Modeling the Perspectives of Changing the Behavioral Habits of Personal Vehicles Users in the Using Public Transportation (Case Study: Tabriz Metropolis)

Subject Areas : Urban and Regional Planning Studies

M.  Abdullahi ¹ (PhD Candidate in Geography and Planning faculty, Department of Geography and Urban Planning, University of Tabriz, Tabriz, Iran)
M.  Pourmohammadi ² (Professor, Geography & Planning faculty, Department of Geography & Urban Planning, University of Tabriz, Tabriz, Iran Tabriz, Iran)
R.  Ghorbani ³ (Professor, Department of Geography and Urban Planning, University of Tabriz, Tabriz, Iran)

Received: 2016-12-25 Accepted : 2017-06-07 Published : 2017-12-21

Keywords: structural equation modeling, Behavioural Constraints, Travel Prospect, Travel Habit Index, Binary Logistic Regression,

Abstract :

To understand the background of current travel model is a key issue in the efficient development of urban transport planning, because identifying the mode and factors of travel choice is important to facilitate behavior change. In most cases, the realities of transportation on behavioral approaches are explained better than the logic models. The objective of this paper is to analyze the impact of various factors on the choice of the way of travel for users of personal and public transport. Therefore, conceptual model of travel habits was designed by combining the constraints on the choice of perceptual behavior and planned attitude. Based on the objective, the methodology of this study is of the type of applied-developmental research and also descriptive, analytical and survey method in terms of objective and also, nature and method, respectively. Data were collected through questionnaires and obtaining public comments according to the general displacement in Tabriz city. The sample was estimated using Cochran formula for 1,100 people in 166 traffic area of Tabriz. The results of the Structural equation modeling of travel pattern represent a variance more than 17 percent in private vehicle users and more than 14 percent in public transport users. Other factors such as attitudinal and choice constraints were not so important. Unlike the results of structural equation modeling, attitudinal believes provide possibility of prediction. Accordingly, through binary logistic regression, the willingness of travellers in using public transport was studied, suggesting a significant negative effect of travel habits on the probability of choosing public transportation. Based on the findings of this study, strategic suggestions have been presented aimed at breaking unsustainable driving habits and forming new sustainable habits.

References:

Araghi, M. (2009). Assessment of modeling approaches in Transportation Master Plan, 8th Transportation and Traffic Engineering Conference of Iran. (pp: 116-133), Tehran Municipality Transport and Traffic Organization, Tehran, Iran. (In Persian)

Executive Committee to reduce air pollution Tabriz. (2013). the draft master plan to reduce air pollution Tabriz. Tabriz: east Azerbaijan Department of Environment. (In Persian)

Andishkar Consulting Engineers. (2006). Tabriz Transportation Master Plan. Tabriz: Tabriz municipality. (In Persian)

Naghshe-Mohit Consulting Engineers. (2016). Tabriz master plan. Tabriz: General Directorate of Roads and Urban Development in East Azerbaijan province.(In Persian)

Aarts, H., & Dijksterhuis. A.P. (2000). The automatic activation of goal-directed behaviour: the case of travel habit. Journal of Environmental Psychology, 20 (1), 5-82.

Aarts, H., Verplanken, B., & van Knippenberg, A. (1998). Predicting behavior from actions in the past: repeated decision making or a matter of habit. Journal of Applied Social Psychology, 28 (15), 1355-1374.

Abrahamse, W., Steg, L., Gifford, R., & Vlek, C. (2009). Factors influencing car use for commuting and the intention to reduce it: A question of self-interest or morality?. Transportation Research Part F, 12 (4), 317-324.

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50 (2), 179-211.

Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103 (3), 411-423.

Bamberg, S., & Möser, G. (2007). Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. Journal of Environmental Psychology, 27(1), 14-25.

Bamberg, S., & Schmidt. P. (1999). Regulating transport: Behavioral changes in the field. Journal of Consumer Policy, 22(4), 479-509.

Bhat, C. R., & Koppelman, F. S. (2003). Activitybased modeling of travel demand. Handbook of Transportation Science, R. W. Hall (Ed.), U.S.A: Kluwer Academic Publishers.

Carrus, G., Passafaro, P., & Bonnes, M. (2008). Emotions, habits and rational choices in ecological behaviours: The case of recycling and use of public transportation. Journal of Environmental Psychology, 28(1), 51-62.

Chen, C.F., & Chao, W.H. (2011). Habitual or reasoned? Using the theory of planned behavior, technology acceptance model, and habit to examine switching intentions toward public transit. Transportation Research Part F. 14 (2), 128-137.

