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AN ANALYSIS FOR MODE CHOICE PREFERENCES BETWEEN ANKARA AND ISTANBUL

Yıl 2020, Cilt: 38 Sayı: 2, 727 - 739, 01.06.2021

Öz

In this study we conduct a survey which asks the respondents to evaluate the transportation modes based on “trip time”, “trip cost”, “comfort”, “reliability” variables whether they use or not the mode. It is assumed that the choices made based on “utility theory” and Multinomial Logit Model (MLM) incorporated. Utility functions for all modes (air, intercity bus, rail and private car) that serve between Ankara and Istanbul incorporated to the model presented. The weights of variables that effects choice probabilities used in utility function are calculated and then aimed modal distributions with required probability expressions. Finally modal distribution percentages are calculated for HSR (High Speed Rail System) in-operation as well as other three modes. Calculated modal distribution percentages are 51,91 % for intercity bus, 20,70 % for private car, 19,96 % for air and 7,43 % for HSR. With this study, we aimed that decision makers will be able to make more realistic projections and to develop a useful tool to help them made best possible transportation investments. Also a contribution for the related literature via a case-study is another aim of this work.

Kaynakça

  • [1] Turkish State Railways Website, http://www.tcdd.gov.tr/content/57 (Accessed: July 27, 2017).
  • [2] Dell’Olio, L., A. Ibeas, and P. Cecı´n. Modelling user perception of bus transit quality. Transport Policy, Vol. 17, No. 6, 2010, pp.388–397.
  • [3] Kızıltaş, M. Ç. Yüksek hızlı demiryolları mevcut durum, gelişme eğilimleri, Türkiye ve Dünyadaki örneklerin değerlendirilmesi, İstanbul Teknik Üniversitesi, Yüksek lisans tezi,2013.
  • [4] Givoni, M. Development and Impact of the Modern High‐speed Train: A Review. Transport reviews. Vol 26, No. 5, pp.593-611.
  • [5] Roth, D.L. State of the art practices in high speed rail ridership forecasting. High speed rail in the US: super trains for the new millennium. Amsterdam, Netherlands: Gordon & Breech, pp.52-80.
  • [6] Cohen, G., Erlbaum, N.S. and Hartgen, D.T. Intercity rail travel models. Transportation Research Record, Vol. 673, 1978, pp. 21-25.
  • [7] Brand, D., Parody, T.E., Hsu, P.S. and Tierney, K. Forecasting high-speed rail ridership. Transportation Research Record, Vol. 1342, 1992, pp. 12-18.
  • [8] Marwick, P. Florida high speed and intercity rail market and ridership study: final report. KPMG in association with ICF Kaiser Engineers, Inc., Midwest System Sciences, Resource Systems Group, Comsis Corporation and Transportation Consulting Group, Florida Department of Transportation, 1993.
  • [9] Chu, C. and Chen, X. Forecasting the patronage of high speed rail in Southern California Proceedings of the 8th REAAA conference, 1995, pp. 377-382.
  • [10] Charles River Associates. Independent ridership and passenger revenue projections for high speed rail alternatives in California. Prepared for the California high-speed rail authority, Parsons Brinckerhoff Cambridge Systematics Systra, 2000.
  • [11] Grayson, A. Disaggregate model of mode choice in intercity travel. Transportation Research Record, Vol. 835, 1981, pp. 36-42.
  • [12] TMS/Benesch High Speed Rail Consultants. Tri-state study of high speed rail service: Chicago-Milwaukee-Twin cities corridor. Illinois, Minnesota, and Wisconsin Departments of Transportation., 1991.
  • [13] Forinash, C. and Koppelman, F.S. Application and interpretation of nested logit models of intercity mode choice. Transportation Research Record, Vol. 1413, 1993, pp. 98-106.
  • [14] Bhat, C.R. A heteroscedastic extreme value model of intercity travel mode choice. Transportation Research Part B, Vol. 29, No.6, 1995, pp. 471-483.
  • [15] Bhat, C.R. An endogenous segmentation mode choice model with an application to intercity travel. Transportation Science, Vol. 31, 1997, pp. 34-48.
  • [16] Bhat, C.R. Accommodating variations in responsiveness to level-of-service measures in travel choice modeling.Transportation Research Part A, Vol. 32, No.7, 1998, pp. 49-57.
