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首页> 外文期刊>International journal of production economics >Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0
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Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0

机译：具有交通IOT基础架构的务实实时物流管理：物流运费的大数据预测分析4.0

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摘要

When studying the vehicle routing problem, especially for on-time arrivals, the determination of travel time plays a decisive role in the optimization of logistics companies. Traffic Internet of Things (IoT) connects ubiquitous devices and collects data from various channels like traffic cameras, vehicle detectors, GPS, sensors, etc. that can be used to analyze real-time traffic status and eventually increase the efficiency of logistics management for Logistics 4.0. However, big IoT data contain joint features that interact non-linearly and complicatedly, thus increasing the stochastic nature and difficulty of determining travel time on real-time basis. This research proposes a novel method (named the gradient boosting partitioned regression tree model) to forecast travel time based on big data collected from the industrial IoT infrastructure. The proposed method separates the global regression tree model based on the gradient boosting decision tree into several partitions to capture the timevarying features simultaneously - that is, to subdivide the non-linearity into fragments and to characterize the feature interactions in a manageable way with recursive partitions. We illustrate several analytical properties with manageable advantages in terms of big data analytics of the proposed method and apply it to real traffic IoT data. Findings of this research show that the proposed method performs successfully at enhancing the predictive accuracy of travel time after empirically comparing it with other computational methods.
机译：在研究车辆路由问题时，特别是对于准时到达，旅行时间的确定在物流公司的优化中起着决定性作用。事物的交通互联网（IOT）连接无处不在的设备，并从各种通道收集数据，如交通摄像机，车辆探测器，GPS，传感器等，可用于分析实时交通状态，并最终提高物流物流管理效率4.0。然而，大型物联网数据包含非线性和复杂相互作用的联合特征，从而增加了在实时确定旅行时间的随机性质和难度。本研究提出了一种新的方法（命名为渐变升值分区回归树模型），以基于从工业物联网基础设施收集的大数据预测旅行时间。该方法将全局回归树模型基于渐变升压决策树分离为几个分区，以同时捕获时变特征 - 即将非线性分成片段，并以可管理的方式对递归分区表征特征交互。我们在提出的方法的大数据分析方面说明了几种具有可管理优势的分析性质，并将其应用于实际交通IOT数据。该研究的结果表明，该方法在与其他计算方法统一地比较后，提高了旅行时间的预测准确性成功。

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