Bounded rationality in hyperpath assignment: the locally rational traveller model.

Abstract

The concepts of optimal strategy and hyperpath were introduced in the transport field under the paradigms of equilibrium and expected utility theory, which assume perfect rationality. The initial scope for hyperpaths was frequency-based transit assignment, but increasingly they are used also for other transport problems, such as scheduled-based transit assignment and vehicle routing. Extensions to the original formulation have been developed to take into account congestion, the availability of information, and time-dependent networks. Psychological research has shown that human rationality is bounded by limitations on computational ability. Frequently the issue has been neglected assuming that travellers can learn from experience but recent literature shows that this cannot be taken for granted. In particular, humans can evaluate only a reduced number of alternatives, so hypothesizing that travellers are able to think in terms of hyperpaths whatever their extension may be can give rise to unrealistic assignments. This paper presents a behavioural model in which “myopic” travellers can determine hyperpaths only if their extension is under a certain threshold. At each node they reroute choosing stochastically a local intermediate node according to the cost of the hyperpath to this local destination and an a priori estimate of the cost of the remaining trip to the final destination. Implementation on a test network demonstrates that the model can generate results that differ considerably from those derived from the perfect rationality assumption, and so further work is needed to improve its realism and to test it against real world problems.

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