Jihed Khiari, Cristina Olaverri-Monreal,
"Boosting Algorithms for Delivery Time Prediction in Transportation Logistics"
: Proceedings IEEE International Conference on Data Mining, IEEE, Italy, 10-2020
Boosting Algorithms for Delivery Time Prediction in Transportation Logistics
Sprache des Titels:
Proceedings IEEE International Conference on Data Mining
Travel time is a crucial measure in transportation.
Accurate travel time prediction is also fundamental for operation
and advanced information systems. A variety of solutions exist
for short-term travel time predictions such as solutions that
utilize real-time GPS data and optimization methods to track
the path of a vehicle. However, reliable long-term predictions
remain challenging. We show in this paper the applicability
and usefulness of travel time i.e. delivery time prediction for
postal services. We investigate several methods such as linear
regression models and tree based ensembles such as random
forest, bagging, and boosting, that allow to predict delivery
time by conducting extensive experiments and considering many
usability scenarios. Results reveal that travel time prediction can
help mitigate high delays in postal services. We show that some
boosting algorithms, such as light gradient boosting and catboost,
have a higher performance in terms of accuracy and runtime
efficiency than other baselines such as linear regression models,
bagging regressor and random forest.