نقش شوک قیمتی بنزین و کرایه بر واکنش رفتاری مسافران کلان‌شهر تهران در استفاده از وسایل حمل‌ونقل عمومی (مترو، بی آر تی و اتوبوس)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری اقتصاد شهری و منطقه‏ای، دانشکدۀ اقتصاد و علوم اجتماعی، دانشگاه شهید چمران اهواز، اهواز، ایران

2 دانشیار، گروه اقتصاد، دانشکدۀ اقتصاد و علوم اجتماعی، دانشگاه شهید چمران اهواز، اهواز، ایران

3 استاد، گروه اقتصاد، دانشکدۀ اقتصاد و علوم اجتماعی، دانشگاه شهید چمران اهواز، اهواز، ایران

چکیده

حمل‏ونقل عمومی به ‏عنوان یک کالای عمومی بسیار مهم، اثرات بسیار گسترده‏ای بر اقتصاد، اجتماع و محیط ‏زیست شهری به ‏جای می‏گذارد. وقوع شوک‏ها و تغییرات پدیدآمده در سیستم اقتصادی یک شهر یا کشور می‏تواند رفتار مسافران برای انتخاب هر یک از انواع وسایل حمل‏ونقل عمومی شهری را تحت تأثیر قرار دهد. توانایی پیش‏بینی نوسانات سیستم حمل‏ونقل عمومی شهری بر اثر شوک‏های درونی و بیرونی سیستم حمل‏ونقل عمومی، از اساسی‏ترین نیازها برای سیاست‏گذاری مناسب سیستم حمل‏ونقل عمومی در پاسخ‏گویی به نوسانات ناگهانی تقاضای حمل‏ونقل عمومی در ایران و به‏ویژه کلان‏شهر تهران است. بر همین اساس، هدف این مطالعه بررسی نقش شوک قیمتی بنزین و کرایه بر واکنش رفتاری مسافران کلان‏شهر تهران در استفاده از وسایل حمل‏ونقل عمومی (مترو، بی‌.آر‌.تی و اتوبوس) طی دورۀ زمانی 1387-1398 با استفاده از داده‏های ماهانه و به روش خودرگرسیون برداری جامع (GVAR) بوده است. نتایج این پژوهش نشان داد با وقوع شوک مثبت در قیمت بنزین، مسافران مترو، بی‌.آر‌.تی و اتوبوس افزایش خواهد یافت. همچنین وقوع شوک مثبت در قیمت کرایۀ مترو، مسافران بی‌.آر‌.تی و اتوبوس را کاهش می‌دهد و با وقوع تکانۀ مثبت در قیمت کرایۀ بی‌.آر‌.تی، مسافران مترو افزایش و تقاضای بی‌.آر‌.تی و اتوبوس با شیب کم افزایش می‏یابد. در نهایت، وقوع تکانۀ مثبت در قیمت کرایۀ اتوبوس، افزایش مسافران مترو و بی‌.آر‌.تی را به دنبال دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

The Role of Fare and Gasoline Price Shocks on the Behavioral Response of Passengers in Tehran Metropolitan for Using Public Transportation(Metro, BRT, and Bus)

نویسندگان [English]

  • Mahboubeh Shojaeian 1
  • Masoud Khodapanah 2
  • Mansour Zarra-Nezhad 3
1 Ph.D. Candidate in Urban and Regional Economics, Department of Economics, Faculty of Economics and Social Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Associate Professor of Economics, Department of Economics, Faculty of Economics and Social Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 Professor of Economics, Department of Economics, Faculty of Economics and Social Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran
چکیده [English]

