A MULTI-STREAM FEATURE FUSION APPROACH FOR TRAFFIC PREDICTION

Abstract

ighway traffic accidents remain the biggest cause of mortality notwithstanding
an increase in web traffic awareness. Road accidents pose a serious hazard to people's lives and
property in emerging nations. Traffic accidents are caused by a variety of factors, some of which
are more important than others in determining how serious an accident is. Data extraction
methods can help with the prediction of key aspects of collapse intensity. In this study, utilising
Random Forest, it was discovered that a number of characteristics have a strong correlation with
the seriousness of highway crashes. Range, temperature, wind chill, humidity, exposure, and
wind orientations are the main factors influencing surprise severity. To forecast the severity of
traffic accidents, this study blends RFCNN, or Random Forest and Convolutional Semantic
Network, with existing deep learning and artificial intelligence models. Comparing the
effectiveness of the proposed strategy to a variety of fundamental learner classifiers is necessary.
The crash statistics for the United States from February 2016 to June 2020 are among the data
considered in the analysis