Modelling the demand for new modes like self-driving cars, flying taxis, hyperloops, etc. is crucial for long term transport planning. The reliability of these models depend on the quality of the data used for developing the models. This is a challenging task since modes like self-driving cars, flying taxis, hyperloops are very new concepts to the travellers at this point of time. The proposed PhD topic will involve designing novel Virtual Reality (VR) experiments to simulate future transport scenarios to get insights about travel behaviour in presence of future modes and use them for developing mathematical models of travel behaviour.
The traditional approach to quantify demand for future modes relies on collecting data using Stated Preference (SP) surveys where participants are presented with hypothetical scenarios of current and future modes and asked to state their choices. The attributes of the modes (e.g. travel time, cost, comfort levels) are typically presented using textual descriptions. However, SP surveys are often criticized for lack of realism in delivering the details of the hypothetical scenarios (Patterson et al. 2017). This can bias the results, especially in the context of future modes, where participants have not experienced similar situations and need detailed information before making the decisions. One solution to the problem of lack of realism in SP surveys is to use pictures or videos to elaborate the alternatives to the respondents. Although images help with the efficiency of delivering the details, they are often not neutral, e.g., tending to picture happy passengers in perfect environments which can result in bias in the choices. Moreover, the role of the respondent will remain passive as they will not interact with the hypothetical scenarios.
On a parallel stream of research Virtual Reality (VR) experiments have gained popularity in various research fields. With the development of interactive computer-generated experiences, realistic virtual scenes can be directly projected to the participants using a head-mounted VR display device, so that they will be immersed in the simulated environment. VR experiments have been primarily used in the context of psychology and human factors research where the participants have to make simple choices (e.g. cross a road or not when a self-driving car is approaching (Kalatian & Farooq 2021), rate perceived cycling risk in different scenarios (Bogacz et al. 2021, etc). The proposed PhD research will take this further by examining travel behaviour in more complex choice scenarios where multiple attributes are varying across multi-modal choice scenarios (e.g. access/egress time and facility, travel time, comfort levels etc.). The data will be used to develop detailed mathematical models of travel behaviour using advanced choice modelling and/or machine learning techniques. Along with the VR experiments, traditional SP surveys on similar scenarios will be collected to further enhance this research direction.
Within this broad theme, the PhD student will have the option to investigate detailed research questions of interest – ranging from investigating approaches to improve the representation of and the interaction with futuristic transport modes in VR to looking at learning effect, mood, etc. on travel choices. The developed models will be useful for predicting travel behaviour in the future and provide inputs to identifying the features of the built environment that can lead to the successful integration of the new modes with the current transport network. The project has a close relationship with the UKRI funded project ‘NEXt generation activity and travel behavioUr modelS’ (NEXUS).
Further details and link to the application portal available at: Modelling the preference for transport modes of the future using Virtuocity | Project Opportunities | PhD | University of Leeds
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