Decisional processing on parking behavior in entropic settings

Moreno Ferrarese

doi:10.5604/01.3001.0009.7377

Authors

Moreno Ferrarese University of Trento, Department of Economics and Management, Trento, Italy Author

DOI:

https://doi.org/10.5604/01.3001.0009.7377

Keywords:

urban parking lot, discrete models, choice model, decision making models

Abstract

This paper surveys the most recent advances in the context of decisional processing with focusing on the parking behavior in entropic settings, including the measures and the necessary mechanisms for the interaction of the actors-players, and their connection to decisional processing theory. The aim of this article is to provide a critical review of the most fashionable models and methods in parking lot financial design: the first class of methods covers the approach of analysis with the random entropic model; the second class of methods is the decisional processing through rational choice models as rational individual evaluations. Both techniques are described in detail in sections; we illustrate them using the well-known and easy multimodal problem approach and then we present the advanced applications. Thus, it is possible to identify all strong and weak points of the models and to compare them for a best feasible solution for parking lot economic and financial design. Taking into account a close equivalence between the aggregate methods of entropy maximization and disaggregated microeconomic method of discrete choice models, based on random utility theory, we try to provide a critical approach of it through the rational choice models and to underline the possible benefit of it for the problem decision.

References

ABRAMOWITZ, M. & STEGUN, I. A., 1972. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 9th printing. New York: Dover, pp. 652-653.

ARENTZE, T. A. & DELLAERT, B. G. & CHORUS, C. G., 2014. Incorporating Mental Representations in Discrete Choice Models of Travel behavior: Modeling Approach and Empirical Application, Transportation Science.

AVINERI, E. & BEN-ELIA, E., 2015. Prospect Theory and its Applications to the Modelling of Travel Choice. Bounded Rational Choice behavior: Applications in Transport, p. 233.

AXHAUSEN, K. W. & KOWALD, M. (Eds.), 2015. Social networks and travel behavior. Ashgate Publishing Ltd.

BAJARI, P. & NEKIPELOV, D. & RYAN, S. P. & YANG, M., 2015. Demand estimation with machine learning and model combination. National Bureau of Economic Research, Nr. w20955.

BEN-AKIVA, M. E. & LERMAN, S. R., 1985. Discrete choice analysis: theory and application to travel demand, Nr. 9, MIT press.

BIFULCO, G.N., 1993. A stochastic user equilibrium assignment model for the evaluation of parking policies. European Journal of Operational Research, Nr. 71, pp. 269-287.

BOLLEN, K. A., 2014. Structural equations with latent variables. John Wiley & Sons.

BRADLEY M. & KROES, E. & HINLOOPEN E., 1993. A joint model of mode/parking type choice with supply-constrained application. Proceedings of the 21st Annual Summer PTRC Meeting on European Transport Highways and Planning, pp. 61-73.

CAMPANELLA, F., 1977. L’Economia neoclassica, ISEDI, pp. 67-69.

CANTARELLA, G. E. & DE LUCA, S. & DI GANGI, M. & DI PACE, R., 2015. Approaches for solving the stochastic equilibrium assignment with variable demand: internal vs. external solution algorithms. Optimization Methods and Software, Vol. 30. Nr. 2, pp. 338-364.

CHEN, W. Q.& GRAEDEL, T. E., 2012. Anthropogenic cycles of the elements: A critical review. Environmental science & technology, Vol. 46. No.16, p. 8574-8586.

COHEN, J. & COHEN, P. & WEST, S. G. & AIKEN, L. S., 2013. Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.

DAGANZO, C., 2014. Multinomial probit: the theory and its application to demand forecasting. Elsevier.

De Grange, L. & González, F. & Vargas, I. & Muñoz, J. C., 2013. A polarized Logit model. Transportation Research part A: policy and practice, Nr. 53, pp. 1-9.

DONG, Y. & LEWBEL, A., 2015. A simple estimator for binary choice models with endogenous regressors. Econometric Reviews, Vol. 34. No. (1-2), pp. 82-105.

