AIMC Topic: Built Environment
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Social media interaction and built environment effects on urban walking experience: A machine learning analysis of Shanghai Citywalk.
PloS one
Apr 29, 2025
In fast-paced urban environments, Citywalk has emerged as a key leisure activity for urban residents to alleviate stress and enhance emotional well-being. From the perspective of virtual-physical interaction, this study integrates social media data w...
Spatial heterogeneity effect of built environment on traffic safety using geographically weighted atrous convolutions neural network.
Accident; analysis and prevention
Feb 8, 2025
The built environment exerts a significant influence on the frequency and severity of traffic accidents. Spatially uniform assumptions on the impacts of built environment factors commonly employed in existing research may lead to inconsistent and con...
What factors influence the willingness and intensity of regular mobile physical activity?- A machine learning analysis based on a sample of 290 cities in China.
Frontiers in public health
Jan 23, 2025
INTRODUCTION: This study, based on Volunteered Geographic Information (VGI) and multi-source data, aims to construct an interpretable macro-scale analytical framework to explore the factors influencing urban physical activities. Using 290 prefecture-...
Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision.
Accident; analysis and prevention
Nov 23, 2024
Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and ...
Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework.
Accident; analysis and prevention
Aug 13, 2024
The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into t...
Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning.
Accident; analysis and prevention
Jul 1, 2024
Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, an...
A spatiotemporal deep learning approach for pedestrian crash risk prediction based on POI trip characteristics and pedestrian exposure intensity.
Accident; analysis and prevention
Feb 8, 2024
Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian ...
Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning.
Accident; analysis and prevention
Jan 12, 2024
Road safety is a critical concern that impacts both human lives and urban development, drawing significant attention from city managers and researchers. The perception of road safety has gained increasing research interest due to its close connection...
Integrating street view images and deep learning to explore the association between human perceptions of the built environment and cardiovascular disease in older adults.
Social science & medicine (1982)
Oct 11, 2023
Understanding how built environment attributes affect health remains important. While many studies have explored the objective characteristics of built environments that affect health outcomes, few have examined the role of human perceptions of built...
JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments.
IEEE transactions on pattern analysis and machine intelligence
May 8, 2023
We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360 RGB video at 15 fps, 3D point clouds from two 1...
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