Real-time crash risk analysis using deep learning

Abstract

Road traffic accidents are a major source of worry for traffic safety decision-makers and researchers all around the world. Traffic safety has become a serious concern for rural roads and metropolitan expressways throughout the world as traffic volume has increased rapidly and crashes have grown more common. The variety, rarity, and interconnectedness of historical data on elements that cause automobile accidents lead to the necessity for more targeted research for assessing, forecasting, and visualizing the risk of accidents in the short and long term for preventative reasons. To evaluate, forecast and display risk, a variety of methodologies and tools are used. The majority of research used linear time-series approaches to anticipate risk, with only a few using machine learning and deep learning techniques. A substantial quantity of traffic data has been acquired in recent years from a variety of sources, including road sensors, probes, GPS, CCTV, and incident reports. Transportation, like many other businesses, has begun to generate big data. It's difficult to develop solid prediction models based on typical shallow machine learning approaches with such a large amount of traffic data. Many areas of modern civilization are powered by machine-learning technologies. Deep learning allows computational models to learn representations of data with various degrees of abstraction, as opposed to traditional machine learning approaches that were confined to processing natural data in its raw form. An enhanced deep learning model is suggested in this research to investigate the intricate relationships between highways, traffic, environmental factors, and traffic crashes. Deep learning is a brand-new state-of-the-art machine learning method that has sparked a lot of interest in both academic and industrial research. This research is aimed at the possibility of using a deep learning technique to create a worldwide road safety performance function that can be used to forecast projected crash frequency across different areas. For the effective deployment of an intelligent transportation system to offer proportional levels of medical help and transportation in a timely manner in traffic accidents, an accurate and crash frequency forecast approach is required. Modeling real-time crash risk prediction is an essential method for recognizing traffic conditions that cause crashes, and it may be utilized in active traffic management control to prevent traffic accidents and assure traffic safety. The current approaches for predicting the frequency of traffic crashes mostly rely on shallow prediction models and statistical models. Many kinds of research have been bound to generate improved traffic management systems in addition to enhancing traffic safety. The real-time crash risk prediction is one of its most important features.