An Approach to Multi Stream Feature Fusion for Traffic Prediction

Abstract

Smart transport systems (ITS) rely on accurate and timely traffic flow forecasts. Recent developments in graph based artificial neural networks, predictions have been positive. However, significant challenges remain, most notably in graph construction and model time complexity. In this paper, we present a multi stream feature merger technique for identifying plus merging rich features in traffic information, and we build graphs using a data driven nearby matrix instead of a distance based matrix. We compute the correlation of Spearman correlations across monitor sites to provide the first proximity framework, which we optimise throughout learning. Another popular feature extraction method used in NLP jobs is the BoW. It is the easiest and most customizable method for obtaining the characteristics of a document. BoW examines the histogram of the word inside the text. The frequency of the words is used as a function in the set's training. The count victories is used to implement the BoW technique in this study. Victimization is the process of getting numerical vectors from a textual data collection. The frequency of words is tallied, showing that tokens have been counted and the token vectors have been created. The BoW assigns a value to each property depending on how frequently those traits occur.