  • [17] Gunn, H.F., Bradley, M.A. and Hensher, D.A. High speed rail market projection: survey design and analysis.Transportation, Vol. 19, No.2, 1992, pp. 117-139.
  • [18] Gliebe, J. P. and Kim K. Time-dependent utility in activity and travel choice behavior. Transportation Research Record, Vol. 2156, 2010, pp. 9-16.
  • [19] Miller, J. H. Activities in space and time. Handbook of Transport Geography and Spatial Systems, Vol. 5, 2004, pp. 647-660.
  • [20] Algers, S. Integrated structure of long-distance travel behavior models in Sweden. Transportation Research Record, Vol. 1413, 1993.
  • [21] Hsu, C.I. and Chung, W.M. A model for market share distribution between high-speed and conventional rail services in a transportation corridor. The Annals of Regional Science, Vol. 31, No.2, 1993, pp. 121-153.
  • [22] Rus, G. and Inglada, V. Cost-benefit analysis of the high-speed train in Spain. Annals Regional Science, Vol 31, 1997, pp. 175-188.
  • [23] Kim, Y.M., Park, Y., Kim, J.C. and Hong, S.J. A study on the enhancement of air service with the opening of high speed railroad. Policy report, The Korea Transport Institute. Vol. 12, 2002.
  • [24] González‐Savignat. Will the high‐speed train compete against the private vehicle? Transport Reviews, Vol. 24, No. 3, 2004, pp. 293-316.
  • [25] López-Pita, A. and Robusté, F. Impact of high-speed lines in relation to very high frequency air services. Journal of Public Transportation, Vol. 8, No. 2, 2005, pp.2.
  • [26] Lee, J. H., K. S. Chon and C. Park. Accommodatl Heterogeneity and Heteroscedasticity in Intercity Travel Mode Choice Model: Formulation and Application to HoNam, South Korea, Highspeed Rail Demand Analysis. Transportation Research Record, Vol. 1898, No. 1, 2004, pp.69-78.
  • [27] Morikawa, T., Ben-Akiva, M. and Yamada, K. Forecasting intercity rail ridership using revealed preference and stated preference data. Transportation Research Record, Vol. 1328, 1991, pp.30-35.
  • [28] Hung-Yen C. and Fu, C. A study of domestic air passenger- preference for high- speed rail mode in Taiwan, The Journal of Global Business Management, Vol. 3, No. 2, 2002, pp. 147-155.
  • [29] Fröidh, O. Perspectives for a future high-speed train in the Swedish domestic travel market. Journal of Transport Geography, Vol. 16, No. 4, 2008, pp. 268-277.
  • [30] Ben-Akiva, M., Cascetta, E., Coppola, P., Papola, A. and Velardi, V. High speed rail demand forecasting in a competitive market: the Italian case study. . In Proceedings of the World Conference of Transportation Research (WCTR), Lisbon, Portugal.
  • [31] Chen, X. Calibrating the intercity high speed rail (HSR) choice model for the Richmond-Washington, D.D. corridor. Theoretical and Empirical Researches in Urban Management, Vol. 6, No. 2, 2011, pp. 35-53.
  • [32] Barreira, A. et al. F.S. Competitiveness of the high speed rail: Lisbon-Madrid corridor analysis based on discrete choice models. Transportation Research Board-2012 AR010, 2012.
  • [33] Behrens, C. and Pels, E. Intermodal competition in the London–Paris passenger market: High-Speed Rail and air transport. Journal of Urban Economics, Vol. 71, No. 3, 2012, pp. 278-288.
  • [34] Bergantino, A.S., Capozza, C. and Capurso, M. The impact of open access on intra-and inter-modal rail competition. A national level analysis in Italy. Transport Policy, Vol. 39, 2015, pp. 77-86.
  • [35] Yamane, T. Statistics: an introductory analysis. Harper and Row, Newyork, 2nd edition, 1967.
  • [36] Kanafani, A. Transportation Demand Analysis. McGraw-Hill, 1983.
  • [37] Ben-Akiva, M. ve Lerman, S. R. Discrete choice analysis: theory and application to travel demand. Cambridge, MA, MIT press, 1985.
  • [38] Manski, C. F. ve McFadden D. Alternative estimators and sample designs for discrete choice analysis. National Science Foundation, 1981, SOC72-05551-AO2 & SOC75-22657.
  • [39] McFadden, D. Conditional logit analysis of qualitative choice behaviour. P. Zarembka (ed.), Frontiers in econometrics, Academic press: New York, 1974, pp. 105-142.
  • [40] Morey, R., Rowe, R. D. And Watson M. A repeated nested-logit model of Atlantic salmon fishing. American Journal of Agricultural Economics, Vol. 75, 1993, pp. 578-592.
Yıl 2020, Cilt: 38 Sayı: 2, 727 - 739, 01.06.2021

