Prediction of Highway Multi‑Accident Sections Based on Machine Learning

Traffic Accident Severity Prediction Based on Multi-Model Comparison

Results show that the tree-based ensemble models Random Forest and XGBoost outperform Logistic Regression and MLP in terms of overall predictive performance, with XGBoost exhibiting better overall performance.

A F-R Algorithm-based Machine Learning Model for Traffic Accident Severity Prediction

A novel approach to optimizing intelligent transportation models using the hybrid Foraging Habitat Selection Particle Swarm Optimization–Random Forest (FHSPSO-RF) technique, which can adaptively optimize several parameters of the Random Forest classifier, namely the number of trees, maximum depth, and minimal samples p...

Statistical and predictive analysis based on the factors affecting the severity of coal mine disasters.

This study analyzes China's national coal mine accident data (2000-2025) to evaluate safety performance. Using one-way analysis of variance and random forest regression model, the study quantitatively analyzes temporal trends, spatial clustering, accident causes and types. A time-series model was developed to predict t...

prediction models considering pavements quality

Predicting 85th Percentile Speeds for Safer Roads: A Case Study Using Regression-Based Estimation

Operating speed is one of the key parameters in road design, classification, and safety assessment. This study examines changes in 85th percentile (operating) speeds (V85) across the analyzed road network in the Middle East, between 2016 and 2020, using a combined dataset of measured and predicted values. The analysis...

Urban traffic volume forecasting using machine learning and neural networks

To build sustainable cities, it is critical to be able to predict traffic volume and to manage and control it effectively, especially in large cities. This study aims to determine whether the number of vehicles on the road in Istanbul, the world's most congested city, can be predicted using meteorological and traffic-r...