FAZIO, R. H. & PIETRI, E. S. & ROCKLAGE, M. D. & SHOOK, N. J,. 2015. Chapter Three-Positive Versus Negative Valence: Asymmetries in Attitude Formation and Generalization as Fundamental Individual Differences. Advances in Experimental Social Psychology, Nr. 51, pp. 97-146.

FERGUSON, T. S., 2014. Mathematical statistics: A decision theoretic approach. Academic press.

FERRARESE, M., 2016. A perceptual-behavioural approach with a non-parametric experimental coefficient for urban parking design. Archives of Transport, (37)1, pp. 15-30.

FINNIS, J., 2011. Intention and Identity: Collected Essays. Oxford University Press. Vol. 2.

GALATIOTO, F. & HUANG, Y. & PARRY, T. &, BIRD, R. & BELL, M., 2015. Transportation Research Part D.

HOYOS, D. & MARIEL, P. & HESS, S., 2015. Incorporating environmental attitudes in discrete choice models: An exploration of the utility of the awareness of consequences scale. Science of The Total Environment, Nr. 505, pp.1100-1111.

HUANG, Y.& SMITH, B. & OLARU, D. & TAPLIN, J., 2015. Modelling travellers’ choice between park-and-ride and other modes of travel to work in the context of risk and uncertainty. Transportation Research Board.

KRUGLANSKI, A. W. & CHERNIKOVA, M. & KOPETZ, C., 2015. Motivation Science. Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource. Wiley Online Library.

LEBO, M. J. & WEBER, C., 2015. An Effective Approach to the Repeated Cross Sectional Design. American Journal of Political Science, Vol. 9, Nr. 1, pp. 242-258.

MIRABI, V. & FADIHE, Z. A., 2015. Investigating the effect of triple perceptual-behavioral traps on marketing decisions (case study: Green Pipe manufacturing factories in Isfahan). Journal of Scientific Research and Development, 2 (7), pp. 148-153.

NETH, H. & GIGERENZER, G., 2015. Heuristics: Tools for an uncertain world. Emerging Trends in the Social and behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource. Wiley Online Library.

NIJKAMP, P. & REGGIANI, A., 1988. Entropy, Spatial Interactions Models and Discrete Choice Analysis: Static and Dynamic Analogies. European Journal of Operational Research, Nr. 36, pp. 186-196.

NUZZOLO, A. & CRISALLI, U. & COMI, A., & ROSATI, L., 2015. Individual behavioral models for personal transit pre-trip planners. Transportation Research Procedia, Nr. 5, pp. 30-43.

OPPENHEIMER, D. M. & KELSO, E., 2015. Information Processing as a Paradigm for Decision Making. Annual review of psychology, Nr. 66, pp. 277-294.

PERLOFF, J., 2008. Microeconomics Theory & Applications with Calculus. Boston: Pearson.

SPIEGLER, R., 2015. Choice Complexity and Market Competition. Working paper.

STIGLER, G. J. & BECKER, G. S., 1977. De gustibus non est disputandum. The american economic review, pp. 76-90.

SWAIT, J. D., 2011. Discrete choice theory and modeling. The Oxford Handbook of the Economics of Food Consumption and Policy, p. 119.

TRAIN, K., 2002. Discrete choice methods with simulation. Cambridge (U.K.): Cambridge University Press.

WOO, J. Y. & BAE, S. M. & PARK, S. C., 2005.Visualization method for customer targeting using customer map. Expert Systems with Applications, Vol. 28, Nr. 4, pp. 763-772.

WU, H. & BROWNE, M. W., 2015.Quantifying adventitious error in a covariance structure as a random effect. Psychometrika, pp. 1-30.

XIONG, C. & CHEN, X. & HE, X. & GUO, W. & ZHANG, L., 2015. The Analysis of Dynamic Travel Mode Choice: A Heterogeneous Hidden Markov Approach. In: Transportation Research Board 94th Annual Meeting, Nr. 15-5211.