Urban landscape plays a crucial role in reshaping the activity and mobility pattern of citizens. Studies have explored the relationships between the built environment, socio-economic, transport infrastructure, travel behaviour, and quality of life at different spatial scales. However, associations between the built environment, driver distraction, and driving performance at the micro-level are less studied. In this study, influences of different visual objects from drivers’ view and other possible factors on driver distraction and speed variation are investigated. Based on the street view imagery and image segmentation technique, proportions of visible objects including vegetation and road furniture within driver perspective can be estimated. Furthermore, vehicle kinematics in terms of longitudinal speed, longitudinal acceleration, and lateral acceleration can be measured from vehicle trajectory data. The Gaussian distributed copula model is used to jointly model the ratio of driver distraction and speed standard deviation. Results indicate that proportions of road, sky, and buildings in the drivers’ view significantly affect driver distraction ratio. In addition, speed standard deviation is associated with driver distraction ratio, proportions of sky and buildings, vehicle longitudinal and lateral acceleration, and driver age. Findings should shed light on enhancing urban design and planning by considering the effects of built environment attributes and drivers’ visual environment.

中文翻译：

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哪些街景图像功能有利于驾车？用于驾驶员分心和驾驶性能的 copula 模型

城市景观在重塑市民的活动和出行模式方面发挥着至关重要的作用。研究探讨了不同空间尺度的建筑环境、社会经济、交通基础设施、出行行为和生活质量之间的关系。然而，在微观层面上，建筑环境、驾驶员分心和驾驶性能之间的关联研究较少。在这项研究中，研究了驾驶员视角的不同视觉对象和其他可能因素对驾驶员分心和速度变化的影响。基于街景图像和图像分割技术，可以估计驾驶员视角内可见物体（包括植被和道路设施）的比例。此外，可以从车辆轨迹数据中测量纵向速度、纵向加速度和横向加速度方面的车辆运动学。高斯分布式 copula 模型用于联合模拟驾驶员分心和速度标准差的比率。结果表明，驾驶员视图中道路、天空和建筑物的比例会显著影响驾驶员分心率。此外，速度标准差与驾驶员分心率、天空和建筑物的比例、车辆纵向和横向加速度以及驾驶员年龄相关。研究结果应该通过考虑建筑环境属性和驾驶员视觉环境的影响来阐明加强城市设计和规划。