Virtual Immersive Reality for Stated Preference Travel Behaviour Experiments: A Case Study of Autonomous Vehicles on Urban Roads
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Abstract (arXiv)
Stated preference experiments have been known to suffer from the lack of realism. This issue is particularly visible when the scenario doesn't have a well understood prior reference e.g. in case of the autonomous vehicles related scenarios. We present Virtual Immersive Reality Environment (VIRE) that is capable of developing highly realistic, immersive, and interactive choice scenario. We demonstrate the use of VIRE in the pedestrian preferences related to autonomous vehicles and associated infrastructure changes on urban streets of Montr'eal. The results are compared with predominantly used approaches i.e. text-only and visual aid. We show that VIRE results in better understanding of the scenario and consistent results.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 2; 2025: 1.
6 papers.
- 2021Choice modelling in the age of machine learning -- discussion papercited by 26
- 2022Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choicescited by 5
- 2022Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviourcited by 1
- 2024Towards a large-scale fused and labeled dataset of human pose while interacting with robots in shared urban areascited by 0
- 2024A deep causal inference model for fully-interpretable travel behaviour analysiscited by 0
- 2025Vision-based Perception System for Automated Delivery Robot-Pedestrians Interactionscited by 0
- 2025Vision-based Perception System for Automated Delivery Robot-Pedestrians Interactions5 Aug 2025
- 2024A deep causal inference model for fully-interpretable travel behaviour analysis2 May 2024
- 2024Towards a large-scale fused and labeled dataset of human pose while interacting with robots in shared urban areas28 Jan 2024
- 2022Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour21 Dec 2022
- 2022Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices20 Apr 2022
- 2021Choice modelling in the age of machine learning -- discussion paper28 Jan 2021