AIMC Topic: Accidents, Traffic

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Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking.

Sensors (Basel, Switzerland)
The size of cities has been continuously increasing because of urbanization. The number of public and private transportation vehicles is rapidly increasing, thus resulting in traffic congestion, traffic accidents, and environmental pollution. Althoug...

Understanding the potential of emerging digital technologies for improving road safety.

Accident; analysis and prevention
Each year, 1.35 million people are killed on the world's roads and another 20-50 million are seriously injured. Morbidity or serious injury from road traffic collisions is estimated to increase to 265 million people between 2015 and 2030. Current roa...

A literature review of machine learning algorithms for crash injury severity prediction.

Journal of safety research
INTRODUCTION: Road traffic crashes represent a major public health concern, so it is of significant importance to understand the factors associated with the increase of injury severity of its interveners when involved in a road crash. Determining suc...

Transfer learning for spatio-temporal transferability of real-time crash prediction models.

Accident; analysis and prevention
Real-time crash prediction is a heavily studied area given their potential applications in proactive traffic safety management in which a plethora of statistical and machine learning (ML) models have been developed to predict traffic crashes in real-...

Variance-based global sensitivity analysis for rear-end crash investigation using deep learning.

Accident; analysis and prevention
Traffic accidents are rare events with inconsistent spatial and temporal dimensions; thus, accident injury severity (INJ-S) analysis faces a significant challenge in its classification and data stability. While classical statistical models have limit...

Finding and understanding pedal misapplication crashes using a deep learning natural language model.

Traffic injury prevention
OBJECTIVE: The objective of this study was to develop a system which used the BERT natural language understanding model to identify pedal misapplication (PM) crashes from their crash narratives and validate the accuracy of the system.

A high-resolution trajectory data driven method for real-time evaluation of traffic safety.

Accident; analysis and prevention
Real-time safety evaluation is essential for developing proactive safety management strategy and improving the overall traffic safety. This paper proposes a method for real-time evaluation of road safety, in which traffic states and conflicts are com...

Vision-Based Driver's Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning.

Sensors (Basel, Switzerland)
Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drive...

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction.

Sensors (Basel, Switzerland)
Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the str...

A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers.

Sensors (Basel, Switzerland)
With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to b...