This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.
A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.
The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.
Originality and value
Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.
Xiong, C., Chen, X. and Zhang, L. (2015), "Multidimensional Travel Decision-Making: Descriptive Behavioural Theory and Agent-Based Models", Rasouli, S. and Timmermans, H. (Ed.) Bounded Rational Choice Behaviour: Applications in Transport, Emerald Group Publishing Limited, Bingley, pp. 213-231. https://doi.org/10.1108/978-1-78441-072-820151012
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