1 Ph.D. candidate in transportation planning engineering, Tarbiat Modares University, Tehran, Iran

2 Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran


The modal split model is one of the steps of the classical four-step travel demand planning. Predictive, descriptive, and prescriptive modal split models are essential to make a balance between travel demand and supply. To calibrate these models, it is necessary to detect and employ influential independent variables that are related to characteristics of travel modes, individual and family attributes, zones land use, etc. In previous studies, researchers used the household size, the number of children, and the number of employees as independent variables to show the role of family structure on the modal split. These variables cannot discriminate between different families with different structures. This paper uses the life cycle concept to categorize families based on their structures, and the effectiveness of these new variables on modal split models is examined. For this purpose, five types of family structures are considered that differences between them are based on the age of the family’s children. The Multinomial Logit model is used for mode choice modeling for different trip aims. The mode choice model has been calibrated using the origin-destination data of Qazvin-Iran. Results show the critical role of life cycle dummies in the mode choice models compared to household size, for work, educational, personal, and social- recreational trip aims. Life cycle variables are more active on the work trips mode choice model by estimating 14 significant coefficients, in a 90 percent level of significance. The number of life cycle significant coefficients is decreased to 3 for the shopping trips model.


Main Subjects

Batur, I.; Bayram, I. S.; Koc, M., (2019). Impact assessment of supply-side and demand-side policies on energy consumption and CO2 emissions from urban passenger transportation: the case of Istanbul. J. Cleaner Prod., 219: 391-410 (20 pages).

Ben-Akiva, M. E.; Lerman, S. R., (1985). Discrete choice analysis: theory and application to travel demand, (9): MIT press.

Blainey, S., (2010). Trip end models of local rail demand in England and Wales. J. Transp. Geogr., 18: 153-165 (13 pages).

Boellis, A.; Mariotti, S.; Minichilli, A.; Piscitello, L., (2016). Family involvement and firms’ establishment mode choice in foreign markets. J. Int. Bus. Stud., 47: 929-950 (22 pages).

Calastri, C.; Hess, S.; Choudhury, C.; Daly, A.; Gabrielli, L., (2019). Mode choice with latent availability and consideration: theory and a case study. Transport. Res. B-Meth., 123: 374-385 (12 pages).

Chang, Y.C.; Kao, M.S.; Kuo, A., (2014). The influences of governance quality on equity-based entry mode choice: The strengthening role of family control. Int. Bus. Rev., 23: 1008-1020 (13 pages).

Edrisi, A.; Askari, M., (2019). Probabilistic budget allocation for improving efficiency of transportation networks in pre-and post-disaster phases. Int. J. Disaster Risk Reduct., 101113: 1-9 (9 pages).

Gilhooly, P.; Low, D. J., (2005). Primary school travel behaviour in Midlothian, UK. P. I. Civil Eng-Munic., 158(2): 129-136 (8 pages).

Guo, J.; Feng, T.; Timmermans, H. J., (2020). Co-dependent workplace, residence and commuting mode choice: Results of a multi-dimensional mixed logit model with panel effects. Cities, 96: 102448 (9 pages).

Huntsinger, L. F.; Rouphail, N. M., (2012). Value of life cycle in explaining trip-making behavior and improving temporal stability of trip generation models. Transp. Res. Rec., 2322: 60-69 (10 pages).

Johansson, M. V.; Heldt, T.; Johansson, P., (2006). The effects of attitudes and personality traits on mode choice. Transp. Res. Part A Policy Pract., 40: 507-525 (19 pages).

Kermanshah, M., (1997). Life cycle concept: Application to trip generation procedures. IJST-T CIV. ENG., 21(1): 46-67 (22 pages).

Kitamura, R., (2009). Life-style and travel demand. Transportation (Amst), 36: 679-710 (32 pages).

Kiamura, R.; Kostyniuk, L. P., (1986). Maturing motorization and household travel: The case of nuclear-family households. Transp. Res. Part A Policy Pract., 20: 245-260 (16 pages).

Kuo, A.; Kao, M.S.; Chang, Y.C.; Chiu, C.F., (2012). The influence of international experience on entry mode choice: Difference between family and non-family firms. Eur. Manag. J., 30: 248-263 (16 pages).

