Abstract
Models of geographical choice behavior have been dominantly based on rational choice models, which assume that decision makers are utility-maximizers. Rational choice models may be less appropriate as behavioral models when modeling decisions in complex environments in which decision makers may simplify the decision problem using heuristics. Pedestrian behavior in shopping streets is an example. We therefore propose a modeling framework for pedestrian shopping behavior incorporating principles of bounded rationality. We extend three classical heuristic rules (conjunctive, disjunctive and lexicographic rule) by introducing threshold heterogeneity. The proposed models are implemented using data on pedestrian behavior in Wang Fujing Street, the city center of Beijing, China. The models are estimated and compared with multinomial logit models and mixed logit models. Results show that the heuristic models are the best for all the decisions that are modeled. Validation tests are carried out through multi-agent simulation by comparing simulated spatio-temporal agent behavior with the observed pedestrian behavior. The predictions of heuristic models are slightly better than those of the multinomial logit models.
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References
Antonini G, Bierlaire M, Weber M (2006) Discrete choice models of pedestrian walking behavior. Transp Res B 40(8):667–687
Arentze T, Borgers A, Timmermans H (1993) A model of multi-purpose shopping trip behaviour. Pap Reg Sci 72(3):239–256
Bandini S, Federici ML, Manzoni S, Vizzari G (2006) Towards a methodology for situated cellular agent based crowd. In: Engineering societies in the agents world VI. Springer, Berlin, pp 203–220
Bettman JR, Luce MF, Payne JW (1998) Constructive consumer choice processes. J Consum Res 25(3):187–217
Borgers AWJ, Timmermans HJP (1986) A model of pedestrian route choice and demand for retail facilities within inner-city shopping areas. Geogr Anal 18(2):115–128
Dawes RM (1964) Social selection based on multidimensional criteria. J Abnorm Soc Psychol 68(1):104–109
Dijkstra J, Timmermans H (2002) Towards a multi-agent model for visualizing simulated user behavior to support the assessment of design performance. Autom Constr 11(2):135–145
Fishburn PC (1974) Lexicographic orders, utilities and decision rules: a survey. Manage Sci 20(11):1442–1471
Foerster JF (1979) Mode choice decision process models: a comparison of compensatory and non-compsatory structures. Transp Res A 13(1):17–28
Gibson M, Pullen M (1972) Retail turnover in the east midlands: a regional application of a gravity model. Reg Stud 6(2):183–196
Gigerenzer G, Todd PM, ABC Research Group (1999) Simple heuristics that make us smart. Oxford University Press, New York
Guy CM (1987) Recent advances in spatial interaction modelling: an application to the forecasting of shopping travel. Environ Plann A 19(2):173–186
Hagishima S, Mitsuyoshi K, Kurose S (1987) Estimation of pedestrian shopping trips in a neighbourhood by using a spatial interaction model. Environ Plann A 19(9):1139–1153
Haklay M, O’Sullivan D, Thurstain-Goodwin M (2001) ‘So go downtown’: simulating pedestrian movement in town centers. Environ Plann B 28(3):343–359
Hoogendoorn SP, Bovy PHL (2004) Pedestrian route-choice and activity scheduling theory and models. Transp Res B 38(2):169–190
Lin TH, Dayton CM (1997) Model selection information criteria for non-nested latent class models. J Educ Behav Stat 22(3):249–264
McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142
Payne JW (1976) Task complexity and contingent processing in decision making: an information search and protocol analysis. Organ Behav Hum Perform 16(2):366–387
Payne JW, Bettman JR, Johnson EJ (1988) Adaptive strategy selection in decision making. J Exp Psychol Learn Mem Cogn 14(3):534–552
Simon H (1959) Theories of decision-making in economics and behavioral science. Am Econ Rev 49(3):253–283
Timmermans H, Borgers A (1985) Choice set constrains and spatial decision-making processes. Sistemi Urbani 3:211–220
Train KE (2003) Discrete choice methods with simulation. Cambridge University Press, Cambridge, UK
Wilson AG (1971) A family of spatial interaction models, and associated developments. Environ Plann A 3(1):1–32
Zhu W, Timmermans H, Wang D (2006) Temporal variation in consumer spatial behavior in shopping streets. J Urban Plann Dev 132(3):166–171
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Zhu, W., Timmermans, H. Modeling pedestrian shopping behavior using principles of bounded rationality: model comparison and validation. J Geogr Syst 13, 101–126 (2011). https://doi.org/10.1007/s10109-010-0122-8
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DOI: https://doi.org/10.1007/s10109-010-0122-8