XGBoost predicts total travel time under road closure scenarios more accurately than simple heuristics and other regression models, reaching a MAPE around 11 to 15 percent.
Transportation Science 39(4), 446–450 (2005), https://www.jstor.org/stable/25769266, publisher: INFORMS
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Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling
XGBoost predicts total travel time under road closure scenarios more accurately than simple heuristics and other regression models, reaching a MAPE around 11 to 15 percent.