Bounded Rationality in Dynamic Traffic Assignment

Abstract

Purpose

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models and demonstration in an agent-based microsimulation.

Theory

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.

Findings

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 micro-simulations. 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.

Citation

Szeto, W., Wang, Y. and Han, K. (2015), "Bounded Rationality in Dynamic Traffic Assignment", Rasouli, S. and Timmermans, H. (Ed.) Bounded Rational Choice Behaviour: Applications in Transport, Emerald Group Publishing Limited, Leeds, pp. 163-187. https://doi.org/10.1108/978-1-78441-072-820151016