01661413

Component

Decision field theory (DFT), popular in mathematical psychology, has recently been used in choice modelling for consumer choices and route choices. One of the key differences that DFT has from typical choice models is that it has preference values for each alternative that update over time. This results in a different probability of picking each alternative depending on how long a decision-maker considers their alternatives. Despite this psychologically feasible assumption, computational complexities of calculating the probability of alternatives in a DFT model have resulted in the number of deliberation timesteps often being set to a high value, thus ignoring and failing to utilise the dynamic nature of DFT. However, recent advances in the underlying computational methods for DFT have allowed for the calculation of alternatives at any time point. Thus, the authors build on this work by allowing the estimate for the number of deliberation timesteps to vary as a function of the response time of a decision-maker. They demonstrate that DFT model fit can be improved by considering response times using both route choice and conservation programme choice datasets. Consequentially, the dynamic nature of decision field theory could allow it to become an important model for revealed preference datasets such as gap acceptance in driving behaviour.

This paper was sponsored by TRB committee ADB10 Standing Committee on Traveler Behavior and Values.

18-05869

English

14p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Choice models; Decision theory; Driving; Dynamic models; Mathematical models; Reaction time; Route choice; Stated preferences

Highways; Planning and Forecasting

Transportation Research Board Annual Meeting 2018 Paper #18-05869

TRIS, TRB, ATRI

Jan 8 2018 11:31AM