Urban traffic accident risk prediction for knowledge-based mobile multimedia service

Abstract

Traditional accident prediction models have been mostly designed with statistical analysis that finds and analyzes the causal relationships between traffic accidents and a variety of human, road geometry, and environmental factors. However, these statistical methods have limitations in that they are based on assumptions about data distribution and function type. Therefore, this study suggests an accident prediction model using deep learning. This newly suggested risk prediction model is for predicting risk by reflecting static features of the road, such its length and the speed limit on it, and dynamic features of the road, such as traffic volume when driving on it, and the altitude and azimuth of the sun. For this purpose, 4470 accident cases, collected over 5 months from August to December 2018 in Seoul-the most complex, high-traffic, and accident-prone city in Korea-were analyzed. As a result of testing the model using such data, it was found to have an accuracy of 75% and recall of 81%. Based on testing results for the suggested risk prediction model, a system was developed to guide not only accident-prone regions predicted using statistical data but to also guide a risk level for the road. This level of risk is estimated based upon each given situation, so it can change even for the same road. This guide system can be used to provide a level of risk for each road segment and region but also to improve roads with recommendations, such as installation of safety features. In addition, it could support a mobile system that provides a driver with the optimized driving path for safety.

Traffic Accident Risk Prediction of Tunnel Based on Multi-Source Heterogeneous Data Fusion

“…Gray processing methods can be roughly divided into maximum gray processing, average gray processing and weighted average gray processing. This paper selects the most commonly used average gray processing method shown as: = (, , ) / (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) where R, B and G are the values in the three gray value comp onents The extraction scale was set to 5 and the direction to 8. After calculation, the real and imaginary parts of 40 filters were obtained, as shown in Figure 4 and 5 respectively.…”

“…The above researchs show that the current predicting model of highway tunnel accidents mainly focus on establishing the causal relationship between structured data and events. However, modeling only through structured data will lose the event evolution information contained in unstructured data, and the constructed model cannot reflect the explanatory power of unstructured data on accident prediction evolution [15][16][17].…”

Detection of Road Traffic Anomalies Based on Computational Data Science

“…In AV network, the anomalies are caused by traffic accidents, bad weather, road work, and repeated lane changing attempts [19]. In addition, there are a number of other challenges [18] faced by AVs, such as noise and interference, which are further sources of anomalies in traffic flow. AVs collect various types of data via onboard devices and communication with devices on the Internet of Things [17][35] [37].…”

Study and Design of Scalable Multimedia Service System

“…e emergence of multimedia services, especially the emergence of video and audio services, is not like the previous e-mail, simple WEB and FTP services. Its service form begins to shift from reliable interaction to realtime interaction, which puts forward higher requirements for network performance [3,4].…”