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.
Computer-Aided Civil and Infrastructure Engineering 34(8), 638–653 (Aug 2019)
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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.