Recent research efforts into pedestrian-CAV interactions have focused on large-scale, real-world data, eschewing the experimental approaches that are required for controlled testing and investigations. This study introduces a dataset investigating pedestrian-CAV interactions, generated by extensive, networked virtual reality experiments. It then goes on to develop a model that investigates behaviour in road-crossing scenarios and utilises this dataset to train and test the model. We investigate the performance of the model relative to other, state-of-the-art approaches, as well as carrying out an ablative study to investigate the relative importance of the various features obtained within the VR environment.