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Journal articles on the topic 'Choice modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Huybers, Twan. "Destination Choice Modelling: What's in a Name?" Tourism Economics 11, no. 3 (2005): 329–50. http://dx.doi.org/10.5367/000000005774352999.

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Discrete choice modelling can be employed to analyse tourists' holiday destination choices. The data for a destination choice modelling analysis are often obtained from a survey in which prospective tourists indicate their choices in a series of hypothetical scenarios. In that context, an issue deserving investigation is the use of destination names in the stated choice task. Using the names of destinations to label the alternatives in a choice scenario would be expected to enhance the predictive validity of the choice model. On the other hand, the experimental design of the choice scenarios m

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2

Roberts, D. R. "Modelling customer choice." Journal of Revenue and Pricing Management 1, no. 4 (2003): 369–78. http://dx.doi.org/10.1057/palgrave.rpm.5170040.

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3

Huybers, Twan. "Domestic tourism destination choices ? a choice modelling analysis." International Journal of Tourism Research 5, no. 6 (2003): 445–59. http://dx.doi.org/10.1002/jtr.450.

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4

Thill, Jean-Claude. "Choice set formation for destination choice modelling." Progress in Human Geography 16, no. 3 (1992): 361–82. http://dx.doi.org/10.1177/030913259201600303.

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5

Fotheringham, A. S. "Modelling Hierarchical Destination Choice." Environment and Planning A: Economy and Space 18, no. 3 (1986): 401–18. http://dx.doi.org/10.1068/a180401.

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The production-constrained gravity formulation is shown to be an especially inaccurate specification of reality whenever the selection of destinations by individuals results from a hierarchical choice process. Hierarchical decisionmaking violates the Independence from Irrelevant Alternatives property embedded in the theoretical derivation of the production-constrained gravity model from choice axioms. Various aspects of gravity model misspecification resulting from hierarchical destination choice are investigated and an empirical example is given in terms of US migrants. A discussion is presen

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6

Timmermans, H., and P. van der Waerden. "Modelling Sequential Choice Processes: The Case of Two-Stop Trip Chaining." Environment and Planning A: Economy and Space 24, no. 10 (1992): 1483–90. http://dx.doi.org/10.1068/a241483.

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Traditional decompositional preferences and choice studies are focused on the prediction of single choices, such as choice of shopping centre or transport mode. Discrete choice experiments are used to derive choice models that predict the probability of choosing a choice alternative as a function of its attributes. In this paper these traditional models are extended by addressing the problem of sequential choice behaviour. It is demonstrated how discrete choice experiments and universal logit models may be used to predict a choice sequence. The approach is illustrated for the problem of trip c

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7

Li, Dawei, Tomio Miwa, and Takayuki Morikawa. "Considering En-Route Choices in Utility-Based Route Choice Modelling." Networks and Spatial Economics 14, no. 3-4 (2014): 581–604. http://dx.doi.org/10.1007/s11067-014-9255-5.

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8

Gönsch, Jochen, Robert Klein, and Claudius Steinhardt. "Discrete Choice Modelling (Teil I)." WiSt - Wirtschaftswissenschaftliches Studium 37, no. 7 (2008): 356–62. http://dx.doi.org/10.15358/0340-1650-2008-7-356.

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9

Gönsch, Jochen, Robert Klein, and Claudius Steinhardt. "Discrete Choice Modelling (Teil II)." WiSt - Wirtschaftswissenschaftliches Studium 37, no. 8 (2008): 412–18. http://dx.doi.org/10.15358/0340-1650-2008-8-412.

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10

Rungie, Campbell M., Leonard V. Coote, and Jordan J. Louviere. "Structural Choice Modelling: Theory and Applications to Combining Choice Experiments." Journal of Choice Modelling 4, no. 3 (2011): 1–29. http://dx.doi.org/10.1016/s1755-5345(13)70040-x.

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11

Arencibia, Ana Isabel, María Feo-Valero, Leandro García-Menéndez, and Concepción Román. "Modelling mode choice for freight transport using advanced choice experiments." Transportation Research Part A: Policy and Practice 75 (May 2015): 252–67. http://dx.doi.org/10.1016/j.tra.2015.03.027.

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12

van Middelkoop, Manon, Aloys W. J. Borgers, and H. J. P. Timmermans. "Modelling Tourist Destination Choice Using a Decision Table Induction Algorithm." Environment and Planning A: Economy and Space 35, no. 9 (2003): 1669–87. http://dx.doi.org/10.1068/a35182.

