Anomaly Detection in Traffic Systems

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 83 - 114

Опубликована: Фев. 21, 2025

There is an effective need to manage the existing traffic systems due rapid increase in production and usage of vehicles. Traffic congestion, crashes delays are some challenges being faced today. Neural networks have emerged as a powerful solution tackle dynamic complex nature systems. This chapter, “Anomaly Detection Systems,” discusses application neural identifying anomalies by highlighting its importance enhancing safety, efficiency, overall management. As urban areas continue grow prevalence such accidents, unexpected patterns poses significant for transportation authorities. The chapter emphasizes role machine learning (ML) deep (DL) techniques highly focuses on networks, within data. also explores how deal with Management System (TMS) make it intelligent.

Язык: Английский

Detection of Vehicle Crashes on Roads using Deep Learning

Опубликована: Май 2, 2024

As the modern life is highly dependent on transport system, road safety became a high priority. Most of systems are aiming for very less reporting time to reduce severity accidents. This paper presents an in-depth analysis crash detection roads using deep learning(DL). We have use one mechanisms deepening i.e. Convolutional Neural Networks (CNNs) detect same. These DL models used classify traffic accidents and it automatically recognize with minimum effort. integrated automatic SMS alert which send after detecting crash. feature enhances acceleration emergency response, makes system more robust. achieved accuracy 96.3% relatively good compared existing baseline methods.

Язык: Английский

Road Accident Severity Detection In Smart Cities

International Journal of Scientific Research in Computer Science Engineering and Information Technology, Год журнала: 2024, Номер 10(2), С. 180 - 187

Опубликована: Март 16, 2024

Ensuring safety, in cities is a focus the development of urban areas requiring new and creative methods for categorizing managing accidents. Traditional approaches often face challenges evaluating accident seriousness within changing city environments. This research utilizes Long Short Term Memory (LSTM) Convolutional Neural Network (CNN) techniques to create system that categorizes accidents into three severity levels; minor, moderate severe. By leveraging learning capabilities, our method boosts precision efficiency safety protocols cities. The outcomes exhibit promising results offering tool enhancing infrastructure. Through empowering handle accidents, model establishes foundation initiatives. In essence, this study contributes standards promoting resilience sustainability, settings.

Язык: Английский