AIMC Topic: Built Environment

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Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis.

Accident; analysis and prevention Dec 20, 2019
Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)-a Machine Learning (ML) technique-to detect the occurrence of accidents using a set of real time data compri...
Humans Machine Learning Models, Statistical Built Environment Chicago Accidents, Traffic Weather Spatial Analysis
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Crash prediction based on traffic platoon characteristics using floating car trajectory data and the machine learning approach.

Accident; analysis and prevention Oct 4, 2019
Predicting crash propensity helps study safety on urban expressways in order to implement countermeasures and make improvements. It also helps identify and prevent crashes before they happen. However, collecting real-time wide-coverage traffic inform...
Accidents, Traffic Built Environment Risk Assessment China Logistic Models Support Vector Machine Automobiles Humans
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Expressway crash risk prediction using back propagation neural network: A brief investigation on safety resilience.

Accident; analysis and prevention Jan 17, 2019
This study presents the work in predicting crash risk on expressways with consideration of both the impact of safety critical events and traffic conditions. The traffic resilience theory is introduced to learn safety problems from the standpoint of 1...
Humans Neural Networks, Computer Computer Simulation Automobile Driving Built Environment Environment Design Accidents, Traffic Risk Assessment Safety China
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Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements.

Accident; analysis and prevention Jan 8, 2019
Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent adva...
Safety Management Built Environment Accidents, Traffic Humans Data Mining Artificial Intelligence Risk Assessment Logistic Models
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A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data.

Accident; analysis and prevention Nov 1, 2018
The primary objective of this study is to investigate how the deep learning approach contributes to citywide short-term crash risk prediction by leveraging multi-source datasets. This study uses data collected from Manhattan in New York City to illus...
Humans Risk Assessment Spatial Analysis Built Environment New York City Automobiles Deep Learning Accidents, Traffic Logistic Models Weather
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Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity.

JAMA network open Aug 3, 2018
IMPORTANCE: More than one-third of the adult population in the United States is obese. Obesity has been linked to factors such as genetics, diet, physical activity, and the environment. However, evidence indicating associations between the built envi...
Deep Learning United States Urban Health Cross-Sectional Studies Built Environment Humans Prevalence Adult Residence Characteristics Obesity Models, Statistical
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Subject Matter Knowledge in the Age of Big Data and Machine Learning.

JAMA network open Aug 3, 2018
Big Data Obesity Built Environment Humans Adult Machine Learning Prevalence Deep Learning
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Human-perceived vs actual built environment: Using human-centred GeoAI and street view images to support urban planning in Australia.

Journal of environmental management Aug 1, 2025
In alignment with the United Nations Sustainable Development Goals, the pursuit of safe and sustainable cities that promote well-being across all age groups has become a core objective in urban planning and environmental management. The built environ...
Humans Built Environment Perception City Planning Pilot Projects Cities Australia
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Context-dependent effects of built environment factors on pedestrian-injury severities with imbalanced and high dimensional crash data.

Accident; analysis and prevention Aug 1, 2025
Built environment is an important component that influences pedestrian injury severities in pedestrian-vehicle crashes. Previous studies indicated that the effects of various built environment factors on pedestrian injury severities are heterogeneous...
Built Environment Wounds and Injuries Accidents, Traffic North Carolina Humans Machine Learning Environment Design Pedestrians
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Revealing the impacts of the built environment factors on pedestrian-weighted air pollutant concentration using automated and interpretable machine learning.

Journal of environmental management Jul 1, 2025
Urban air pollution poses significant health risks, especially to pedestrians due to their proximity to pollutants and lack of physical protection. Understanding the influence of built environment factors is essential to mitigate this pollution and s...
Humans Pedestrians Built Environment Air Pollutants Air Pollution Machine Learning Particulate Matter Environmental Monitoring
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