An Intelligent Transportation Systems-Based Machine Learning-Enhanced Traffic Prediction Model using Time Series Analysis and Regression Techniques*

Abstract

Translate Article

Traffic congestion represents a daunting challenge for all facets of urban development, as well as represents a universal problem in all urban areas, to various extents. In recent years, many cities have adopted the use of Intelligent Transport Systems (ITS) to manage traffic congestion. These systems are indeed useful, but they are mainly geared to predict real-time traffic congestion, yielding to a certain short-sightedness in our prediction model. Through the use of Time Series Analysis, and Regression models for traffic congestion prediction, we posit that we can address the issue. The data that these models were trained on derives from two datasets, namely a dataset from Kaggle and another from the road traffic footage of Tirana. The data then points to Gated Recurrent Units (GRU) being a more accurate time series category, and Support Vector Regression (SVR) to be a better performer than linear regression.