Introduction
Public transportation is considered one of the main requirements for economic growth and the sustainable development of cities. Lack of appropriate public transportation infrastructure can cause various socio-economic issues, including congestion, waste of time and fuel, intensive air pollution and the emission of greenhouse gasses, high urban density, and environmental degradation. The use of the public transportation system can be affected by various policies inside or outside the system. The ability to predict the fluctuations of the urban public transportation system due to internal and external shocks of the public transportation system is one of the most basic requirements for appropriate policymaking in response to sudden fluctuations, especially in the Tehran metropolitan. One of the main issues that put pressure on urban public transportation systems is their significant effectiveness in the fluctuations of gasoline prices and fares in big cities. Gasoline price fluctuations can change people's behavior in the substitution between private car use and public transportation system. On the other hand, fare changes can affect the demand for various public transportation vehicles like buses, metro, and BRT in Tehran as well. Despite the considerable importance of gasoline prices and fares on public transportation and the behavior of passengers in choosing between various transportation modes, no study has investigated the mentioned relationship and the response of public transportation passengers with respect to fuel price and fares. Therefore, the purpose of the current study was to evaluate the role of fare and gasoline price shocks on the behavioral response of passengers in the Tehran metropolitan using the Global Vector Autoregressive (GVAR) approach.
Materials and Methods
The main objective of this study was to investigate the role of gas price and fare shocks on the behavioral response of passengers in the Tehran metropolitan using public transportation (metro, BRT, and bus) during the period of 2007-2018 using monthly data and Global Vector Autoregressive (GVAR) models. The Global Vector Autoregressive Model (GVAR) model is one of the popular approaches to evaluating the relationships between economic entities. Compared to the traditional Vector Autoregressive (VAR) approach, it provides a universal and practical modeling framework to perform quantitative analyzes on the relative importance of different impulses and various channels for the transmission mechanism of the shocks. In this context, this method includes a compact econometric framework that is specifically designed to explicitly model the interdependence between the various variables that enter the model. In particular, the structure of the GVAR model combines individual error correction models in such a way that it is possible to relate the internal variables related to an economic unit in the model to the corresponding external variables in another economic unit j (i≠j). In this case, it is possible to check and determine the desired pattern of economic units in a much more precise way. Individual models are linked together using a weight matrix; finally, the GVAR model can be solved for all units. In other words, the GVAR approach represents a method that is used to check the degree of interdependence between model variables through the analysis of the impulse response function.
Findings
This study attempted to answer two main questions about the occurrence of two shocks, including gasoline price and fare shocks, on the behavioral response of passengers in Tehran metropolitan to various public transportation modes. The first question stated that what is the consequence of a positive gasoline price shock on the demand for various modes of public transport (metro, BRT, and bus) in the Tehran metropolitan? In response to this question, the estimation results of the GVAR model indicated that when a positive shock in the price of gasoline happens, an increase in passenger demand for all three types of public transportation happens.
The occurrence of a positive shock in the price of gasoline, metro, BRT, and bus passengers will increase. In other words, when a positive shock occurs in the price of gasoline, it causes people to use public transportation instead of private cars, and they will be more willing to use public transportation. This issue makes the policymakers in the public transportation system make the necessary arrangements to increase the carrying capacity when planning to respond to the fluctuations caused by the gasoline price shock and consider proper arrangements to divide this demand among all types of public transportation.
The second question of this research was what effect does the occurrence of a positive shock in the fare price of each type of public transportation (metro, BRT, and bus) have on the demand for other public transportation in the Tehran metropolis?
In response to this question, positive shocks on fares on all types of public transportation in the Tehran metropolis were investigated. The results of the study showed that when a positive shock in the metro fare occurs, the demand for metro passengers will be constant until the 20th month, and this indicated that part of the passengers' demand for the metro is independent of the fare and demand for bus and metro will be decreasing. Moreover, by the occurrence of a positive shock in bus fares, the demand for bus passengers will decrease, and the excess demand will transmit to other modes like metro and BRT. This leads to the simultaneous increase in passengers of both transportation modes. In other words, by the occurrence of a positive shock in bus fares, the passengers would replace the metro and BRT as alternatives to the buses. Besides, from the eight months onwards, when a positive shock happens in BRT fares, the demand for the metro will increase while the demand for buses increases slightly. This result states that a part of the demand for BRT will transmit to the metro and bus as alternatives. In this circumstance, metro and bus will be replaced with BRT; however, a part of the BRT passengers will not change their choices, and the demand will increase from the fourth month onwards. Finally, it can be stated that the occurrence of positive shocks in metro fares will decrease the BRT and bus passengers, while a positive shock in BRT fares would increase the demand for metro, and the demand for BRT and bus will increase with a flatter slope.
Conclusions
The results of this study can have important implications for policymaking during the occurrence of shocks in demand for intra-city public transport in Tehran. Based on the results of the study, it is suggested that policymakers think of the necessary predictions to create the appropriate capacity to meet the demand fluctuations in urban public transportation in response to the internal and external shocks of the transportation system. In addition, when setting fares for any public transportation, the relevant authorities must consider the interdependence between demand and fares for other public transportation. Also, the price shock of gasoline and the upward trend of passengers using all three types of public transportation means that the increase and expansion of subway lines, BRT, and the renovation of the bus system in all areas of Tehran will be among the most important policy suggestions for the public transportation system in Tehran. Finally, it is suggested to the policymakers and planners in the public transportation system of the Tehran metropolitan to set up a real-time passenger monitoring system for all types of urban public transportation vehicles so that they can be aware of the occurrence of sudden fluctuations in the transportation system.

