A PSO-Neural Network Hybrid Machine Learning Approach for Traffic Speed Prediction

Intelligent traffic systems (ITS) are being explored to make traffic systems more efficient based on data collection and analytics. One of the major applications to avoid congestions in traffic speed forecasting. Traffic speed forecasting using machine learning is a crucial aspect of intelligent transportation systems, aiming to enhance traffic management and optimize the overall efficiency of urban mobility. This process involves the utilization of various data sources, such as historical traffic patterns, real-time sensor data, and environmental factors, to predict future traffic speeds. Machine learning models play a pivotal role in this domain, as they can analyze complex patterns and relationships within the data to generate accurate predictions. This paper presents a Particle Swarm Optimization (PSO)-Neural network model for traffic speed forecasting. Contrary to conventional neural network models, the PSO is used to adaptively update the network weights. The results clearly indicate that the proposed approach outperforms existing baseline approaches in terms of MAPE and Forecasting Accuracy.