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The authors report on the development and test of a rule-based model of tourist destination choice, using decision tables to represent the rules that specify the conditions under which particular destination choices are made. Rules are extracted from empirical data on tourist destination choice, collected in the Netherlands in 1998. The specification of the model and its performance are discussed.

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13

Oral, Muhittin, and Ossama Kettani. "Modelling the Process of Multiattribute Choice." Journal of the Operational Research Society 40, no. 3 (1989): 281. http://dx.doi.org/10.2307/2583341.

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14

Beheshtitabar, Ehsan, Sergi Aguilar Ríos, David König-Hollerwöger, Zdeněk Svatý, and Clas Rydergren. "ROUTE CHOICE MODELLING FOR BICYCLE TRIPS." INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING 4, no. 2 (2014): 194–209. http://dx.doi.org/10.7708/ijtte.2014.4(2).06.

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15

Bashir, Jasmeena, Sarafaraz Equbal, and Bisma Raina. "The Choice Modelling Approach: A Review." ECS Transactions 107, no. 1 (2022): 15081–97. http://dx.doi.org/10.1149/10701.15081ecst.

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Among the set of stated preference methods, the Choice Experiments (CES) is now widely applied to diverse environmental goods where market failure exists. This method has found its success in resolving some methodological issues of Contingent Valuation Method (CVM). Choice experiment (CE) is the method which has greatest number of applications where people select the best favored substitute among the set of substitutes by simply ranking and the rating scales. Despite many limitations or methodological issues arising due to hypothetical nature of stated preference surveys, this method could be

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16

Gostilovich, A. O. "Consumer Choice Modelling in Sharing Economy." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1153–61. http://dx.doi.org/10.47059/revistageintec.v11i2.1744.

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Development of sharing economy creates new challenges and opens unprecedented business opportunities. In this economic environment, industrial enterprises can expand their direct selling strategies with the new business model “product as a service”. This option is the result of a shift in consumer preferences among clients of industrial enterprises. The development of the consumer choice model applied to sharing economy is a topical agenda, perhaps now more than ever. Such a model, if available, would help predict multiple scenarios of consumer behaviour and prepare the manufacturing companies

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17

Oral, Muhittin, and Ossama Kettani. "Modelling the Process of Multiattribute Choice." Journal of the Operational Research Society 40, no. 3 (1989): 281–91. http://dx.doi.org/10.1057/jors.1989.38.

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18

Varlamov, A. A., D. L. Yakobchuk, and I. A. Lozhkin. "Choice of Factors Modelling of Concrete." IOP Conference Series: Materials Science and Engineering 661 (November 20, 2019): 012087. http://dx.doi.org/10.1088/1757-899x/661/1/012087.

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19

Wiley, James B., and Harry J. P. Timmermans. "Modelling Portfolio Choice in Transportation Research." Transport Reviews 29, no. 5 (2009): 569–86. http://dx.doi.org/10.1080/01441640902829462.

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20

Cardoso, Antonio, Mario de Araujo, and Eduarda Coquet. "Modelling children's choice decisions of clothing." Journal of Fashion Marketing and Management: An International Journal 12, no. 3 (2008): 415–28. http://dx.doi.org/10.1108/13612020810889344.

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21

Van Camp, Arthur, and Enrique Miranda. "Modelling epistemic irrelevance with choice functions." International Journal of Approximate Reasoning 125 (October 2020): 49–72. http://dx.doi.org/10.1016/j.ijar.2020.06.010.

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22

Bujosa Bestard, Angel, and Antoni Riera Font. "Environmental diversity in recreational choice modelling." Ecological Economics 68, no. 11 (2009): 2743–50. http://dx.doi.org/10.1016/j.ecolecon.2009.05.016.

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23

Ryan, Mandy, and Diane Skåtun. "Modelling non-demanders in choice experiments." Health Economics 13, no. 4 (2004): 397–402. http://dx.doi.org/10.1002/hec.821.

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24

Madan, Dilip B. "Utility correlations in probabilistic choice modelling." Economics Letters 20, no. 3 (1986): 241–45. http://dx.doi.org/10.1016/0165-1765(86)90030-3.

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25

Skinner, Dawne, and John Blake. "Modelling consumers’ choice of novel food." PLOS ONE 18, no. 8 (2023): e0290169. http://dx.doi.org/10.1371/journal.pone.0290169.

