Which machine learning technique revolutionized demand prediction in transportation by enabling Uber to forecast rider demand with 85% accuracy during high-volume events?

Modern transportation companies are increasingly turning to advanced machine learning algorithms to optimize their operations and improve customer experiences. From ride-sharing platforms to public transit systems, predictive analytics has transformed how transportation services anticipate and respond to consumer demand patterns. This trivia question explores a groundbreaking ML technique that revolutionized demand forecasting in the transportation sector.

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Neural Prophet - a time series forecasting algorithm that combines neural networks with Prophet's decomposable time series model
Random Forest Regression - an ensemble learning method that constructs multiple decision trees during training
LSTM Networks - Long Short-Term Memory neural networks specialized for processing sequential time-series data
XGBoost - an optimized gradient boosting algorithm designed to improve computational speed and model performance