An adapted decision field theory model for capturing the impact of experiences on preferential change for new travel modes.

Abstract

There have been limited applications using physiological sensor data to unpick the role of experience in preferential change under experimental conditions. We design a novel virtual reality (VR)–based data collection process that allows us to collect physiological sensor data to measure the effect of experience in a controlled setting. Specifically, we ask participants to complete a number of stated preference tasks on travel mode choice based on traditional stated preference (SP) scenarios. After each SP choice, the participant ‘experiences’ their chosen mode in Virtual Reality (VR). The participant then ‘re-evaluates’ their choices. We develop and test different versions of a sequential sampling model (decision field theory) to evaluate how to best capture the impact of experiencing the chosen travel mode. We gain insights into the participants’ relative preferences towards new travel modes and how experiencing the new modes may influence their uptake when they become available.