özen, H. (2024). Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand. Journal of Transportation and Logistics, 9(2), 298-310. https://doi.org/10.26650/JTL.2024.1429951
AMA Karabulut NC, Özen H. Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand. JTL. Kasım 2024;9(2):298-310. doi:10.26650/JTL.2024.1429951
Chicago Karabulut, Nihat Can, ve Halit Özen. “Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand”. Journal of Transportation and Logistics 9, sy. 2 (Kasım 2024): 298-310. https://doi.org/10.26650/JTL.2024.1429951.
EndNote Karabulut NC, Özen H (01 Kasım 2024) Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand. Journal of Transportation and Logistics 9 2 298–310.
IEEE N. C. Karabulut ve H. Özen, “Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand”, JTL, c. 9, sy. 2, ss. 298–310, 2024, doi: 10.26650/JTL.2024.1429951.
ISNAD Karabulut, Nihat Can - Özen, Halit. “Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand”. Journal of Transportation and Logistics 9/2 (Kasım 2024), 298-310. https://doi.org/10.26650/JTL.2024.1429951.
JAMA Karabulut NC, Özen H. Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand. JTL. 2024;9:298–310.
MLA Karabulut, Nihat Can ve Halit Özen. “Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand”. Journal of Transportation and Logistics, c. 9, sy. 2, 2024, ss. 298-10, doi:10.26650/JTL.2024.1429951.
Vancouver Karabulut NC, Özen H. Assessment of Profitability of Small-Scale Traditional Taxi Services: A Case Study of a Taxi Stand. JTL. 2024;9(2):298-310.



The JTL is being published twice (in April and October of) a year, as an official international peer-reviewed journal of the School of Transportation and Logistics at Istanbul University.