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A variety of approaches to reducing the environmental impact of food production and consumption are being explored including technological solutions, such as food produced via biotechnological processes. However, the development of these technologies requires significant upfront investment and consumer acceptance is not guaranteed. The purpose of this research is to develop a system dynamics model to forecast demand, under multiple marketing and quality scenarios, for foods produced via novel technologies, using cellular agriculture as a case study. The model considers consumer heterogeneity,

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26

Moller, Mathias, and Sebastián Raveau. "Behavioural modelling of metro car choice." Transportation Research Part A: Policy and Practice 180 (February 2024): 103970. http://dx.doi.org/10.1016/j.tra.2024.103970.

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27

Zhang, Hengyang, Jason Hawkins, and Khandker Nurul Habib. "A joint model of place of residence (POR) and place of work (POW): Making use of Gibbs sampling technique to overcome arbitrary assumptions in contexts of data limitation." Journal of Transport and Land Use 12, no. 1 (2019): 873–92. http://dx.doi.org/10.5198/jtlu.2019.1624.

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Place or residence (POR) and place of work (POW) are two spatial pivots defining patterns of travel behavior. These choices are considered part of long-term choice influencing short-term daily travel choices. Hence, POR-POW distributions are input into almost all daily travel demand models. However, in many cases, POW-POR is modelled in an ad-hoc way considering the gravity-based or entropy is maximizing aggregate modelling approach. Lack of data on the sequence of choices related to POR and POW is often blamed for avoiding using disaggregate choice model. Recognizing such data limitation, thi

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28

Moslem, Sarbast, Tiziana Campisi, Agnieszka Szmelter-Jarosz, Szabolcs Duleba, Kh Md Nahiduzzaman, and Giovanni Tesoriere. "Best–Worst Method for Modelling Mobility Choice after COVID-19: Evidence from Italy." Sustainability 12, no. 17 (2020): 6824. http://dx.doi.org/10.3390/su12176824.

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All countries have suffered from the COVID-19 crisis; the pandemic has adversely impacted all sectors. In this study, we examine the transport sector with a specific focus on the problem of commuting mode choice and propose a new decision-making approach for the alternative modes after synthesizing expert opinions. As a methodology, a customized model of the recently developed best–worst method (BWM) is used to evaluate mobility choice alternatives. The survey reflects citizens’ opinions toward mobility choices in two Italian cities, Palermo and Catania, before and during the pandemic. BWM is

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29

Timmermans, H. J. P. "A Stated Choice Model of Sequential Mode and Destination Choice Behaviour for Shopping Trips." Environment and Planning A: Economy and Space 28, no. 1 (1996): 173–84. http://dx.doi.org/10.1068/a280173.

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Stated preference and choice models currently used in urban planning are focused on predicting single choices. In this paper the intention is to extend these modelling approaches to the case of sequential choice behaviour. Design strategies and model specifications that allow one to predict sequential choice are discussed. The approach is illustrated in a study of sequential mode and destination choice behaviour for shopping trips. The research findings suggest that the proposed approach may be a valuable extension of currently available stated preference and choice methods to analyse more com

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30

Windle, Jill, and John Rolfe. "Diversification choices in agriculture: a Choice Modelling case study of sugarcane growers." Australian Journal of Agricultural and Resource Economics 49, no. 1 (2005): 63–74. http://dx.doi.org/10.1111/j.1467-8489.2005.00279.x.

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31

Leong, Joseph, Neema Nassir, Seyed Sina Mohri, and Majid Sarvi. "A dynamic discrete choice modelling approach for forward-looking travel mode choices." Transportation Research Part A: Policy and Practice 190 (December 2024): 104272. http://dx.doi.org/10.1016/j.tra.2024.104272.

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32

Wahyuni, Sisri, A. Muri Yusuf, and Z. Mawardi Efendi. "The Effectiveness of Group Guidance with Modelling in Increasing The Maturity of Career Choice." International Journal of Research in Counseling and Education 1, no. 1 (2018): 37. http://dx.doi.org/10.24036/0011za0002.

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The maturity of the career choice direction becomes a central idea in the career development of the students. There are still many students who are less thorough in understanding career information. Therefore, the purpose of this study is to look at the effectiveness of group counseling services with modelling in improving student career choices. The research used quasi experimental approach through the design of non equivalent control group. Research subjects seven students experimental group and seven students control group. This research used career maturity choice with reliability 0.86. Th

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33

Kramberger, Tomaž, Bojan Rupnik, Gregor Štrubelj, and Klemen Prah. "Port Hinterland Modelling Based on Port Choice." PROMET - Traffic&Transportation 27, no. 3 (2015): 195–203. http://dx.doi.org/10.7307/ptt.v27i3.1611.

