Hybrid choice models combine discrete-choice theory with latent psychological constructs and are now analyzed alongside machine-learning and computational approaches. However, the evolution of this research field over time, the structure of its skills, the interdependencies among its ideas, and its computational evolution have not yet been fully documented. This study is a reproducible scientometric review of hybrid choice model studies published in the Dimensions database until 2026. N unique publications remained after screening out duplicates and ineligible records. We analyzed publication trends, journal influence, author and institutional collaborations, international research networks, keyword co-occurrence, co-citation relationships, and bibliographic-coupling structures. We quantified machine-learning trends using a prespecified keyword taxonomy, annual prevalence measures, method-specific frequencies, and temporal co-occurrence analysis. The results showed a continued rise in HCM research and its focus in transportation research, a shift in focus toward health and environmental decision-making, and a growing focus on specific machine-learning methods. The findings are not a direct measure of the scientific quality of the articles or predictive of their superiority and should be viewed in terms of publication, citation, thematic and collaboration patterns between articles. The study offered a repeatable snapshot of HCM research and opportunities to integrate interpretable, externally validated machine learning behavioral choice theory in a computationally scalable way.
Citation: Mashail M. AL Sobhi. Mapping the evolution of hybrid choice model research: A Scientometric review of intellectual structure, machine-learning trends, and global collaboration[J]. AIMS Mathematics, 2026, 11(9): 30858-30885. doi: 10.3934/math.20261222
Hybrid choice models combine discrete-choice theory with latent psychological constructs and are now analyzed alongside machine-learning and computational approaches. However, the evolution of this research field over time, the structure of its skills, the interdependencies among its ideas, and its computational evolution have not yet been fully documented. This study is a reproducible scientometric review of hybrid choice model studies published in the Dimensions database until 2026. N unique publications remained after screening out duplicates and ineligible records. We analyzed publication trends, journal influence, author and institutional collaborations, international research networks, keyword co-occurrence, co-citation relationships, and bibliographic-coupling structures. We quantified machine-learning trends using a prespecified keyword taxonomy, annual prevalence measures, method-specific frequencies, and temporal co-occurrence analysis. The results showed a continued rise in HCM research and its focus in transportation research, a shift in focus toward health and environmental decision-making, and a growing focus on specific machine-learning methods. The findings are not a direct measure of the scientific quality of the articles or predictive of their superiority and should be viewed in terms of publication, citation, thematic and collaboration patterns between articles. The study offered a repeatable snapshot of HCM research and opportunities to integrate interpretable, externally validated machine learning behavioral choice theory in a computationally scalable way.
| [1] |
K. Ashok, W. R. Dillon, S. Yuan, Extending discrete choice models to incorporate attitudinal and other latent variables, J. Mark. Res., 39 (2002), 31–46. https://doi.org/10.1509/jmkr.39.1.31.18937 doi: 10.1509/jmkr.39.1.31.18937
