References
[1] Babic D, et al. Choice factors of distribution channels. J. Transp. Logist. 2010;1:5-13. https://journals.vstecb.cz/wp-content/uploads/fullissue/35.pdf [Accessed 20th Jan. 2018].
[2] Babojelić K, Novacko L. Modelling of driver and pedestrian behaviour–a historical review. Promet – Traffic&Transportation. 2020;32(5):727-745. DOI: https://doi.org/gp7q6h.
[3] Vij A, Krueger R. Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions. Transportation Research Part B: Methodological. 2017;(106):76-101. DOI: https://doi.org/gcqbd3.
[4] Thurstone LL. A law of comparative judgement. Psychological Review. 1927;(34):278-286. DOI: https://doi.org/b9pn6t.
[5] Zermelo E. The calculation of tournament results as a maximum problem of probability theory [Die Berechnung der Turnier-Ergebnisse als ein Maximumproblem der Wahrscheinlichkeitsrechnung]. Mathematical Journal [Mathematische Zeitschrift]. 1928;(29):436-460 DOI: https://doi.org/bvrnmg.
[6] Bradley RA, Terry ME. Rank analysis of incomplete block designs, I. The method of paired comparisons. Biometrika. 1952;(39):324-345. DOI: https://doi.org/c5bcq8.
[7] Train K. Mixed logit with a flexible mixing distribution. J. Choice Model. 2016;(19):40-53. DOI: https://doi.org/grgzg6.
[8] Novačko L, et al. Selection of LRT system track gauge using multi-criteria decision-making (City of Zagreb). WIT Transactions on the Built Environment. 2008;101(7):167-173. DOI: https://doi.org/dhbgs8.
[9] Novačko L, et al. Simulation-based public transport priority tailored to passenger conflict flows: A case study of the city of Zagreb. Applied Sciences. 2021;11(11):4820. DOI: https://doi.org/gp5j7b.
[10] Keane M, Wasi N. Comparing alternative models of heterogeneity in consumer choice behavior. Journal of Applied Econometrics. 2013;28(6):1018-1045. DOI: https://doi.org/ghnztj.
[11] Zhang Q, et al. Time differential pricing model of urban rail transit considering passenger exchange coefficient. Promet – Traffic&Transportation. 2022;34(4):609-618. DOI: https://doi.org/ksb9.
[12] Vasudevan N, et al. Determining mode shift elasticity based on household income and travel cost. Research in Transportation Economics. 2021;(85):100771. DOI: https://doi.org/gmt56k.
[13] Hensher DA. The sensitivity of the valuation of travel time savings to the specification of unobserved effects. Transportation Research Part E: Logistics and Transportation Review. 2001;37(2-3):129-142. DOI: https://doi.org/cw38hj.
[14] Jiang R, et al. Predicting bus travel time with hybrid incomplete data – A deep learning approach. Promet – Traffic&Transportation. 2022;34(5):673-685. DOI: https://doi.org/kscb.
[15] Sadrani M, et al. Optimisation of service frequency and vehicle size for automated bus systems with crowding externalities and travel time stochasticity. Transportation Research Part C: Emerging Technologies. 2022;143:103793. DOI: https://doi.org/jnm3.
[16] Bansal P, et al. A dynamic choice model with heterogeneous decision rules: Application in estimating the user cost of rail crowding. arXiv preprint arXiv. 2020. DOI: https://doi.org/kscc.
[17] Massobrio R, et al. Learning to optimise timetables for efficient transfers in public transportation systems. Applied Soft Computing. 2022;119:108616. DOI: https://doi.org/kscd.
[18] Manasra H, Toledo T. Optimisation-based operations control for public transportation service with transfers. Transportation Research Part C: Emerging Technologies. 2019;105:456-467. DOI: https://doi.org/kscf.
[19] Aboutaleb Y, et al. Discrete choice analysis with machine learning capabilities. arXiv preprint arXiv. 2021. DOI: https://doi.org/kscg.
[20] Bansal P, et al. Flexible estimates of heterogeneity in crowding valuation in the New York City subway. Journal of choice modelling. 2019;31:124-140. DOI: https://doi.org/gh55rs.
[21] Bansal P, et al. Comparison of parametric and semiparametric representations of unobserved preference heterogeneity in logit models. Journal of Choice Modelling. 2018;27:97-113. DOI: https://doi.org/gdmm2w.