Öz

Kaynakça

  • [1] Turkish State Railways Website, http://www.tcdd.gov.tr/content/57 (Accessed: July 27, 2017).
  • [2] Dell’Olio, L., A. Ibeas, and P. Cecı´n. Modelling user perception of bus transit quality. Transport Policy, Vol. 17, No. 6, 2010, pp.388–397.
  • [3] Kızıltaş, M. Ç. Yüksek hızlı demiryolları mevcut durum, gelişme eğilimleri, Türkiye ve Dünyadaki örneklerin değerlendirilmesi, İstanbul Teknik Üniversitesi, Yüksek lisans tezi,2013.
  • [4] Givoni, M. Development and Impact of the Modern High‐speed Train: A Review. Transport reviews. Vol 26, No. 5, pp.593-611.
  • [5] Roth, D.L. State of the art practices in high speed rail ridership forecasting. High speed rail in the US: super trains for the new millennium. Amsterdam, Netherlands: Gordon & Breech, pp.52-80.
  • [6] Cohen, G., Erlbaum, N.S. and Hartgen, D.T. Intercity rail travel models. Transportation Research Record, Vol. 673, 1978, pp. 21-25.
  • [7] Brand, D., Parody, T.E., Hsu, P.S. and Tierney, K. Forecasting high-speed rail ridership. Transportation Research Record, Vol. 1342, 1992, pp. 12-18.
  • [8] Marwick, P. Florida high speed and intercity rail market and ridership study: final report. KPMG in association with ICF Kaiser Engineers, Inc., Midwest System Sciences, Resource Systems Group, Comsis Corporation and Transportation Consulting Group, Florida Department of Transportation, 1993.
  • [9] Chu, C. and Chen, X. Forecasting the patronage of high speed rail in Southern California Proceedings of the 8th REAAA conference, 1995, pp. 377-382.
  • [10] Charles River Associates. Independent ridership and passenger revenue projections for high speed rail alternatives in California. Prepared for the California high-speed rail authority, Parsons Brinckerhoff Cambridge Systematics Systra, 2000.
  • [11] Grayson, A. Disaggregate model of mode choice in intercity travel. Transportation Research Record, Vol. 835, 1981, pp. 36-42.
  • [12] TMS/Benesch High Speed Rail Consultants. Tri-state study of high speed rail service: Chicago-Milwaukee-Twin cities corridor. Illinois, Minnesota, and Wisconsin Departments of Transportation., 1991.
  • [13] Forinash, C. and Koppelman, F.S. Application and interpretation of nested logit models of intercity mode choice. Transportation Research Record, Vol. 1413, 1993, pp. 98-106.
  • [14] Bhat, C.R. A heteroscedastic extreme value model of intercity travel mode choice. Transportation Research Part B, Vol. 29, No.6, 1995, pp. 471-483.
  • [15] Bhat, C.R. An endogenous segmentation mode choice model with an application to intercity travel. Transportation Science, Vol. 31, 1997, pp. 34-48.
  • [16] Bhat, C.R. Accommodating variations in responsiveness to level-of-service measures in travel choice modeling.Transportation Research Part A, Vol. 32, No.7, 1998, pp. 49-57.
  • [17] Gunn, H.F., Bradley, M.A. and Hensher, D.A. High speed rail market projection: survey design and analysis.Transportation, Vol. 19, No.2, 1992, pp. 117-139.
  • [18] Gliebe, J. P. and Kim K. Time-dependent utility in activity and travel choice behavior. Transportation Research Record, Vol. 2156, 2010, pp. 9-16.
  • [19] Miller, J. H. Activities in space and time. Handbook of Transport Geography and Spatial Systems, Vol. 5, 2004, pp. 647-660.
  • [20] Algers, S. Integrated structure of long-distance travel behavior models in Sweden. Transportation Research Record, Vol. 1413, 1993.
  • [21] Hsu, C.I. and Chung, W.M. A model for market share distribution between high-speed and conventional rail services in a transportation corridor. The Annals of Regional Science, Vol. 31, No.2, 1993, pp. 121-153.
  • [22] Rus, G. and Inglada, V. Cost-benefit analysis of the high-speed train in Spain. Annals Regional Science, Vol 31, 1997, pp. 175-188.
