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 which, as an important element of urban dynamics, 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 analysed 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, educational programs for children, enhanced safety measures for seniors, promotion of active modes of transportation, and revised traffic rules and regulations should be considered.

为了确保自动驾驶汽车时代的行人友好的街道，重新评估城市地区的现行政策，做法，设计，规则和法规至关重要。这项研究调查了行人过路行为，这是城市动态的重要组成部分，预计会受到自动驾驶汽车的影响。为此，提出了一种可解释的机器学习框架，以探讨影响行人在有自动驾驶车辆穿越中段人行横道之前等待时间的因素。为了收集丰富的行为数据，我们开发了一个动态的，身临其境的虚拟现实实验，来自大多伦多地区（GTA）4个不同位置的异类人群的180名参与者。然后使用数据驱动的Cox比例危害（CPH）模型分析行人的等待时间行为，其中协变量的线性组合被灵活的非线性深度神经网络代替。拟议的模型使拟合优度提高了5％，但更重要的是，它使我们能够纳入更丰富的协变量集。基于博弈论的可解释性方法用于了解不同协变量对行人过马路之前等待时间的贡献。结果表明，道路上自动驾驶汽车的存在，较宽的车道宽度，道路上的高密度，有限的视线距离和缺乏步行习惯是导致较长等待时间的主要因素。我们的研究表明，为了朝着行人友好的城市地区发展儿童教育计划，