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.
International Journal of Transportation Science and Technology 5(1), 17–27 (Aug 2016)
1 Pith paper cite this work, alongside 22 external citations. Polarity classification is still indexing.
1
Pith paper citing it
22
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.