Analysis of the interaction between destination and departure time choices

Document Type : Article

Authors

Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-111, Iran

Abstract

Destination and departure time choices are interrelated decisions that affect urban travel demand estimation. Most previous studies ignore this interrelation and assume that these decisions are independent. Some other studies use a hierarchy structure, while the literature suggests that destination and departure time are selected simultaneously before the commencing of trips. This paper employs copula-based joint modeling to explore the interdependency between destination and departure time choices. The destination choice modeling is developed using a multinomial logit model, and a binary logit model is used for modeling departure time choice. To obtain a better-fitted model, several copula functions are used thereafter; the frank copula is selected for the final model. Results show that there are some common unobserved factors between these decisions by estimating copula dependence parameters with high statistical significance. Furthermore, there are some commonly observed factors, such as socio-demographic and travel characteristics that appear in the utility functions of both models.

Keywords

References

References
[1] Seyedabrishami, S. and A.R. Izadi, “A Copula-Based Joint Model to Capture the Interaction between Mode and Departure Time Choices in Urban Trips”, Transp. Res. Rec., 41, pp. 722-730, (2019).
[2] Thorhauge, M., E. Cherchi, and J. Rich, “How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips”, Transp. Res. Part A Policy Pract., 86, pp. 177-193, (2016).
[3] Sultana, S., “Mode and Departure Time Choice Behavior of Non-Work Related Trips”, Schulich School of Engineering, (2019).
[4] Sasic, A. and K.N. Habib, “Modelling departure time choices by a Heteroskedastic Generalized Logit (Het-GenL) model: An investigation on home-based commuting trips in the Greater Toronto and Hamilton Area (GTHA)”, Transp. Res. Part A Policy Pract., 50, pp. 15-32, (2013).
[5] Hasnat, M.M., Faghih-Imani, A., Eluru, N. and Hasan, S., “Destination choice modeling using location-based social media data”, J. Choice Model., 31, pp. 22-34 (2019).
[6] Fox, J., B. Patruni, and A. Daly, “A new travel demand model for London: Estimation of the mode and destination choice models”, (2019).
[7] Clifton, K.J., Singleton, P.A., Muhs, C.D. and Schneider, R.J., “Development of destination choice models for pedestrian travel”, Transp. Res. Part A Policy Pract., 94, pp. 255-265, (2016).
[8] Hossain, S., M.S. Hasnine, and K.N. Habib, “A latent class joint mode and departure time choice model for the Greater Toronto and Hamilton Area”, Transportation, pp. 1-23, (2020).
[9] Parsa, A.B., Kamal, K., Taghipour, H. and Mohammadian, A.K., “Does security of neighborhoods affect non-mandatory trips? A copula-based joint multinomial-ordinal model of mode and trip distance choices”, (2019).
[10] Ding, C., Xie, B., Wang, Y. and Lin, Y., “Modeling the joint choice decisions on urban shopping destination and travel-to-shop mode: A comparative study of different structures”, Discrete Dyn. Nat. Soc., (2014).
[11] Bhat, C.R., “Analysis of travel mode and departure time choice for urban shopping trips”, Transport. Res. B-Meth.,. 32(6), pp. 361-371 (1998).
[12] Elmorssy, M. and T. Onur, “Modelling Departure Time, Destination and Travel Mode Choices by Using Generalized Nested Logit Model: Discretionary Trips”, Int. J. Eng., 33(2), pp. 186-197, (2020).
[13] Rasaizadi, A. and M. Kermanshah, “Mode choice and number of non-work stops during the commute: Application of a copula-based joint model”, Sci. Iran., 25(3), pp. 1039-1047, (2018).
[14] Seyedabrishami, S., Izadi, A.R., Rayaprolu, H.S. and Moeckel, R. “Car ownership: A joint model for number of cars and fuel types”, Transp. Res. Rec., 41, (2019).
[15] Ermagun, A., Rashidi, T.H., Arian, A. and Samimi, A., “Mode Choice and Escort Decisions in School Trips: Application of a Copula-Based Model”, (2014).
[16] Bhat, C.R. and I.N. Sener, “A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units”, J. Geogr. Syst., 11(3), pp. 243-272, (2009).
[17] Ben-Akiva, M.E., S.R. Lerman, and S.R. Lerman, “Discrete choice analysis: theory and application to travel demand”. MIT press, (1985).
[18] Portoghese, A., Spissu, E., Bhat, C.R., Eluru, N. and Meloni, I., “A copula-based joint model of commute mode choice and number of non-work stops during the commute”, Int. J. Transp. Econ., pp. 337-362, (2011).
[19] Trivedi, P.K. and D.M. Zimmer, “Copula modeling: an introduction for practitioners”. Found. Trends Econ., 1(1), pp. 1-111, (2007).
[20] Liu, X.D., Pan, F., Cai, W.L. and Peng, R., “Correlation and risk measurement modeling: A Markov-switching mixed Clayton copula approach”, Reliab. Eng. Syst. Saf., 19(7), pp. 106808, (2020).
[21] Wang, K., Bhowmik, T., Yasmin, S., Zhao, S., Eluru, N. and Jackson, E., “Multivariate copula temporal modeling of intersection crash consequence metrics: a joint estimation of injury severity, crash type, vehicle damage and driver error”, Accid. Ana. Prev., 125, pp. 188-197, (2019).
[22] Rasaizadi, A. and M. Askari, “Effect of family structure on urban areas modal split by using the life cycle concept”, Int. J. Hum. Capital Urban Manage., 5(2), pp. 165-174, (2020).
[23] Qi, Z., S. Lim, and T. Hossein Rashidi, “Assessment of transport equity to Central Business District (CBD) in Sydney, Australia”, Transport. Lettr., 12(4), pp. 246-256, (2020).
[24] Hensher, D.A., J.M. Rose, and W.H. Greene, “Applied choice analysis: a primer”, Cambridge University Press, (2005).
Scientia Iranica
Volume 28, Issue 5
Transactions on Civil Engineering (A)
September and October 2021
Pages 2471-2478

Files

Share

How to cite

Statistics