|
| [2] |
M. Ben-Akiva, D. McFadden, K. Train, J. Walker, C. Bhat, M. Bierlaire, et al., Hybrid choice models: Progress and challenges, Mark. Lett., 13 (2002), 163–175. https://doi.org/10.1023/A:1020254301302 doi: 10.1023/A:1020254301302
|
| [3] | M. Ben-Akiva, J. Walker, A. T. Bernardino, D. A. Gopinath, T. Morikawa, A. Polydoropoulou, Integration of choice and latent variable models, In: Perpetual motion: Travel behaviour research opportunities and application challenges, Amsterdam: Elsevier, 2002,431–470. https://doi.org/10.1016/B978-008044044-6/50022-X |
| [4] | D. Bolduc, R. Alvarez-Daziano, On estimation of hybrid choice models, In: Choice modelling: The state-of-the-art and the state-of-practice, Bingley: Emerald Group Publishing, 2010,259–287. https://doi.org/10.1108/9781849507738-011 |
| [5] |
A. Borriello, J. M. Rose, Global versus localised attitudinal responses in discrete choice, Transportation, 48 (2021), 131–165. https://doi.org/10.1007/s11116-019-10045-3 doi: 10.1007/s11116-019-10045-3
|
| [6] |
A. Borriello, P. F. Burke, J. M. Rose, If one goes up, another must come down: A latent class hybrid choice modelling approach for understanding electricity mix preferences among renewables and non-renewables, Energy Policy, 159 (2021), 112611. https://doi.org/10.1016/j.enpol.2021.112611 doi: 10.1016/j.enpol.2021.112611
|
| [7] |
A. Borriello, S. Scagnolari, J. M. Rose, Reducing the randomness of latent variables using the evaluative space grid: Implementation in a hybrid choice model, Transp. Res. Part F Traffic Psychol. Behav., 62 (2019), 192–211. https://doi.org/10.1016/j.trf.2018.12.018 doi: 10.1016/j.trf.2018.12.018
|
| [8] |
J. Buckell, D. A. Hensher, S. Hess, Kicking the habit is hard: A hybrid choice model investigation into the role of addiction in smoking behavior, Health Econ., 30 (2021), 3–19. https://doi.org/10.1002/hec.4173 doi: 10.1002/hec.4173
|
| [9] |
E. Cherchi, A stated choice experiment to measure the effect of informational and normative conformity in the preference for electric vehicles, Transp. Res. Part A Policy Pract., 100 (2017), 88–104. https://doi.org/10.1016/j.tra.2017.04.009 doi: 10.1016/j.tra.2017.04.009
|
| [10] |
S. Choo, P. L. Mokhtarian, What type of vehicle do people drive? The role of attitude and lifestyle in influencing vehicle type choice, Transp. Res. Part A Policy Pract., 38 (2004), 201–222. https://doi.org/10.1016/j.tra.2003.10.005 doi: 10.1016/j.tra.2003.10.005
|
| [11] |
C. G. Chorus, M. Kroesen, On the (im-)possibility of deriving transport policy implications from hybrid choice models, Transp. Policy, 36 (2014), 217–222. https://doi.org/10.1016/j.tranpol.2014.09.001 doi: 10.1016/j.tranpol.2014.09.001
|
| [12] |
R. A. Daziano, Taking account of the role of safety on vehicle choice using a new generation of discrete choice models, Saf. Sci., 50 (2012), 103–112. https://doi.org/10.1016/j.ssci.2011.07.007 doi: 10.1016/j.ssci.2011.07.007
|
| [13] |
R. A. Daziano, D. Bolduc, Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model, Transp. A Transp. Sci., 9 (2013), 74–106. https://doi.org/10.1080/18128602.2010.524173 doi: 10.1080/18128602.2010.524173
|
| [14] |
M. Haghani, M. C. J. Bliemer, D. A. Hensher, The landscape of econometric discrete choice modelling research, J. Choice Model., 40 (2021), 100303. https://doi.org/10.1016/j.jocm.2021.100303 doi: 10.1016/j.jocm.2021.100303
|
| [15] |
S. Haustein, M. Thorhauge, E. Cherchi, Commuters' attitudes and norms related to travel time and punctuality: A psychographic segmentation to reduce congestion, Travel Behav. Soc., 12 (2018), 41–50. https://doi.org/10.1016/j.tbs.2018.04.001 doi: 10.1016/j.tbs.2018.04.001
|
| [16] |
