Sensitivity to travel time variability: Travelers' learning perspective

Avineri, E. and Prashker, J. N. (2005) Sensitivity to travel time variability: Travelers' learning perspective. Transportation Research Part C Emerging Technologies, 13 (2). pp. 157-183. ISSN 0968-090X

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Publisher's URL: http://dx.doi.org/10.1016/j.trc.2005.04.006

Abstract

This paper discusses the effect of the feedback mechanism on route-choice decision-making under uncertainty. Recent ITS (intelligent transportation systems) applications have highlighted the need for better models of the behavioral processes involved in travel decisions. However, travel behavior, and specifically route-choice decision-making, is usually modeled using normative models instead of descriptive models. Common route-choice models are based on the assumption of utility maximization. In this work, route-choice laboratory experiments and computer simulations were conducted in order to analyze route-choice behavior in iterative tasks with immediate feedback. The experimental results were compared to the predictions of two static models (random utility maximization and cumulative prospect theory) and two dynamic models (stochastic fictitious play and reinforcement learning). Based on the experimental results, it is showed that the higher the variance in travel times, the lower is the travelers’ sensitivity to travel time differences. These results are in conflict with the paradigm about travel time variability and risk-taking behavior. The empirical results may be explained by the payoff variability effect: high payoff variability seems to move choice behavior toward random choice.

Item Type:Article
Additional Information:An early version of the paper was presented at the 13th EWGT Conference (Bari, 2002). Drew on PhD research by the lead author. Internationally cited (US, China, the Netherlands, Italy, Japan, Switzerland, France, Israel and the UK). Featured on the journal's "Top 25 Hot Articles" from 4/2005 to 3/2007.
Uncontrolled Keywords:route-choice, uncertainty modeling, prospect theory, reinforcement learning
Faculty/Department:Faculty of Environment and Technology
ID Code:6058
Deposited By: R. Upload account
Deposited On:22 Jan 2010 15:11
Last Modified:11 Mar 2014 09:37

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