Using Virtual Reality Environments to Predict Pedestrian Behaviour.

Pedestrian behaviour modelling and simulation play a fundamental role in reducing traffic risks and new policies implementation costs. However, representing human behaviour in this dynamic environment is not a trivial task and such models require an accurate representation of pedestrian behaviour. Virtual environments have been gaining notoriety as a behaviour elicitation tool, but it is still necessary to understand the validity of this technique in the context of pedestrian studies, as well as to create guidelines for its use. This work proposes a proper methodology for pedestrian behaviour elicitation using virtual reality environments in conjunction with surveys or questionnaires. The methodology focuses on gathering data about the subject, the context, and the action taken, as well as on analyzing the collected data to finally output a behavioural model. The resulting model can be used as a feedback signal to improve environment conditions for experiment iterations. A concrete implementation was built based on this methodology, serving as an example for future studies. A virtual reality traffic environment and two surveys were used as data sources for pedestrian crossing experiments. The subjects controlled a virtual avatar using an HTC Vive and were asked to traverse the distance between two points in a city. The data collected during the experiment was analyzed and used as input to a machine learning model capable of predicting pedestrian speed, taking into account their actions and perceptions. The proposed methodology allowed for the successful data gathering and its use to predict pedestrian behaviour with fairly acceptable accuracy.