S. Hess, Rethinking heterogeneity: The role of attitudes, decision rules and information processing strategies, Transp. Lett., 4 (2012), 105–113. https://doi.org/10.3328/TL.2012.04.02.105-113 doi: 10.3328/TL.2012.04.02.105-113
|
| [17] |
S. Hess, D. A. Hensher, Making use of respondent reported processing information to understand attribute importance: A latent variable scaling approach, Transportation, 40 (2013), 397–412. https://doi.org/10.1007/s11116-012-9420-y doi: 10.1007/s11116-012-9420-y
|
| [18] |
S. Hess, J. W. Polak, Mixed logit modelling of airport choice in multi-airport regions, J. Air Transp. Manag., 11 (2005), 59–68. https://doi.org/10.1016/j.jairtraman.2004.09.001 doi: 10.1016/j.jairtraman.2004.09.001
|
| [19] |
M. V. Johansson, T. Heldt, P. Johansson, The effects of attitudes and personality traits on mode choice, Transp. Res. Part A Policy Pract., 40 (2006), 507–525. https://doi.org/10.1016/j.tra.2005.09.001 doi: 10.1016/j.tra.2005.09.001
|
| [20] | E. Kitrinou, A. Polydoropoulou, D. Bolduc, Development of integrated choice and latent variable models for the residential relocation decision in island areas, In: Choice modelling: The state-of-the-art and the state-of-practice, Bingley: Emerald Group Publishing, 2010,593–618. https://doi.org/10.1108/9781849507738-027 |
| [21] |
V. Kolarova, E. Cherchi, Impact of trust and travel experiences on the value of travel time savings for autonomous driving, Transp. Res. Part C Emerg. Technol., 131 (2021), 103354. https://doi.org/10.1016/j.trc.2021.103354 doi: 10.1016/j.trc.2021.103354
|
| [22] |
M. Kroesen, C. Chorus, The role of general and specific attitudes in predicting travel behavior—A fatal dilemma, Travel Behav. Soc., 10 (2018), 33–41. https://doi.org/10.1016/j.tbs.2017.09.004 doi: 10.1016/j.tbs.2017.09.004
|
| [23] |
M. Kroesen, C. Chorus, A new perspective on the role of attitudes in explaining travel behavior: A psychological network model, Transp. Res. Part A Policy Pract., 133 (2020), 82–94. https://doi.org/10.1016/j.tra.2020.01.014 doi: 10.1016/j.tra.2020.01.014
|
| [24] |
L. La Paix, E. Cherchi, K. Geurs, Role of perception of bicycle infrastructure on the choice of the bicycle as a train feeder mode, Int. J. Sustain. Transp., 15 (2021), 486–499. https://doi.org/10.1080/15568318.2020.1765223 doi: 10.1080/15568318.2020.1765223
|
| [25] |
U. Liebe, J. Meyerhoff, Mapping potentials and challenges of choice modelling for social science research, J. Choice Model., 38 (2021), 100270. https://doi.org/10.1016/j.jocm.2021.100270 doi: 10.1016/j.jocm.2021.100270
|
| [26] |
R. Maldonado-Hinarejos, A. Sivakumar, J. W. Polak, Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: A hybrid choice modelling approach, Transportation, 41 (2014), 1287–1304. https://doi.org/10.1007/s11116-014-9551-4 doi: 10.1007/s11116-014-9551-4
|
| [27] |
F. Manca, A. Sivakumar, J. W. Polak, The effect of social influence and social interactions on the adoption of a new technology: The use of bike sharing in a student population, Transp. Res. Part C Emerg. Technol., 105 (2019), 611–625. https://doi.org/10.1016/j.trc.2019.02.010 doi: 10.1016/j.trc.2019.02.010
|
| [28] |
F. Manca, A. Sivakumar, J. W. Polak, Capturing the effect of multiple social influence sources on the adoption of new transport technologies and services, J. Choice Model., 42 (2022), 100344. https://doi.org/10.1016/j.jocm.2022.100344 doi: 10.1016/j.jocm.2022.100344
|
| [29] |
P. Mariel, J. Meyerhoff, Hybrid discrete choice models: Gained insights versus increasing effort, Sci. Total Environ., 568 (2016), 433–443. https://doi.org/10.1016/j.scitotenv.2016.06.019 doi: 10.1016/j.scitotenv.2016.06.019
|
| [30] |
