AIMC Topic: Built Environment

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Does Deep Learning Enhance the Estimation for Spatially Explicit Built Environment Stocks through Nighttime Light Data Set? Evidence from Japanese Metropolitans.

Environmental science & technology Feb 21, 2023
Built environment stocks have attracted much attention in recent decades because of their role in material and energy flows and environmental impacts. Spatially refined estimation of built environment stocks benefits city management, for example, in ...
Deep Learning Industry Built Environment Cities Japan
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A Design Framework of Exploration, Segmentation, Navigation, and Instruction (ESNI) for the Lifecycle of Intelligent Mobile Agents as a Method for Mapping an Unknown Built Environment.

Sensors (Basel, Switzerland) Sep 1, 2022
Recent work on intelligent agents is a popular topic among the artificial intelligence community and robotic system design. The complexity of designing a framework as a guide for intelligent agents in an unknown built environment suggests a pressing ...
Built Environment Algorithms Learning Artificial Intelligence
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Measuring Perceived Psychological Stress in Urban Built Environments Using Google Street View and Deep Learning.

Frontiers in public health May 11, 2022
An urban built environment is an important part of the daily lives of urban residents. Correspondingly, a poor design can lead to psychological stress, which can be harmful to their psychological and physical well-being. The relationship between the ...
Deep Learning Stress, Psychological Republic of Korea Built Environment Search Engine Humans
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Application of an adapted FMEA framework for robot-inclusivity of built environments.

Scientific reports Mar 1, 2022
Mobile robots are deployed in the built environment at increasing rates. However, lack of considerations for a robot-inclusive planning has led to physical spaces that would potentially pose hazards to robots, and contribute to an overall productivit...
Humans Robotics Built Environment
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Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning.

Computational intelligence and neuroscience Feb 7, 2022
This paper integrates classical design theory, multisource urban data, and deep learning to explore an accurate analytical framework in a new data environment, providing a scientific analysis path for the "where" and "how" of greenways in a high-dens...
Deep Learning Residence Characteristics Walking Built Environment Environment Design
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Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network.

Accident; analysis and prevention Oct 17, 2020
Pedestrian protection is an important component of road safety. Intersections are dangerous locations for pedestrians with mixed traffic. This paper aims to predict potential traffic conflicts between pedestrians and vehicles at signalized intersecti...
Safety Pattern Recognition, Automated Built Environment Pedestrians Accidents, Traffic Neural Networks, Computer Humans
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The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms.

Accident; analysis and prevention Jul 2, 2020
In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to...
Injury Severity Score Built Environment Bicycling Accidents, Traffic Humans Sex Factors Fuzzy Logic
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Automated traffic incident detection with a smaller dataset based on generative adversarial networks.

Accident; analysis and prevention Jun 20, 2020
An imbalanced and small training sample can cause an incident detection model to have a low detection rate and a high false alarm rate. To solve the scarcity of incident samples, a novel incident detection framework is proposed based on generative ad...
Support Vector Machine Sample Size Accidents, Traffic Built Environment Humans Research Design
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The importance of flow composition in real-time crash prediction.

Accident; analysis and prevention Jan 31, 2020
Previous real-time crash prediction models have scarcely used data disaggregated by vehicle type such as light, heavy and motorcycles. Thus, little effort has been made to quantify the impact of flow composition variables as crash precursors. We anal...
Motorcycles Humans Logistic Models Accidents, Traffic Support Vector Machine Built Environment Chile
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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...
Chicago Accidents, Traffic Humans Machine Learning Weather Spatial Analysis Models, Statistical Built Environment
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