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

Modelling the role of experience in the formation of preferences is an increasingly popular topic in numerous research areas. Thus far, however, there have been limited applications using physiological sensor data to unpick the role of experience in preferential change under experimental conditions, a particularly challenging task in real-world settings where the effect of an experience can be confounded with changes in other external factors. This motivates the work in the present study, where 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. It also allows us to collect data on hypothetical futuristic scenarios where preferences may be more subject to change. Specifically, we ask participants to complete stated preference (SP) tasks on travel mode choice. After each SP choice, the participant `experiences' their chosen mode in Virtual Reality (VR), where the level-of-service attributes (e.g. travel time, waiting time, level of comfort, etc.) are mapped with those presented in the SP. They are then asked to reevaluate their choices. Electroencephalogram recordings, eye-tracking data, and skin conductance data are recorded to (a) better understand the initial decision-making process, (b) better evaluate the participant's experience within the virtual reality settings and (c) better predict preferential change. We develop different versions of decision field theory to evaluate how to best capture the impact of experiencing the chosen travel mode. This also allows us to test models for their capabilities in predicting preference reversal.