Accident Detection and Alert System Using Big Data Analytics

Abstract

Road accidents are a serious hazard to life and limb and result in considerable financial damages on a global scale. For reducing reaction times and ensuring that victims receive aid quickly, quick and effective accident detection technologies are essential. A brief description of a crash monitoring and warning system that uses big data analytics to improve traffic safety is provided in this abstract. To correctly identify and anticipate accidents, the proposed system incorporates a variety of info sources such as real-time traffic data, meteorological information, and car telematics. The system can analyze huge amounts of disparate data in real-time by using modern data analytics techniques like machine neural networks and predictive modeling. Road accidents are becoming more commonplace across the world, which calls for the creation of cutting-edge technologies that can quickly identify incidents and notify the appropriate authorities for fast help. The use of big data analytics has developed in recent years as a viable strategy to improve accident identification and response. These systems are able to recognize possible accidents and produce alerts in real time by using enormous quantities of varied sources of data, such as real-time traffic data, meteorological conditions, and vehicle telematics. Data Preprocessing: To guarantee quality and consistency, collected data is preprocessed. This entails managing missing values, data normalization, noise reduction, and data cleaning. Relevant elements, such as traffic patterns, weather patterns, types of roads, and vehicle behavior, are retrieved from preprocessed data. Techniques for feature engineering turn unstructured data into useful representations. Alert generation: When an accident is detected, alerts are created and transmitted to the appropriate parties, such as medical professionals, law enforcement, and passing cars, with details on the accident's location, level of seriousness, and suggested next steps. System assessment: To determine the efficiency of the system in accurately detecting accidents and producing timely alerts, performance assessment is carried out using metrics including reliability, precision, recollection, and reaction time. Taken of Radar Model Example, Azimuth, Elevation, Horizontal resolution, Maximum detectable speed Taken of Evaluation parameters: Radar type, Short Range, Radar Mid-Range, Radar Long Range Radar Model Example, Azimuth, Elevation, Horizontal Resolution, and Maximum Detectable Speed are alternative parameters. These parameters are defined in the materials and methods section. The figure below represents an accident detection and alert system using big data analytics with performance value, weightage, weighted normal decision matrix, and preference score. Every parameter is monitored to some extent in the graph, and the evaluation parameters are also precisely stated in the materials and techniques section. The Figure below represents an accident detection and alert system using big data analytics with performance value, weightage, weighted normal decision matrix, and the preference score the alternative parameters are Radar Model Example, Azimuth, Elevation, Horizontal Resolution, and Maximum Detectable speed which is defined as in the materials and methods section. The Evaluation parameters are also clearly defined in the materials and methods section, every parameter is measured to a certain degree in the graph.