Li, Z.; Wang, W.; Liu, Z.; Xu, C.; Wang, Y.; Guo, Y., (2014). Analysis of Mode Choice Decision and Choice Uncertainty Between Commuting and Non-commuting Trip Chains. Transportation Research Board 93rd Annual Meeting, 14-2409.

Liang, L.; Xu, M.; Grant-Muller, S.; Mussone, L., (2018). Travel Mode Choice Analysis Based on Household Mobility Survey Data in Milan: Comparison of the Multinomial Logit Model and Random Forest Approach. Transportation Research Board 97th Annual Meeting, 18-03595.

Ma, S.; Yu, Z.; Liu, C., (2020). Nested Logit Joint Model of Travel Mode and Travel Time Choice for Urban Commuting Trips in Xi'an, China. J. Urban Plan. D-ASCE, 146: 04020020.

Mehdizadeh, M.; Nordfjaern, T.; Mamdoohi, A., (2018). The role of socio-economic, built environment and psychological factors in parental mode choice for their children in an Iranian setting. Transportation (Amst), 45: 523-543 (21 pages).

Mitra, S., (2013). Discrete Choice Model of Agricultural Shipper's Mode Choice. Transp. J., 52(1): 6-25 (20 pages).

Othayoth, D.; Katti, B. K., (2017). Modelling Trip Distribution Using Fuzzy Logic Approach. Trans. Develop. Econ., 3(15): 1-8 (8 pages).

Provotorov, I.; Gasilov, V.; Anisimova, N., (2018). Problems of increased transport load as a result of implementation of projects of high-rise constructions. E3S Web of Conferences, EDP Sciences, 03019.

Rasaizadi, A.; Kermanshah, M., (2018). Mode choice and number of non-work stops during the commute: Application of a copula-based joint model. Sci. Iran., 25: 1039-1047 (9 pages).

Rubin, O.; Mulder, C. H.; Bertolini, L., (2014). The determinants of mode choice for family visits–evidence from Dutch panel data. J. Transp. Geogr., 38: 137-147 (11 pages).

Seyedabrishami, S.; Izadi, A. R., (2019). A Copula-Based Joint Model to Capture the Interaction between Mode and Departure Time Choices in Urban Trips. Transp. Res. Proc., 41: 722-730 (9 pages).

Shen, H.; Zou, B.; Lin, J.; Liu, P., (2020). Modeling travel mode choice of young people with differentiated E-hailing ride services in Nanjing China. Transp. Res. D Transp. Environ., 78: 102216.

Susilo, Y. O.; Liu, C.; Borjesson, M., (2019). The changes of activity-travel participation across gender, life-cycle, and generations in Sweden over 30 years. Transportation (Amst), 46: 793-818 (26 pages).

Ton, D.; Duives, D. C.; Cats, O.; Hoogendoorn-Lanser, S.; Hoogendoorn, S. P., (2019). Cycling or walking? Determinants of mode choice in the Netherlands. Transp. Res. Part A Policy Pract.,  123: 7-23 (17 pages).

Train, K.; McFadden, D., (1978). The goods/leisure tradeoff and disaggregate work trip mode choice models. Transp. Res. Part A Policy Pract., 12: 349-353 (5 pages).

Van Can, V., (2013). Estimation of travel mode choice for domestic tourists to Nha Trang using the multinomial probit model. Transp. Res. Part A Policy Pract., 49: 149-159 (11 pages).

Vijayalakshmi, S.; Raj, K., (2019). Income and Vehicular Growth in India: A Time Series Econometric Analysis. The Institute for Social and Economic Change, Bangalore.

Vrtic, M.; Frohlich, P.; Schussler, N.; Axhausen, K. W.; Lohse, D.; Schiller, C.; Teichert, H., (2007). Two-dimensionally constrained disaggregate trip generation, distribution and mode choice model: Theory and application for a Swiss national model. Transp. Res. Part A Policy Pract.,  41: 857-873 (17 pages).

Wijaya, S. E.; Imran, M. (2019). Moving the Masses: Bus-Rapid Transit (BRT) Policies in Low Income Asian Cities. Springer.

Yun, M.P.; Chen, Z.H.; Liu, J.Y., (2014). Comparison of mode choice behavior for work tours and non-work tours considering trip chain complexity. Transportation Research Board 93rd Annual Meeting, 14-0610.


International Journal of Human Capital in Urban Management (IJHCUM) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in IJHCUM should be sent to the editorial office of IJHCUM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.