A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles

Hani Alnami, Imad Mahgoub (), Hamzah Al-Najada and Easa Alalwany
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Hani Alnami: Electrical Engineering & Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA
Imad Mahgoub: Electrical Engineering & Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA
Hamzah Al-Najada: Electrical Engineering & Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA
Easa Alalwany: College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia

Future Internet, 2025, vol. 17, issue 3, 1-21

Abstract: Abnormal traffic flow prediction is crucial for reducing traffic congestion. Most recent studies utilized machine learning models in traffic flow detection systems. However, these detection systems do not support real-time analysis. Centralized machine learning methods face a number of challenges due to the sheer volume of traffic data that needs to be processed in real-time. Thus, it is not scalable and lacks fault tolerance and data privacy. This study designs and evaluates a scalable distributed machine learning-based scheme to predict highway traffic flows in real-time. The proposed system is segment-based where the vehicles in each segment form a cluster. We train and validate a local Random Forest Regression (RFR) model for each vehicle’s cluster (highway-segment) using six different hyper parameters. Due to the variance of traffic flow patterns between segments, we build a global Distributed Machine Learning Random Forest (DMLRF) regression model to improve the system performance for abnormal traffic flows. Kappa Architecture is utilized to enable real-time prediction. The proposed model is evaluated and compared to other base-line models, Linear Regression (LR), Logistic Regression (LogR), and K Nearest Neighbor (KNN) regression in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R 2 ), and Adjusted R-Squared (AR 2 ). The proposed scheme demonstrates high accuracy in predicting abnormal traffic flows while maintaining scalability and data privacy.

Keywords: internet of vehicles; real-time traffic flow prediction; intelligent transportation systems; big data processing; distributed machine learning; distributed architecture; Apache Spark (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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