An Optimized Machine Learning Model For Forecasting Traffic Speeds In Urban Environments

Abstract

The Potential For Data Collecting And Analytics-based Intelligent Traffic Systems (ITS) To Improve Traffic Systems Is Now Being Investigation. Predicting How Fast Traffic Will Be Moving Is One Of The Most Important Uses For This Technology. In Order To Improve Traffic Management And Maximize The Overall Efficiency Of Urban Mobility, Intelligent Transportation Systems Rely On Machine Learning For Traffic Speed Forecasts. Several Data Sources Are Used In This Procedure To Forecast Traffic Speeds, Including Past Trends, Data From Sensors In Real-time, And Environmental Conditions. The Ability Of Machine Learning Models To Sift Through Mountains Of Data In Search Of Hidden Patterns And Correlations And Provide Reliable Forecasts Makes Them Indispensable In This Field. A Neural Network Model For Traffic Speed Predictions Based On Particle Swarm Optimization (PSO) Is Presented In This Research. By Using The PSO, The Network Weights May Be Adaptively Updated, Which Is Different From Traditional Neural Network Models. Both The MAPE And The Forecasting Accuracy Metrics Show That The Suggested Method Is Superior Than The Current Baseline Methods.

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