Comprehensive Approaches to Traffic Flow Prediction

Abstract

Intelligent transportation systems require traffic flow prediction, and anomaly detection is the key to ensuring accuracy. Using traditional statistical models to handle complex traffic scenarios is becoming increasingly challenging as urbanization accelerates. Consequently, to enhance the precision of forecasting traffic flow and detecting anomalies, various deep learning techniques, and emerging methods have been introduced. The purpose of this paper is to examine how traditional and deep learning methods, as well as emerging technologies, can be used to predict traffic flow and detect anomalies, including techniques such as the autoregressive integrated moving average model (ARIMA), K-nearest neighbor (KNN) algorithm and convolutional neural network (CNN). The result shows that emerging technologies can improve system performance in multiple traffic environments by accurately extracting features from complex data. The research of this paper provides a theoretical basis for the traffic management department and helps to realize a safer and more efficient advanced transportation system.