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Accidents, Traffic

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Traffic accident risk prediction based on deep learning and spatiotemporal features of vehicle trajectories.

PloS one
With the acceleration of urbanization and the increase in traffic volume, frequent traffic accidents have significantly impacted public safety and socio-economic conditions. Traditional methods for predicting traffic accidents often overlook spatiote...

Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.

International journal of injury control and safety promotion
Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fat...

Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.

Scientific reports
Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electroencephalogram (EEG)-based detection systems. Due to the high-dimensional nature of EEG signals and ...

Evaluating crash risk factors of farm equipment vehicles on county and non-county roads using interpretable tabular deep learning (TabNet).

Accident; analysis and prevention
Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with fast...

A game theoretical model to examine pedestrian behaviour and safety on unsignalised slip lanes using AI-based video analytics.

Accident; analysis and prevention
Left-turn slip lanes, also known as channelised right-turn lanes in right-hand driving countries, are widely implemented to facilitate left-turning at signalised intersections. However, pedestrian safety on slip lanes is not well known. At unsignalis...

Spatio-temporal crash severity analysis with cost-sensitive multi-graphs attention network.

Accident; analysis and prevention
Most conventional crash severity models attempt to achieve a low classification error rate, implicitly assuming the same losses for all classification errors. In this paper, we suggest that this setting has limitations in terms of reasonableness, as ...

Optimized driver fatigue detection method using multimodal neural networks.

Scientific reports
Driver fatigue is a significant factor contributing to road accidents, highlighting the need for precise and reliable detection systems. This study introduces a comprehensive approach using multimodal neural networks, leveraging the DROZY dataset, wh...

Dynamic cross-domain transfer learning for driver fatigue monitoring: multi-modal sensor fusion with adaptive real-time personalizations.

Scientific reports
Driver fatigue is one of the most common causes of road accidents, which means that there is a great need for robust and adaptive monitoring systems. Current models of fatigue detection suffer from domain-specific limitations in generalizing across d...

A dense multi-pooling convolutional network for driving fatigue detection.

Scientific reports
Driver fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles, who are more susceptible to fatigue due to prolonged driving hours and monotonous conditions during their journeys. Existing vision-based driv...

Waist rotation angle as indicator of probable human collision-avoidance direction for autonomous mobile robots.

PloS one
The likelihood of pedestrians encountering autonomous mobile robots (AMRs) in smart cities is steadily increasing. While previous studies have explored human-to-human collision avoidance, the behavior of humans avoiding AMRs in direct, head-on scenar...