Conner, M., & Armitage, C.J. (1998). Extending the Theory of Planned Behavior: A review and avenues for further research, Journal of Applied Social Psychology, 28 (15), 1429-1464.

Dacko, S.G., & Spalteholz, C. (2014). upgrading the city: Enabling intermodal travel behavior, Technological Forecasting and Social Change, 89 (1), 222-235.

Davidov, E. (2007). Explaining habits in a new context the case of travel-mode choice. Rationality and Society, 19 (3), 315-334.

Falk, R.F., & Miller, N.B. (1992). A primer for soft modeling. U.S.A: The University of Akron Press, Akron (OH(.

Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18 (1), 39-50.

Frey, B.S. (1988). Ipsative and objective limits to human behavior. Journal of Behavioral Economics, 17 (4), 229-248.

Fujii, S., & Gärling, T. (2003). Application of attitude theory for improved predictive accuracy of stated preference methods in travel demand analysis. Transportation Research Part A, 37 (4), 389-402.

Gardner, B, (2009), Modelling motivation and habit in stable travel mode contexts. Transportation Research Part F, 12 (1), 68-76.

Gärling, T., Fujii, S., & Boe, O. (2001). Empirical tests of a model of determinants of scriptbased driving choice. Transportation Research Part F, 4 (2), 89-102.

Golob, T.F. (2003). Structural equation modeling for travel behavior research. Transportation Research Part B, 37 (1), 1-25.

Guo, J. Y. & Bhat, C. R. (2001). Representation and analysis plan and data needs analysis for the activity-travel system. Research Report 0-4080-1, Center for Transportation Research, The University of Texas at Austin, USA.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th Ed.). Englewood Cliffs (NJ) London: Prentice Hall.

Hair, J.F., Sarstedt, M., Ringle, C.M., & Mena, J.A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414‒433.

Kaiser, F.G., & Gutscher, H. (2003). The proposition of a general version of the theory of planned behaviour (TPB): predicting ecological behaviour. Journal of Applied Social Psychology, 33 (3), 586-603.

Klöckner, C.A., & Blöbaum, A. (2010). A comprehensive action determination model: Toward a broader understanding of ecological behaviour using the example of travel mode choice. Journal of Environmental Psychology, 30 (4), 574-586.

MacKenzie, S.B., Podsakoff, P.M., & Jarvis, C.B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90 (4), 710-730.

Madden, T.J., Scholder E.P., & Ajzen, I. (1992). A comparison of the Theory of Planned Behavior and the Theory of Reasoned Action. Personality and Social Psychology Bulletin, 18 (1), 3-9.

Manessa, M., Cirilloa, C.R., & Dugundjib, E. (2015). Generalized behavioral framework for choice models of social influence: Behavioral and data concerns in travel behavior. Journal of Transport Geography, 46 (1),137-150.

Oreg, S., & Katz-Gerro, T. (2006). Predicting proenvironmental behavior cross-nationally: Values, the Theory of Planned Behavior, and Value-Belief-Norm Theory. Environment and Behavior, 38 (4), 462- 483.

Ringle, C.M., Wende, S., & Will, A. (2005). SmartPLS 2.0. Hamburg. Retrieved 2015, Nov.28, from http://www.smartpls.de.

Roberts, N., & Thatcher, J.B. (2009). Conceptualizing and testing formative constructs: Tutorial and annoted example. The Data Base for Advances in Information Systems, 40 (3), 9-39.

Steurer, N., & Bonilla, D. (2016). Building sustainable transport futures for the Mexico City Metropolitan Area. Transport Policy, 52 (3),121-133.

Tanner, C. (1999). Constraints on environmental behaviour. Journal of Environmental Psychology, 19 (2),145-157.

Tanner, C., Kaiser, F.G., & Wölfing-Kast, S. (2004). Contextual conditions of ecological consumerism: A food-purchasing survey. Environment and Behavior, 36 (1), 94-111.

Taylor, S., & Todd ,P. (1995). An integrated model of waste management behaviour: A test of household recycling and composting intentions. Environment and Behavior, 27 (5), 603-630.

Urbach, N., & Ahlemann, F. (2010), Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11 (2), 5-40.

Wold, H. (1982). Soft modeling: The basic design and some extensions. In: K.G. Jöreskog, H. Wold (Eds.), Systems under indirect observations: Causality, structure, prediction, Part 2, (pp. 1-54). Amsterdam, North-Holland.