  • [23] Kim, Y.M., Park, Y., Kim, J.C. and Hong, S.J. A study on the enhancement of air service with the opening of high speed railroad. Policy report, The Korea Transport Institute. Vol. 12, 2002.
  • [24] González‐Savignat. Will the high‐speed train compete against the private vehicle? Transport Reviews, Vol. 24, No. 3, 2004, pp. 293-316.
  • [25] López-Pita, A. and Robusté, F. Impact of high-speed lines in relation to very high frequency air services. Journal of Public Transportation, Vol. 8, No. 2, 2005, pp.2.
  • [26] Lee, J. H., K. S. Chon and C. Park. Accommodatl Heterogeneity and Heteroscedasticity in Intercity Travel Mode Choice Model: Formulation and Application to HoNam, South Korea, Highspeed Rail Demand Analysis. Transportation Research Record, Vol. 1898, No. 1, 2004, pp.69-78.
  • [27] Morikawa, T., Ben-Akiva, M. and Yamada, K. Forecasting intercity rail ridership using revealed preference and stated preference data. Transportation Research Record, Vol. 1328, 1991, pp.30-35.
  • [28] Hung-Yen C. and Fu, C. A study of domestic air passenger- preference for high- speed rail mode in Taiwan, The Journal of Global Business Management, Vol. 3, No. 2, 2002, pp. 147-155.
  • [29] Fröidh, O. Perspectives for a future high-speed train in the Swedish domestic travel market. Journal of Transport Geography, Vol. 16, No. 4, 2008, pp. 268-277.
  • [30] Ben-Akiva, M., Cascetta, E., Coppola, P., Papola, A. and Velardi, V. High speed rail demand forecasting in a competitive market: the Italian case study. . In Proceedings of the World Conference of Transportation Research (WCTR), Lisbon, Portugal.
  • [31] Chen, X. Calibrating the intercity high speed rail (HSR) choice model for the Richmond-Washington, D.D. corridor. Theoretical and Empirical Researches in Urban Management, Vol. 6, No. 2, 2011, pp. 35-53.
  • [32] Barreira, A. et al. F.S. Competitiveness of the high speed rail: Lisbon-Madrid corridor analysis based on discrete choice models. Transportation Research Board-2012 AR010, 2012.
  • [33] Behrens, C. and Pels, E. Intermodal competition in the London–Paris passenger market: High-Speed Rail and air transport. Journal of Urban Economics, Vol. 71, No. 3, 2012, pp. 278-288.
  • [34] Bergantino, A.S., Capozza, C. and Capurso, M. The impact of open access on intra-and inter-modal rail competition. A national level analysis in Italy. Transport Policy, Vol. 39, 2015, pp. 77-86.
  • [35] Yamane, T. Statistics: an introductory analysis. Harper and Row, Newyork, 2nd edition, 1967.
  • [36] Kanafani, A. Transportation Demand Analysis. McGraw-Hill, 1983.
  • [37] Ben-Akiva, M. ve Lerman, S. R. Discrete choice analysis: theory and application to travel demand. Cambridge, MA, MIT press, 1985.
  • [38] Manski, C. F. ve McFadden D. Alternative estimators and sample designs for discrete choice analysis. National Science Foundation, 1981, SOC72-05551-AO2 & SOC75-22657.
  • [39] McFadden, D. Conditional logit analysis of qualitative choice behaviour. P. Zarembka (ed.), Frontiers in econometrics, Academic press: New York, 1974, pp. 105-142.
  • [40] Morey, R., Rowe, R. D. And Watson M. A repeated nested-logit model of Atlantic salmon fishing. American Journal of Agricultural Economics, Vol. 75, 1993, pp. 578-592.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Mustafa Gürsoy Bu kişi benim 0000-0002-3782-5941

Sümeyya Şeyma Kuşakcı Gündoğar Bu kişi benim 0000-0002-7665-0005

Sami Cankat Tanrıverdi Bu kişi benim 0000-0002-4881-5618

Güzin Akyıldız Alçura Bu kişi benim 0000-0001-7424-2764

Yayımlanma Tarihi 1 Haziran 2021
Gönderilme Tarihi 24 Ocak 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 38 Sayı: 2

Kaynak Göster

Vancouver Gürsoy M, Kuşakcı Gündoğar SŞ, Tanrıverdi SC, Akyıldız Alçura G. AN ANALYSIS FOR MODE CHOICE PREFERENCES BETWEEN ANKARA AND ISTANBUL. SIGMA. 2021;38(2):727-39.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/