Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity

Mathematics, Год журнала: 2025, Номер 13(2), С. 310 - 310

Опубликована: Янв. 18, 2025

Road traffic accident severity prediction is crucial for implementing effective safety measures and proactive management strategies. Existing methods often treat this as a nominal classification problem use traditional feature selection techniques. However, ordinal that account the ordered nature of (e.g., slight < serious fatal injuries) in still need to be investigated thoroughly. In study, we propose novel approach, Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS), which utilizes inherent ordering class labels both stages classification. The proposed approach enhances model performance by separately determining importance based on levels. experiments demonstrated effectiveness ORT-ROFS an accuracy 87.19%. According results, method improved 10.81% over state-of-the-art studies average different train–test split ratios. addition, it achieved improvement 4.58% methods. These findings suggest promising accurate prediction, supporting road planning intervention

Язык: Английский

An Autonomous Intelligent Liability Determination Method for Minor Accidents Based on Collision Detection and Large Language Models

Applied Sciences, Год журнала: 2024, Номер 14(17), С. 7716 - 7716

Опубликована: Сен. 1, 2024

With the rapid increase in number of vehicles on road, minor traffic accidents have become more frequent, contributing significantly to congestion and disruptions. Traditional methods for determining responsibility such often require human intervention, leading delays inefficiencies. This study proposed a fully intelligent method liability determination accidents, utilizing collision detection large language models. The approach integrated advanced vehicle recognition using YOLOv8 algorithm coupled with minimum mean square error filter real-time target tracking. Additionally, an improved global optical flow estimation support vector machines were employed accurately detect accidents. Key frames from accident scenes extracted analyzed GPT4-Vision-Preview model determine liability. Simulation experiments demonstrated that efficiently detected collisions, rapidly determined liability, generated detailed reports. achieved automated AI processing without manual ensuring both objectivity fairness.

Язык: Английский

Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm

Biomimetics, Год журнала: 2024, Номер 9(11), С. 711 - 711

Опубликована: Ноя. 19, 2024

Despite the implementation of numerous interventions to enhance urban traffic safety, estimation risk crashes resulting in life-threatening and economic costs remains a significant challenge. In light above, an online inference method for crash based on self-developed TAR-DETR WOA-SA-SVM methods is proposed. The method's robust data capabilities can be applied autonomous mobile robots vehicle systems, enabling real-time road condition prediction, continuous monitoring, timely roadside assistance. First, dataset object detection, named TAR-1, created by extracting information from major roads around Hainan University China incorporating Russian car news. Secondly, we develop innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). model demonstrates detection accuracy 76.8% objects, which exceeds performance other state-of-the-art models. employed TAR-1 extract features, feature was designated as TAR-2. TAR-2 comprises six features three categories. A new algorithm proposed optimize parameters (C, g) SVM, thereby enhancing robustness inference. developed combining Whale Optimization Algorithm (WOA) Simulated Annealing (SA), Hybrid Bionic Intelligent Algorithm. inputted into Support Vector Machine (SVM) optimized using hybrid used infer crashes. achieves average 80%

Язык: Английский