Factors Affecting Mode Choice of Work Trips in Developing Cities—Gaza as a Case Study


The lack of efficient application of transportation planning process in developing cities, such as Gaza, leads to deficiency in adopting the suitable transport policies to mitigate the transportation problems resulting from urbanization and rapid increase of population. The mode choice model is probably the most important element in transportation planning and policy making. The aim of this study is to develop mode choice model for work trips in Gaza city and therefore investigating the factors that affect the employed people’s choice for transport modes. The model was developed using about two thirds of 552 questionnaires distributed for this purpose. The rest remaining third of questionnaires were used to validate the chosen models. The results of this research show that the factors that significantly affect the choice of transport modes are: total travel time, total cost divided by personal income, ownership of means of transport, distance, age, and average family monthly income. The developed model is able to predict the choice behavior of employed people in Gaza city as it is valid at 95% confidence level. This study can be used by transportation planners to predict the employed people’s behavior and travel demand analysis. The developed model can be used for predicting the future modal split by inputting predicted future value of exploratory variables.

Share and Cite:

E. Almasri and S. Alraee, "Factors Affecting Mode Choice of Work Trips in Developing Cities—Gaza as a Case Study," Journal of Transportation Technologies, Vol. 3 No. 4, 2013, pp. 247-259. doi: 10.4236/jtts.2013.34026.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Palestinian Central Bureau of Statistics (PCBS), 2011.
[2] D. A. Hensher and K. J. Button, “Handbook of Transport Modeling,” Elsevier Science Ltd, Oxford, 2000.
[3] European Commission, “Modeling of Urban Transport,” Final Report, Brussels, 1996.
[4] J. de D. Qrtuzar and L. G. Willumsen, “Modeling Transport,” 3rd Edition, John Wiely and Sons Ltd, New York, 2002.
[5] M. Ben-Akiva, “Structure of Passenger Travel Demand Models,” IEEE Transportation Research Record, Vol. 526, 1974, pp. 26-41.
[6] H. Williams, “On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit,” Environment and Planning, Vol. 9, No. 3, 1977, pp. 285-344. http://dx.doi.org/10.1068/a090285
[7] C. F. Daganzo, “Multinomial Probit: The Theory and Its Application to Demand Forecasting,” Academic Press, New York, 1980.
[8] A. Daly and M. Bierlaire, “A General and Operational Representation of General Extreme Value Models,” Transportation Research Part B, Vol. 40, No. 4, 2006, pp. 285-305. http://dx.doi.org/10.1016/j.trb.2005.03.003
[9] D. Brownstone, D. S Bunch and K. Train, “Joint Mixed Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” Transportation Research Part B, Vol. 34, No. 5, 2000, pp. 315-338.
[10] C. R. Bhat, “A Multiple Discrete-Continuous Extreme Value Model: Formulation and Application to Discretionary Time-Use Decisions,” Transportation Research Part B, Vol. 39, No. 8, 2005, pp. 679-707.
[11] M. E. Ben-Akiva, and S. R. Lerman, “Discrete Choice Analysis: Theory and Applications to Travel Demand,” MIT Press, Cambridge, 1985.
[12] O. Khan, “Modelling Passenger Mode Choice Behavior Using Computer Aided Stated Preference Data,” Ph.D. Thesis, Queensland University of Technology, Kelvin Grove, 2007.
[13] J. K. Dow and J. W. Endersby, “Multinomial Probit and Multinomial Logit: A Comparison of Choice Models for Voting Research,” Electoral Studies, Vol. 23, No. 1, 2004, pp. 107-122.
[14] N. H. Siddiqui, “Nested Logit Models for Motorized and Non-Motorized Modes,” Master Thesis, N.E.D. University of Engineering and Technology, Karachi, 1999.
[15] T. Domencich and D. McFadden, “Urban Travel Demand: A Behavioral Analysis,” North-Holland Publishing Company, Amsterdam, 1975.
[16] B. Mandel, M. Gaudry and W. Rothengatter, “A Disaggregate Box-Cox Logit Mode Choice Model of Intercity Passenger Travel in Germany and Its Implications for High-Speed Rail Demand Forecasts,” The Annals Regional Science, Vol. 31, No. 2, 1997, pp. 99-120.
[17] F. S. Koppelman and C. Bhat, “A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models,” U.S. Department of Transportation, Federal Transit Administration, 2006.
[18] E. Almasri, “Factors Affecting Travel Choice of Shared Taxi versus Bus for Palestinian University Student Trips,” International Review of Civil Engineering, Vol. 2, No. 1, 2011, pp. 35-45.

Copyright © 2021 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.