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This paper presents a new approach for hinterland modelling based on the results of port choice modelling. The paper follows the idea that the shippers’ port choice is a trade-off between various objective and subjective factors. The presented model tackles the problem by applying the AHP method in order to obtain ports’ preference rates based on subjective factors, and combine them with objective factors, which include port operation costs, sailing times, and land transport costs using MILP. The ports’ hinterlands are modelled by finding the optimal port of choice for different locations acro

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34

Neumann, Oliver, and Adrian Ritz. "Public service motivation and rational choice modelling." Public Money & Management 35, no. 5 (2015): 365–70. http://dx.doi.org/10.1080/09540962.2015.1061179.

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35

Concu, Giovanni B. "Measuring Environmental Externality Spillovers through Choice Modelling." Environment and Planning A: Economy and Space 41, no. 1 (2009): 199–212. http://dx.doi.org/10.1068/a39266.

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36

Glenk, Klaus, and Sergio Colombo. "Modelling outcome-related risk in choice experiments." Australian Journal of Agricultural and Resource Economics 57, no. 4 (2013): 559–78. http://dx.doi.org/10.1111/1467-8489.12012.

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37

Yeo, Gi-Tae, Adolf K. Y. Ng, Paul Tae-Woo Lee, and Zaili Yang. "Modelling port choice in an uncertain environment." Maritime Policy & Management 41, no. 3 (2013): 251–67. http://dx.doi.org/10.1080/03088839.2013.839515.

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38

Fitzgerald, PE, and M. Aristides. "PMC18 STATISTICAL ISSUES IN DISCRETE CHOICE MODELLING." Value in Health 7, no. 6 (2004): 774. http://dx.doi.org/10.1016/s1098-3015(10)66065-2.

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39

Fischer, Manfred M., and Peter Nijkamp. "From static towards dynamic discrete choice modelling." Regional Science and Urban Economics 17, no. 1 (1987): 3–27. http://dx.doi.org/10.1016/0166-0462(87)90066-4.

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40

Roh, Hyuk-Jae, and Ata M. Khan. "Enhancing algorithmic base for discrete choice modelling." KSCE Journal of Civil Engineering 17, no. 7 (2013): 1798–809. http://dx.doi.org/10.1007/s12205-013-0318-9.

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41

González-Benito, Óscar. "Overcoming data limitations for store choice modelling." Journal of Retailing and Consumer Services 9, no. 5 (2002): 259–68. http://dx.doi.org/10.1016/s0969-6989(01)00035-2.

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42

Crouch, Geoffrey I., Timothy M. Devinney, Jordan J. Louviere, and Towhidul Islam. "Modelling consumer choice behaviour in space tourism." Tourism Management 30, no. 3 (2009): 441–54. http://dx.doi.org/10.1016/j.tourman.2008.07.003.

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43

Magala, Mateus, and Adrian Sammons. "A New Approach to Port Choice Modelling." Maritime Economics & Logistics 10, no. 1-2 (2008): 9–34. http://dx.doi.org/10.1057/palgrave.mel.9100189.

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44

Cantillo, Víctor, and Juan de Dios Ortúzar. "Implications of Thresholds in Discrete Choice Modelling." Transport Reviews 26, no. 6 (2006): 667–91. http://dx.doi.org/10.1080/01441640500487275.

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45

Ottomanelli, Michele, Mauro Dell'Orco, and Domenico Sassanelli. "Modelling parking choice behaviour using Possibility Theory." Transportation Planning and Technology 34, no. 7 (2011): 647–67. http://dx.doi.org/10.1080/03081060.2011.602846.

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46

Dibb, S. "Modelling in new housing choice—An application." Omega 22, no. 6 (1994): 589–600. http://dx.doi.org/10.1016/0305-0483(94)90050-7.

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47

Smith, Tony E. "Spatial interaction modelling and residential choice analysis." Transportation Research Part A: General 22, no. 6 (1988): 470–71. http://dx.doi.org/10.1016/0191-2607(88)90052-0.

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48

Prave, Rose Sebastianelli, and J. Keith Ord. "An adaptive approach for modelling multiattribute choice." Journal of Multi-Criteria Decision Analysis 4, no. 2 (1995): 91–106. http://dx.doi.org/10.1002/mcda.4020040203.

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49

Vyvere, Yves Van De. "Stated preference decompositional modelling and residential choice." Geoforum 25, no. 2 (1994): 189–202. http://dx.doi.org/10.1016/0016-7185(94)90015-9.

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50

Grammig, Joachim, Reinhard Hujer, and Michael Scheidler. "Discrete choice modelling in airline network management." Journal of Applied Econometrics 20, no. 4 (2005): 467–86. http://dx.doi.org/10.1002/jae.799.

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