Forecasting short-term taxi demand using boosting-GCRF
نویسندگان
چکیده
It will be most efficient to frame operation strategies before actual taxi demand is revealed. But this is challenging due to limited knowledge of the taxi demand distribution in immediate future and is more prone to prediction errors. In this study, we develop the boosting Gaussian conditional random field (boosting-GCRF) model to accurately forecast the short-term taxi demand distribution using historical time-series demand over the study area. Comprehensive numerical experiments are conducted to evaluate the performance of boosting-GCRF as compared to five other benchmark algorithms. The results suggest that the boosting-GCRF is superior with the best modified mean absolute percentage error being 10.4%. The approach is observed to be robust based on its prediction performance on anomaly taxi demand data. In addition, the density functions generated by the boosting-GCRF model are found to well capture the actual distribution of the short-term taxi demand.
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