D. McFadden, The choice theory approach to market research, Mark. Sci., 5 (1986), 275–297. https://doi.org/10.1287/mksc.5.4.275 doi: 10.1287/mksc.5.4.275
|
| [31] | T. Morikawa, M. Ben-Akiva, D. McFadden, Discrete choice models incorporating revealed preferences and psychometric data, In: Advances in econometrics, Bingley: Emerald Group Publishing, 2002, 29–55. https://doi.org/10.1016/S0731-9053(02)16003-8 |
| [32] |
J. de D. Ortúzar, Future transportation: Sustainability, complexity and individualization of choices, Commun. Transp. Res., 1 (2021), 100010. https://doi.org/10.1016/j.commtr.2021.100010 doi: 10.1016/j.commtr.2021.100010
|
| [33] |
S. Rasouli, H. J. P. Timmermans, Influence of social networks on latent choice of electric cars: A mixed logit specification using experimental design data, Netw. Spat. Econ., 16 (2016), 99–130. https://doi.org/10.1007/s11067-013-9194-6 doi: 10.1007/s11067-013-9194-6
|
| [34] |
J. J. Soto, V. Cantillo, J. Arellana, Market segmentation for incentivising sustainable transport policies, Transp. Res. Part D Transp. Environ., 99 (2021), 103013. https://doi.org/10.1016/j.trd.2021.103013 doi: 10.1016/j.trd.2021.103013
|
| [35] |
E. Sottile, E. Cherchi, I. Meloni, Measuring soft measures within a stated preference survey: The effect of pollution and traffic stress on mode choice, Transp. Res. Procedia, 11 (2015), 434–451. https://doi.org/10.1016/j.trpro.2015.12.036 doi: 10.1016/j.trpro.2015.12.036
|
| [36] |
E. Sottile, I. Meloni, E. Cherchi, A hybrid discrete choice model to assess the effect of awareness and attitude towards environmentally friendly travel modes, Transp. Res. Procedia, 5 (2015), 44–55. https://doi.org/10.1016/j.trpro.2015.01.017 doi: 10.1016/j.trpro.2015.01.017
|
| [37] |
E. Sottile, I. Meloni, E. Cherchi, Hybrid choice model to disentangle the effect of awareness from attitudes: Application test of soft measures in a medium-size city, Case Stud. Transp. Policy, 5 (2017), 400–407. https://doi.org/10.1016/j.cstp.2016.09.001 doi: 10.1016/j.cstp.2016.09.001
|
| [38] |
E. Sottile, F. Piras, I. Meloni, Could a new mode alternative modify psycho-attitudinal factors and travel behavior, Transp. Res. Rec., 2673 (2019), 94–106. https://doi.org/10.1177/0361198119843478 doi: 10.1177/0361198119843478
|
| [39] |
E. Sottile, F. Piras, D. Calli, I. Meloni, Why don't Italians cycle to work? An experimental analysis, Case Stud. Transp. Policy, 9 (2021), 362–373. https://doi.org/10.1016/j.cstp.2021.01.007 doi: 10.1016/j.cstp.2021.01.007
|
| [40] |
M. Thorhauge, E. Cherchi, J. L. Walker, J. Rich, The role of intention as mediator between latent effects and behavior: Application of a hybrid choice model to study departure time choices, Transportation, 46 (2019), 1421–1445. https://doi.org/10.1007/s11116-017-9839-2 doi: 10.1007/s11116-017-9839-2
|
| [41] |
M. Thorhauge, S. Haustein, E. Cherchi, Accounting for the theory of planned behaviour in departure time choice, Transp. Res. Part F Traffic Psychol. Behav., 38 (2016), 94–105. https://doi.org/10.1016/j.trf.2016.01.009 doi: 10.1016/j.trf.2016.01.009
|
| [42] |
M. Thorhauge, J. Swait, E. Cherchi, The habit-driven life: Accounting for inertia in departure time choices for commuting trips, Transp. Res. Part A Policy Pract., 133 (2020), 272–289. https://doi.org/10.1016/j.tra.2020.01.023 doi: 10.1016/j.tra.2020.01.023
|
| [43] |
M. Thorhauge, A. Vij, E. Cherchi, Heterogeneity in departure time preferences, flexibility and schedule constraints, Transportation, 48 (2021), 1865–1893. https://doi.org/10.1007/s11116-020-10114-y doi: 10.1007/s11116-020-10114-y
|
| [44] |
E. Valeri, E. Cherchi, Does habitual behavior affect the choice of alternative fuel vehicles?, Int. J. Sustain. Transp., 10 (2016), 825–835. https://doi.org/10.1080/15568318.2016.1163445 doi: 10.1080/15568318.2016.1163445
