Boosting Algorithms for Delivery Time Prediction in Transportation Logistics
This work addresses the challenge of reducing delays in postal services, but it is incremental as it applies existing boosting methods to a specific domain.
The paper tackled the problem of long-term delivery time prediction for postal services, showing that boosting algorithms like LightGBM and CatBoost outperform linear regression, bagging, and random forest in accuracy and runtime efficiency.
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.