Prediction of Highway Traffic Flow Based on Artificial Intelligence Algorithms Using California Traffic Data

TL;DR

This paper develops machine learning models using California traffic data to predict highway traffic flow, aiming to improve traffic management and reduce congestion.

Contribution

It compares multiple AI algorithms and identifies optimal data collection intervals for accurate traffic flow prediction.

Findings

Both models perform best with 10-minute data intervals.

Random Forest outperforms Linear Regression in prediction accuracy.

The models demonstrate potential for real-time traffic management applications.

Abstract

The study "Prediction of Highway Traffic Flow Based on Artificial Intelligence Algorithms Using California Traffic Data" presents a machine learning-based traffic flow prediction model to address global traffic congestion issues. The research utilized 30-second interval traffic data from California Highway 78 over a five-month period from July to November 2022, analyzing a 7.24 km westbound section connecting "Melrose Dr" and "El-Camino Real" in the San Diego area. The study employed Multiple Linear Regression (MLR) and Random Forest (RF) algorithms, analyzing data collection intervals ranging from 30 seconds to 15 minutes. Using R^2, MAE, and RMSE as performance metrics, the analysis revealed that both MLR and RF models performed optimally with 10-minute data collection intervals. These findings are expected to contribute to future traffic congestion solutions and efficient traffic management.