کلیدواژه‌ها [English]

  • Fare Rate
  • Gasoline Price Shock
  • Public Transportation System
  • Tehran Metropolitan
[1] Sun Y, Cui Y. Evaluating the coordinated development of economic, social and environmental benefits of urban public transportation infrastructure: Case study of four Chinese autonomous municipalities. Transport Policy. 2018 Aug 1;66:116-26.
[2] Elmansouri O, Almhroog A, Badi I. Urban transportation in Libya: An overview. Transportation research interdisciplinary perspectives. 2020 Nov 1;8:100161.
[3] Zhao P. Sustainable urban expansion and transportation in a growing megacity: Consequences of urban sprawl for mobility on the urban fringe of Beijing. Habitat International. 2010 Apr 1;34(2):236-43.
[4] Aljoufie M. Exploring the determinants of public transport system planning in car-dependent cities. Procedia-Social and Behavioral Sciences. 2016 Jan 6;216:535-44.
[5] Wang MH, Ho YS, Fu HZ. Global performance and development on sustainable city based on natural science and social science research: A bibliometric analysis. Science of the Total Environment. 2019 May 20;666:1245-54.
[6] Yatskiv I, Budilovich E, Gromule V. Accessibility to Riga public transport services for transit passengers. Procedia Engineering. 2017 Jan 1;187:82-8.
[7] Bell A, Fairbrother M, Jones K. Fixed and random effects models: making an informed choice. Quality & quantity. 2019 Mar;53(2):1051-74.
[8] Performance report of Tehran Transport and Traffic Deputy. 2020. (In Persian).
[9] Hörcher D, Graham DJ. The Gini index of demand imbalances in public transport. Transportation. 2021 Oct;48(5):2521-44.
[10] Preston JM. Public transport. In, Kitchin, Rob and Thrift, Nigel (eds.) International Encyclopedia of Human Geography. Elsevier, 2009: 452-459.
[11] Vuchic V. Transportation for Livable Cities New Brunswick: Rutgers Center for Urban Policy Research. 1999.
[12] Block-Schachter D. The myth of the single mode man: how the mobility pass better meets actual travel demand (Doctoral dissertation, Massachusetts Institute of Technology).
[13] Chlond B. Making people independent from the car–multimodality as a strategic concept to reduce co 2-emissions. InCars and carbon 2012 (pp. 269-293). Springer, Dordrecht.
[14] Nobis C. Multimodality: facets and causes of sustainable mobility behavior. Transportation Research Record. 2007;2010(1):35-44.
[15] Rietveld P, Bruinsma FR, Van Vuuren DJ. Coping with unreliability in public transport chains: A case study for Netherlands. Transportation Research Part A: Policy and Practice. 2001 Jul 1;35(6):539-59.
[16] Chica-Olmo J, Gachs-Sánchez H, Lizarraga C. Route effect on the perception of public transport services quality. Transport Policy. 2018 Sep 15;67:40-8.
[17] Chao MC, Huang WH, Jou RC. The asymmetric effects of gasoline prices on public transportation use in Taiwan. Transportation Research Part D: Transport and Environment. 2015 Dec 1;41:75-87.
[18] Lane BW. A time-series analysis of gasoline prices and public transportation in US metropolitan areas. Journal of Transport Geography. 2012 May 1;22:221-35.
[19] Fujisaki K. An empirical analysis of effects of gasoline price change on transportation behavior in Japan, with consideration of regional differences. Socio-Economic Planning Sciences. 2014 Sep 1;48(3):220-33.
[20] Currie G, Phung J. Understanding links between transit ridership and gasoline prices: evidence from the United States and Australia. Transportation Research Record. 2008 Jan;2063(1):133-42.
[21] Mattson JW. Effects of rising gas prices on bus ridership for small urban and rural transit systems. Fargo: Upper Great Plains Transportation Institute, North Dakota State University; 2008 Jun.
[22] Litman T. Transit price elasticities and cross-elasticities. Journal of Public Transportation. 2004;7(2):3.
[23] Nowak WP, Savage I. The cross elasticity between gasoline prices and transit use: Evidence from Chicago. Transport policy. 2013 Sep 1;29:38-45.
[24] Jin Z, Schmöcker JD, Maadi S. On the interaction between public transport demand, service quality and fare for social welfare optimisation. Research in Transportation Economics. 2019 Sep 1;76:100732.
[25] McFadden D. The measurement of urban travel demand. Journal of public economics. 1974 Nov 1;3(4):303-28.
[26] Souche S. Measuring the structural determinants of urban travel demand. Transport policy. 2010 May 1;17(3):127-34.
