AIMC Topic: Accidents, Traffic

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Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning.

Accident; analysis and prevention Jan 12, 2024
Road safety is a critical concern that impacts both human lives and urban development, drawing significant attention from city managers and researchers. The perception of road safety has gained increasing research interest due to its close connection...
Humans Deep Learning Accidents, Traffic Safety Built Environment Cities Environment Design
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Advancing proactive crash prediction: A discretized duration approach for predicting crashes and severity.

Accident; analysis and prevention Dec 5, 2023
Driven by advancements in data-driven methods, recent developments in proactive crash prediction models have primarily focused on implementing machine learning and artificial intelligence. However, from a causal perspective, statistical models are pr...
Humans Accidents, Traffic Logistic Models Rural Population Models, Statistical Artificial Intelligence Sample Size
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A spatio-temporal deep learning approach to simulating conflict risk propagation on freeways with trajectory data.

Accident; analysis and prevention Nov 18, 2023
On freeways, sudden deceleration or lane-changing by vehicles can trigger conflict risk that propagates backward in a specific pattern. Simulating this pattern of conflict risk propagation can not only help prevent crashes but is also vital for the d...
Humans Computer Simulation Safety Automobile Driving Deep Learning Accidents, Traffic
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Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review.

Accident; analysis and prevention Nov 15, 2023
Accurately modelling crashes, and predicting crash occurrence and associated severities are a prerequisite for devising countermeasures and developing effective road safety management strategies. To this end, crash prediction modelling using machine ...
Humans Safety Management Forecasting Accidents, Traffic Machine Learning Algorithms
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Improving model robustness of traffic crash risk evaluation via adversarial mix-up under traffic flow fundamental diagram.

Accident; analysis and prevention Oct 26, 2023
Recent state-of-art crash risk evaluation studies have exploited deep learning (DL) techniques to improve performance in identifying high-risk traffic operation statuses. However, it is doubtful if such DL-based models would remain robust to real-wor...
Accidents, Traffic Neural Networks, Computer Logistic Models Humans
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Before-after safety evaluation of part-time protected right-turn signals: An extreme value theory approach by applying artificial intelligence-based video analytics.

Accident; analysis and prevention Oct 19, 2023
Extreme value theory models have opened doors for before-after safety evaluation of engineering treatments using traffic conflict techniques. Recent advancements in automated conflict extraction technologies have further expedited conflict-based safe...
Safety Humans Neural Networks, Computer Environment Design Artificial Intelligence Accidents, Traffic Cities
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On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry.

BMC medical informatics and decision making Oct 9, 2023
BACKGROUND: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient's condition and deciding on transport destination. Data-driven On Scene Injur...
Wounds and Injuries Accidents, Traffic Injury Severity Score Artificial Intelligence Retrospective Studies Young Adult Triage Humans Sweden
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A Fuzzy Clustering Approach to Identify Pedestrians' Traffic Behavior Patterns.

Journal of research in health sciences Sep 29, 2023
BACKGROUND: Pattern recognition of pedestrians' traffic behavior can enhance the management efficiency of interested groups by targeting access to them and facilitating planning via more specific surveys. This study aimed to evaluate the pedestrians'...
Safety Surveys and Questionnaires Cross-Sectional Studies Cluster Analysis Humans Male Walking Pedestrians Adult Accidents, Traffic
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Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach.

Sensors (Basel, Switzerland) Sep 29, 2023
A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A ...
Deep Learning Electroencephalography Humans Fatigue Accidents, Traffic Automobile Driving
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SWADAPT2: benefits of a collision avoidance assistance for powered wheelchair users in driving difficulty.

Disability and rehabilitation. Assistive technology Sep 8, 2023
PURPOSE: In France, tens of thousands of people use a wheelchair. Driving powered wheelchairs (PWCs) present risks for users and their families. The risk of collision in PWC driver increases with severity of disability and may reduce their independen...
Humans Accidents, Traffic Aged Persons with Disabilities Female Robotics Wheelchairs Male Automobile Driving Adult Equipment Design Middle Aged France
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