Modeling the Intra City Tours with Work Purpose by Using Weighted Multiple Regressions

Document Type : Research Article


1 Road, Housing and Development Research Center (BHRC), Tehran, Iran

2 Faculty of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran


In this paper by using the aggregate socioeconomic features of each zone and considering the land use prosperities, the aggregate zonal tours were modeled. For that, classic 4-step modeling database was utilized in which less data collection cost needs. Due heteroscedasticity , appling weighted least square (WLS) method led to multiple weighted regressions by two exogenous independent variables; population and number of employers in zone. Heteroscedasticity affect the efficiency of regression. In comparing of two model, the F test of WLS method growth 27 percent and the amount t students for population reduced from 2.79 to 2.27 that in negligible. On the other hand, the amount of t students for number of employers increases from 2.62 to 3.40 (30 percent). Constant coefficient is negative but in comparison with the maximum number of observed tours (7202 tours) is 0.069 percent and the average number of observed tours (1386 tours) is 0.36 percent that is negligible and the R2 goodness of fit index is 0.927 and acceptable.


Main Subjects

[1] T.A Arentze, D. Ettema, H.J.P. Timmermans, Estimating a model of dynamic activity generation based on one-day observations: Method and results, Transportation Research Part B, 45 (2) (2011) 447-460.
[2] C.R. Bhat, F.S. Koppelman, A conceptual framework of individual activity program generation, Transportation Research, 27 (1) (1993) 433-446.
[3] C.R. Bhat, Recent Methodological advances relevant to activity and travel behavior analysis, IATBR Conference, Texas-USA, 1997.
[4] J.L. Bowman, M.A. Bradley, Activity-based models: approaches used to achieve integration among trips and tours throughout the day, European Transport Conference, Leeuwenhorst- Netherlands, 2008.
[5] J.L. Bowman, Historical development of activity based models: theory and practice, Traffic Engineering and Control, 50 (3) (2009) 314–318.
[6] D.F. Ettema, H.J.P Timmermans, Activity-based approaches to travel analysis: Chapter one: Theories and Models of Activity Patterns, First Edtition, Pergamon- Elsevier, 1997.
[7] T. Hagerstrand, What about people in regional science? The Regional Science Association, 24(1) (1970) 7-21.
[8] P.M. Jones, New approaches to understanding travel behaviour: the human activity approach, in Hensher and Stopher (eds.), Behavioral Travel Modeling, Croom Helm, London, United Kingdom, 1979.
[9] M.D. Meyer, E.J. Miller, Urban transportation planning, Third edition, McGraw Hill, New York, USA, 2004.
[10] E.I. Pas, F.S. Koppelman, An examination of the determinants of day-to-day variability in individuals' urban travel behavior, Transportation, 14 (4) (1987) 3-20.
[11] E.I. Pas, The effect of selected sociodemographic characteristics on daily travel activity behavior. Environment and Planning A, 16(2) (1984) 571-581.
[12] E.I. Pas, Weekly travel-activity behavior, Transportation, 15(2) (1988) 89-109.
[13] W. Recker, G. McNally, S. Root, , A model of complex travel behavior: part II, An operational model, Transportation Research Part A, 20(1) (1986) 319-330.
[14] Y. Shiftan, M. Ben-Akiva, K. Proussaloglou, G.D. Jong, Y. Popuri, K. Kasturirangan S. Bekhor, Activity-Based modeling as a tool for better understanding travel behavior, 10th International Conference on Travel Behavior Research, Lucerne-Switzerland, 2003.
[15] C.H.U Zhaoming, H. Chen, L. Cheng, A review of activity based travel demand modeling, ASCE, 12(1) (2012) 48- 59.
[16] A. Sivakumar and A. Pinjari, Recent advances in activity and travel pattern modelling, Transportation, 39 (2) (2012) 749- 754.
[17] L. Yang, G. Zheng, X. Zhu, Cross- nested logit model for the joint choice of residential location, travel mode, and departure time, Habitat International, 38(3) (2013) 157-166.
[18] H. Kim, C. Kim, D. Park, Y. Kim, A tour-based approach to destination choice modeling incorporating agglomeration and competition effects, Proceedings of the Eastern Asia Society for Transportation Studies, 8(1) (2011) 101-112.
[19] A. Ettema, A. Borgers, H.J.P. Timmermans, A competing risk hazard model of activity choice, timing, sequencing, and duration, In Transportation Research Record: Journal of the Transportation Research Board, 1493(1) (1995) 101-109.
[20] C.R. Bhat, A hazard-based duration model of shopping activity with nonparametric baseline specification and nonparametric control for unobserved heterogeneity, Transportation Research B, 30(1) (1996) 189-207.
[21] F. Golob, H. Meurs, A structural model of temporal change in multi-modal travel demand, Transportation Research Part A, 21(1) (1987) 391-400.
[22] F. Golob, Review: Structural equation modeling for travel behavior research, Transportation Research Part B, 37(2) (2003) 1- 25.
[23] R. Kitamura, E.I. Pas, V. Lula, K. Lawton and E. Benson, The sequenced activity mobility simulator (SAMS): An integrated approach to modeling transportation, land use and air quality, Transportation, 23(1) (1996) 267-291.
[24] P.A. Salvini, E.J. Miller, ILUTE: An Operational prototype of a comprehensive microsimulation model of urban systems, Networks and Spatial Economics, 5(1) (2005) 217- 234.
[25] C.Q. Ho, C. Mulley, Multiple purposes at single destination: A key to a better understanding of the relationship between tour complexity and mode choice, Transportation Research Part A, 49(1) (2013) 206- 219.
[26] W. Navidi, Principles of statistics for engineers and scientists, First Edition, McGraw Hill, New York-USA, 2011.
[27] J.L. Bowman, Activity based Travel Demand Model System with Daily Activity Schedules, M.Sc. Thesis, Massachusetts Institute of Technology, USA, 1995.
[28] J.L. Bowman, The day activity schedule approach to travel demand analysis, Ph. D. thesis, Massachusetts Institute of Technology, USA, 1998.
[29] Qazvin transportation and traffic Comprehensive studies, Socioeconomic and Land use Report, Qazvin municipality, 2011, In Persian.