[27] Bresson G, Dargay J, Madre JL, Pirotte A. Economic and structural determinants of the demand for public transport: an analysis on a panel of French urban areas using shrinkage estimators. Transportation Research Part A: Policy and Practice. 2004 May 1;38(4):269-85.
[28] Paulley N, Balcombe R, Mackett R, Titheridge H, Preston J, Wardman M, Shires J, White P. The demand for public transport: The effects of fares, quality of service, income and car ownership. Transport policy. 2006 Jul 1;13(4):295-306.
[29] Statistics of Tehran Megacity. Statistical yearbook of Tehran municipality, Compiled by Tehran Municipality Information and Communication Technology Organization. 2019. (In Persian).
[30] Mohammadpour S, Sarafi M, Tavakolinia. An analysis of travel demand management in the direction of sustainable urban transportation (case study: Tehran metropolis). Regional planning scientific-research quarterly. 2016 Apr 20;6(21):103-16. (In Persian).
[31] Transport and Traffic Organization of Tehran city, studies of Tehran's integrated public transportation system. 2018. (In Persian).
[32] Zoghi H, Rahim-Af K, Alipourvavasri M. Modeling freight freight in the road transport network with the approach of fuel price increase, 11th Iran Transportation and Traffic Engineering Conference. 2019. (In Persian).
[33] Sepehr M, Saffarzdeh M, Seyedabrishami E. Evaluating Fare Policies on Transit Users behaviour (Case study: BRT Tehran line seven). Master's thesis, Tarbiat Modares University. 2014. (In Persian).
[34] Nazmi A, Pressure N. Investigating the changes of fares on the behavior change of the Tehran Metro study. Economic Modeling Research Quarterly. March 10, 2017; 7 (26): 89-110. (In Persian).
[35] Hasanpour A, Khuzari M. Presenting the optimal pricing model for urban bus services (case study of Tehran). Economic Modeling Research. 2018;10:181-206.(In Persian).
[36] Stover VW, Bae CH. Impact of gasoline prices on transit ridership in Washington State. Transportation research record. 2011;2217(1):11-8.
[37] Chiang WC, Russell RA, Urban TL. Forecasting ridership for a metropolitan transit authority. Transportation research part A: policy and practice. 2011 Aug 1;45(7):696-705.
[38] Ky KE. How do changes in gasoline prices affect bus ridership in the Twin Cities?.2016.
[39] Agha MR, Bazrafshan M, Mahmoudabadi A. Investigating the Effects of Fuel Price on Inter-City Transportation Utilizing System Dynamics Approach and Simulation (Case Study: Inter-City Transport, Iran). J. Urban Des. 2019;2:1-3.
[40] Guzman LA, Beltran C, Bonilla JA, Cardona SG. BRT fare elasticities from smartcard data: Spatial and time-of-the-day differences. Transportation Research Part A: Policy and Practice. 2021 Aug 1;150:335-48.
[41] Chudik A, Pesaran MH. Theory and practice of GVAR modelling. Journal of Economic Surveys. 2016 Feb;30(1):165-97.
[42] Di Mauro F, Pesaran MH, editors. The GVAR handbook: Structure and applications of a macro model of the global economy for policy analysis. OUP Oxford; 2013 Feb 28.
[43] Kwok J. Macroeconometric Models for Portfolio Management. Vernon Press; 2021 Sep 7.
[44] Pesaran MH, Smith R. Macroeconometric modelling with a global perspective. The Manchester School. 2006 Sep;74:24-49.
[45] Koop G, Pesaran MH, Potter SM. Impulse response analysis in nonlinear multivariate models. Journal of econometrics. 1996 Sep 1;74(1):119-47.
[46] Pesaran MH, Smith RP. Structural analysis of cointegrating VARs. Journal of economic surveys. 1998 Dec;12(5):471-505.
[47] Pesaran MH, Smith RP. Structural analysis of cointegrating VARs. Journal of economic surveys. 1998 Dec;12(5):471-505.
[48] Michaelides PG, Konstantakis KN, Milioti C, Karlaftis MG. Modelling spillover effects of public transportation means: An intra-modal GVAR approach for Athens. Transportation Research Part E: Logistics and Transportation Review. 2015 Oct 1;82:1-8.
[49] Park HJ, Fuller WA. Alternative estimators and unit root tests for the autoregressive process. Journal of Time Series Analysis. 1995 Jul;16(4):415-29.
[50] Johansen S. Testing weak exogeneity and the order of cointegration in UK money demand data. Journal of Policy modeling. 1992 Jun 1;14(3):313-34.
[51] Harbo I, Johansen S, Nielsen B, Rahbek A. Asymptotic inference on cointegrating rank in partial systems. Journal of business & economic statistics. 1998 Oct 1;16(4):388-99.
[52] Li L, Cao M, Bai Y, Song Z. Analysis of public transportation competitiveness based on potential passenger travel intentions: Case study in Shanghai, China. Transportation Research Record. 2019 Apr;2673(4):823-32