June 22, 2024Open Access

A Comparative Study of Various Traffic Flow Prediction Techniques Using ML Models and Real-Time Analysis

Key Points

Traffic flow predictions achieved high accuracy with decision tree models, surpassing other methods.
Key evidence shows that effective feature extraction led to more reliable predictions and simpler techniques.
Assessment using various ML models compared kNN, SVR, decision tree, and Random Forest for traffic flow prediction in San Francisco's urban area with real-time analysis via CCTV data capture and processing with OpenCV and YOLOv4 for vehicle tracking, enhancing the prediction reliability and speed at the same time; accuracy was improved significantly by optimizing features chosen for analysis, indicating a preference for using decision trees in this scenario given their superior performance compared to other ML methods when responding to live data inputs for traffic congestion scenarios, therefore supporting the validity and feasibility of using modern ML techniques in traffic flow analysis.

Abstract

Due to the rapid growth of vehicles and the expanding urban landscape, the challenge of traffic congestion is becoming increasingly prominent. There has been a considerable shift towards data-driven solutions because of the availability of massive amounts of transportation-related data and the development of machine learning algorithms. In the proposed work, several feature sets for traffic flow are compared by using different ML methods: kNN, SVR, Decision tree, and Random Forest. The dataset used for the prediction is the traffic flow of the area between two hospitals in San Francisco. According to the result, feature extraction is more important than method selection to get accurate predictions. Selecting high quality features leads to fewer complex techniques which are easier to handle, are trustworthy and faster. Traffic flow prediction’s accuracy obtained by the decision tree surpasses the other methods. In addition to that, real-time analysis is applied to live feed captured with the help of CCTV. The live feed is then processed using the OpenCV library, the objects are detected using the YOLOv4 model which is further passed to DEEPSORT for vehicle tracking. This can further be processed by applying mathematical formulae to calculate the congestion on the road. Keywords Traffic analysis, Machine Learning, Traffic prediction

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