Research on intelligent vehicle Traffic Flow control algorithm based on data mining

Abstract

Traffic Congestion (TC) is increasing due to urban growth and vehicle numbers, rendering the development of cities and people's well-being difficult. Traffic Prediction (TP) and control systems have been required to improve Traffic Flow (TF) and reduce TC because standard methods are unsuitable. The paper proposes an innovative method for traffic control using the Dynamic Zone Segmentation Algorithm (DZSA) to solve this significant issue. The algorithm uses real-time data and road conditions to partition city traffic into manageable units, enhancing the adaptability and accuracy of Traffic Prediction (TP) performance. Applying DZSA, the recommended Long Short-Term Memory + Bayesian Structural Time Series (LSTM + BSTS) learning model optimizes TP by integrating the best features of conventional and Machine Learning (ML) methods. The model optimized quality performance when experimentally tested against other benchmark models using metrics like Mean Absolute Error, Mean Absolute Scaled Error, Accuracy Percent, Root Mean Squared Error, and Mean Absolute Percent Error. The recommended model, LSTM + BSTS, shows a minimal error rate of 6.68%, indicating its success.

Keywords

• Machine learning
• Dynamic zone segmentation
• Traffic prediction
• Traffic congestion
• Long short-term memory
• Bayesian structural time series