Driving in urban environments is an essential part of urban residents’ daily life. We still know little about impacts of a wide range of greenness on driving performance in real urban environments, after controlling for socioeconomic, demographic, driving record, and other environmental factors. This missing knowledge prevents policymakers and professionals from using appropriate planning and design of green landscapes to create safe driving environments for numerous urban residents. This study aimed to address this significant knowledge gap by using real-world driving experiments. Each of thirty-four residents performed seven driving tasks so 238 driving tasks were completed in total. Each task lasted one hour and followed a randomly assigned sequence. Road greenness and other environmental characteristics were analyzed using deep transfer learning semantic segmentation based on live videos (30 frames per second), recorded by a camera positioned to capture the driver’s eye view. A serial communication technology, known as Controller Area Network bus (CANbus), was employed to continuously measure driving performance using four parameters. A series of hierarchical regression analyses yielded three major findings: First, an increased mean of greenness was associated with improved driving performance, as demonstrated by all four parameters. Second, an increased variation of greenness was also associated with better driving performance in three parameters. Finally, the mean of greenness displayed a stronger positive relationship with driving performance than the variation of greenness in three parameters. The findings imply that both the quantity and quality of green landscapes are critical for promoting driving performance in urban areas.

中文翻译：

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更环保的视野，更安全的驾驶：利用重复的现场实验和深度迁移学习技术来研究城市道路景观对驾驶性能的影响


在城市环境中驾驶是城市居民日常生活的重要组成部分。在控制了社会经济、人口、驾驶记录和其他环境因素后，我们对真实城市环境中各种绿化对驾驶性能的影响仍然知之甚少。这种知识的缺失阻碍了政策制定者和专业人士利用适当的绿色景观规划和设计来为众多城市居民创造安全的驾驶环境。本研究旨在通过现实世界的驾驶实验来解决这一重大知识差距。 34 名居民每人执行 7 次驾驶任务，总共完成 238 次驾驶任务。每项任务持续一小时，并遵循随机分配的顺序。使用基于实时视频（每秒 30 帧）的深度迁移学习语义分割来分析道路绿化和其他环境特征，这些视频由用于捕获驾驶员眼睛视图的摄像头记录。采用称为控制器局域网总线 (CANbus) 的串行通信技术，使用四个参数连续测量驾驶性能。一系列分层回归分析得出了三个主要发现：首先，绿色平均值的增加与驾驶性能的提高相关，正如所有四个参数所证明的那样。其次，绿色度变化的增加也与三个参数中更好的驾驶性能相关。最后，与三个参数的绿色度变化相比，绿色度平均值与驾驶性能表现出更强的正相关关系。研究结果表明，绿色景观的数量和质量对于提升城市地区的驾驶性能至关重要。