  To ensure pedestrian friendly streets in the era of automated vehicles,
reassessment of current policies, practices, design, rules and regulations of
urban areas is of importance. This study investigates pedestrian crossing
behaviour, as an important element of urban dynamics that is expected to be
affected by the presence of automated vehicles. For this purpose, an
interpretable machine learning framework is proposed to explore factors
affecting pedestrians' wait time before crossing mid-block crosswalks in the
presence of automated vehicles. To collect rich behavioural data, we developed
a dynamic and immersive virtual reality experiment, with 180 participants from
a heterogeneous population in 4 different locations in the Greater Toronto Area
(GTA). Pedestrian wait time behaviour is then analyzed using a data-driven Cox
Proportional Hazards (CPH) model, in which the linear combination of the
covariates is replaced by a flexible non-linear deep neural network. The
proposed model achieved a 5% improvement in goodness of fit, but more
importantly, enabled us to incorporate a richer set of covariates. A game
theoretic based interpretability method is used to understand the contribution
of different covariates to the time pedestrians wait before crossing. Results
show that the presence of automated vehicles on roads, wider lane widths, high
density on roads, limited sight distance, and lack of walking habits are the
main contributing factors to longer wait times. Our study suggested that, to
move towards pedestrian-friendly urban areas, national level educational
programs for children, enhanced safety measures for seniors, promotion of
active modes of transportation, and revised traffic rules and regulations
should be considered.
