This systematic literature review aims to investigate the current state-of-the-art in machine
learning (ML) based road traffic analysis, hindrance estimation, and predicting vehicle safety …

Traffic accidents have a substantial impact on human life and property, resulting in millions
of injuries every year. To ensure road safety and enhance the research in this direction, it is …

Highlights What are the main findings? Demonstrates the effectiveness of a novel multitask
learning (MTL) framework utilizing large language models (LLMs) for real-time analysis of …

Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities
annually, with the greatest impact on individuals aged 5 to 29 years old. This paper …

On a global scale, traffic incidents are a leading cause of mortality and material damage.
Romania exhibits the highest rate of road traffic fatalities both in the European Union and …

The opportunity for large amounts of open-for-public and available data is one of the main
drivers of the development of an information society at the beginning of the 21st century. In …

Early detection of accidents and rescue are of paramount importance in the reduction of
fatalities. Social media data, which has evolved to become an important source of sharing …

In contemporary society, the rapid progression of urbanization and technological
advancements has led to a substantial increase in the number of vehicles, consequently …

Identifying road segments with a high crash incidence is essential for improving road safety.
Conventional methods for detecting these segments rely on historical data from various …

This paper presents a novel geohash-based approach for predicting traffic incident locations
using machine learning algorithms. The study utilized a three-stage model for predicting the …