Drivers’ perception of the traffic environment, particularly their depth perception of surrounding elements, has a significant influence on their driving speed. While street view images (SVIs) capture environmental features from a driver’s perspective (such as trees, buildings, and pedestrians), the perceived distance of these elements from the driver (their depth value) plays a critical role in speed regulation that has been understudied. The same features at different distances can create varying levels of psychological pressure on drivers, with closer objects potentially inducing greater caution and lower speeds. To address this research gap, we used a monocular depth estimation model for simulating drivers’ perceptions and explored their impact on driving behavior. Specifically, we characterized the traffic conditions across 5458 road segments in Wuhan based on 5 million taxi GPS records. Next, we calculated the depth value from 14,115 panoramic street view images. Using the interpretable machine learning method, we map the spatial distribution of perceived environmental features associated with both low and high driving speeds. Our approach could explain 38.1% of driving speed variations. The results reveal a threshold effect between perceived environmental depth and driving speed, clarifying the non-linear relationship between drivers’ subjective perception of spatial distances and their speed choices.

Excessive urbanization leads to considerable nature deficiency and abundant artificial infrastructure in urban areas, which triggered intensive discussions on people's exposure to green space and outdoor artificial light at night (ALAN). Recent academic progress highlights that people's exposure to green space and outdoor ALAN may be confounders of each other but lacks systematic investigations. This study investigates the associations between people's exposure to green space and outdoor ALAN by adopting the three most used research paradigms: population-level residence-based, individual-level residence-based, and individual-level mobility-oriented paradigms. We employed the green space and outdoor ALAN data of 291 Tertiary Planning Units in Hong Kong for population-level analysis. We also used data from 940 participants in six representative communities for individual-level analyses. Hong Kong green space and outdoor ALAN were derived from high-resolution remote sensing data. The total exposures were derived using the spatiotemporally weighted approaches. Our results confirm that the negative associations between people's exposure to green space and outdoor ALAN are universal across different research paradigms, spatially non-stationary, and consistent among different socio-demographic groups. We also observed that mobility-oriented measures may lead to stronger negative associations than residence-based measures by mitigating the contextual errors of residence-based measures. Our results highlight the potential confounding associations between people's exposure to green space and outdoor ALAN, and we strongly recommend relevant studies to consider both of them in modeling people's health outcomes, especially for those health outcomes impacted by the co-exposure to them.