AIMC Topic: Accidents, Traffic

Clear Filters Showing 181 to 190 of 290 articles

Predicting and Interpreting Spatial Accidents through MDLSTM.

International journal of environmental research and public health Feb 3, 2021
Predicting and interpreting the spatial location and causes of traffic accidents is one of the current hot topics in traffic safety. This research purposed a multi-dimensional long-short term memory neural network model (MDLSTM) to fit the non-linear...
Neural Networks, Computer China Accidents, Traffic
View on PubMed DOI

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.

PloS one Feb 3, 2021
With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not a...
Humans City Planning Motorcycles Accidents, Traffic Machine Learning Kenya Social Media
View on PubMed DOI

Factors that influence parents' intentions of using autonomous vehicles to transport children to and from school.

Accident; analysis and prevention Jan 25, 2021
High-level autonomous vehicles (AVs) are likely to improve the quality of children's travel to and from school (such as improve travel safety and increase travel mobility). These expected benefits will not be presented if parents are not willing to u...
Intention Attitude Parents Decision Making Schools Accidents, Traffic Robotics Middle Aged Child Adult Male Female Young Adult Humans
View on PubMed DOI

Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial.

Accident; analysis and prevention Jan 23, 2021
Traffic congestion is monotonically increasing, especially in large cities, due to rapid urbanization. Traffic congestion not only deteriorates traffic operation and degrades traffic safety, but also imposes costs to the road users. The concerns asso...
Humans Environment Design Accidents, Traffic Safety Automobile Driving Automobiles Robotics City Planning
View on PubMed DOI

Applying machine learning, text mining, and spatial analysis techniques to develop a highway-railroad grade crossing consolidation model.

Accident; analysis and prevention Jan 22, 2021
The consolidation of Highway-Railroad Grade Crossing (HRGC) is one of the effective approaches to decrease the number of crashes between trains and vehicles. From 2015-2019, there were 57 HRGC crashes at crossings in East Baton Rouge Parish (EBRP), r...
Models, Theoretical Accidents, Traffic Spatial Analysis Railroads Data Mining Humans Machine Learning Male
View on PubMed DOI

Traffic accident detection and condition analysis based on social networking data.

Accident; analysis and prevention Jan 15, 2021
Accurate detection of traffic accidents as well as condition analysis are essential to effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be obtained using an advanced data classification model with a rich ...
Machine Learning Algorithms Humans Neural Networks, Computer Accidents, Traffic Social Networking
View on PubMed DOI

Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods.

Accident; analysis and prevention Dec 29, 2020
Transportation safety is highly correlated with driving behavior, especially human error playing a key role in a large portion of crashes. Modern instrumentation and computational resources allow for the monitorization of driver, vehicle, and roadway...
Deep Learning Accidents, Traffic Humans Automobile Driving Distracted Driving
View on PubMed DOI

Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data.

International journal of environmental research and public health Dec 18, 2020
BACKGROUND: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the well...
Automobile Driving Humans Text Messaging Deep Learning Accidents, Traffic
View on PubMed DOI

Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data.

Accident; analysis and prevention Dec 8, 2020
Traffic violations and improper driving are behaviors that primarily contribute to traffic crashes. This study aimed to develop effective approaches for predicting at-fault crash driver frequency using only city-level traffic enforcement predictors. ...
Machine Learning Deep Learning Automobile Driving Accidents, Traffic Humans Neural Networks, Computer
View on PubMed DOI

Crash narrative classification: Identifying agricultural crashes using machine learning with curated keywords.

Traffic injury prevention Nov 18, 2020
OBJECTIVE: Traditionally, structured or coded data fields from a crash report are the basis for identifying crashes involving different types of vehicles, such as farm equipment. However, using only the structured data can lead to misclassification o...
Louisiana Agriculture Support Vector Machine Algorithms Bayes Theorem Accidents, Traffic Humans Forecasting Texas Machine Learning
View on PubMed DOI
Popular Topics
Recent Journals