|
| [45] |
N. J. van Eck, L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics, 84 (2010), 523–538. https://doi.org/10.1007/s11192-009-0146-3 doi: 10.1007/s11192-009-0146-3
|
| [46] |
B. van Wee, M. Kroesen, Attitude changes, modelling travel behaviour, and ex ante project evaluations, Transp. Res. Interdiscip. Perspect., 16 (2022), 100724. https://doi.org/10.1016/j.trip.2022.100724 doi: 10.1016/j.trip.2022.100724
|
| [47] | A. Vij, Understanding consumer demand for new transport technologies and services, and implications for the future of mobility, In: Data-driven multivalence in the built environment, Cham: Springer, 2020, 91–107. https://doi.org/10.1007/978-3-030-12180-8_5 |
| [48] | A. Vij, J. L. Walker, Hybrid choice models: The identification problem, In: Handbook of choice modelling, Cheltenham: Edward Elgar Publishing, 2014,519–564. https://doi.org/10.4337/9781781003152.00031 |
| [49] |
A. Vij, J. L. Walker, How, when and why integrated choice and latent variable models are latently useful, Transp. Res. Part B Methodol., 90 (2016), 192–217. https://doi.org/10.1016/j.trb.2016.04.021 doi: 10.1016/j.trb.2016.04.021
|
| [50] |
J. L. Walker, J. Li, Latent lifestyle preferences and household location decisions, J. Geogr. Syst., 9 (2007), 77–101. https://doi.org/10.1007/s10109-006-0030-0 doi: 10.1007/s10109-006-0030-0
|
| [51] |
J. L. Walker, Opening up the black box: Enriching behavioral models of spatial and travel choices, J. Transp. Geogr., 14 (2006), 396–398. https://doi.org/10.1016/j.jtrangeo.2006.06.008 doi: 10.1016/j.jtrangeo.2006.06.008
|
| [52] | J. L. Walker, M. Ben-Akiva, Advances in discrete choice: mixture models, In: A handbook of transport economics, Cheltenham: Edward Elgar Publishing, 2011,160–187. https://doi.org/10.4337/9780857930873.00015 |
| [53] |
J. L. Walker, M. Ben-Akiva, Generalized random utility model, Math. Soc. Sci., 43 (2002), 303–343. https://doi.org/10.1016/S0165-4896(02)00023-9 doi: 10.1016/S0165-4896(02)00023-9
|
| [54] |
Y. Wang, Y. Wang, L. Xie, H. Zhou, Impact of perceived uncertainty on public acceptability of congestion charging: An empirical study in China, Sustainability, 11 (2019), 129. https://doi.org/10.3390/su11010129 doi: 10.3390/su11010129
|
| [55] |
M. Wong, B. Farooq, ResLogit: A residual neural network logit model for data-driven choice modelling, Transp. Res. Part C Emerg. Technol., 126 (2021), 103050. https://doi.org/10.1016/j.trc.2021.103050 doi: 10.1016/j.trc.2021.103050
|
| [56] |
C. Wu, S. Le Vine, A. Sivakumar, J. W. Polak, Traveller preferences for free-floating carsharing vehicle allocation mechanisms, Transp. Res. Part C Emerg. Technol., 102 (2019), 1–19. https://doi.org/10.1016/j.trc.2019.02.019 doi: 10.1016/j.trc.2019.02.019
|
| [57] |
C. Wu, S. Le Vine, A. Sivakumar, J. W. Polak, Dynamic pricing of free-floating carsharing networks with sensitivity to travellers' attitudes towards risk, Transportation, 49 (2022), 679–702. https://doi.org/10.1007/s11116-021-10190-8 doi: 10.1007/s11116-021-10190-8
|
| [58] |
D. Zhao, A. Strotmann, Comparing all-author and first-author co-citation analyses of information science, J. Informetr., 2 (2008), 229–239. https://doi.org/10.1016/j.joi.2008.05.004 doi: 10.1016/j.joi.2008.05.004
|
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Mashail M. AL Sobhi. Mapping the evolution of hybrid choice model research: A Scientometric review of intellectual structure, machine-learning trends, and global collaboration[J]. AIMS Mathematics, 2026, 11(9): 30858-30885. doi: 10.3934/math.20261222
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