论文标题
增强运输物流交付时间预测算法
Boosting Algorithms for Delivery Time Prediction in Transportation Logistics
论文作者
论文摘要
旅行时间是运输的关键措施。准确的旅行时间预测对于操作和高级信息系统也是基础。对于短期旅行时间预测，存在各种解决方案，例如利用实时GPS数据和优化方法跟踪车辆路径的解决方案。但是，可靠的长期预测仍然具有挑战性。我们在本文中显示了旅行时间的适用性和实用性，即邮政服务的交付时间预测。我们研究了几种方法，例如线性回归模型和基于树木的合奏，例如随机森林，包装和增强，它们可以通过进行大量实验并考虑许多可用性场景来预测交付时间。结果表明，旅行时间预测可以帮助减轻邮政服务的高延迟。我们表明，与其他基线相比，某些增强算法（例如光梯度增强和catboost）在准确性和运行时效率方面具有更高的性能，例如线性回归模型，装袋回归器和随机森林。
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.
