Predicting Traffic Volume With Artificial Intelligence and Machine Learning
SMRTR summary
Machine learning models were evaluated for predicting traffic flow on I-94 highway using 2012-2018 data. Random forest outperformed linear regression and decision trees, achieving an R2 of 0.849 and lower mean squared error. This highlights random forest's potential for improving traffic forecasting and urban management.
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