Mechanistic Identification of Freight Activity Stops from Global Positioning System Data
Mechanistic Identification of Freight Activity Stops from Global Positioning System Data
复制标题
根据全球定位系统数据机械识别货运活动停靠点
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Xia Yang
中科院分区：
文献类型：
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作者：
J. Holguín;Trilce Encarnación;Sofía Pérez;Xia Yang
货运提货和交货的识别（本文中称为“货运活动”）对于表征货运运营和评估货运系统的性能至关重要。然而，从全球定位系统 (GPS) 数据中识别货运活动停靠点具有挑战性，特别是在交通拥挤的城市货运中。本文提出了一种从原始 GPS 数据中识别货运活动停靠点的机制（因为它基于驾驶模式的物理原理）。该程序的实施是为了在三个不同的案例研究中确定停靠点，这些案例研究呈现了广泛的交通状况：哥伦比亚的巴兰基亚；孟加拉国达卡；和美国纽约市。结果表明，该程序在识别货运活动停靠点时的平均准确率达到 98.6% 以上。将所提出的过程的结果与支持向量机、随机森林和 k 最近邻的结果进行了比较。在使用逐秒 GPS 数据正确分类货运活动方面，机械程序优于这些方法。
The identification of freight pick-ups and deliveries, referred to as “freight activity” in this paper, is crucial to characterizing freight operations and assessing the performance of freight transportation systems. However, identifying freight activity stops from global positioning system (GPS) data is challenging, particularly in urban freight where congested traffic is common. This paper presents a mechanistic—because it is based on the physics of driving patterns—procedure to identify freight activity stops from raw GPS data. The procedure was implemented to identify stops in three distinct case studies that present a wide range of traffic conditions: Barranquilla, Colombia; Dhaka, Bangladesh; and New York City, United States. The results show that the procedure achieves an average accuracy of above 98.6% when identifying freight activity stops. The results of the proposed procedure were compared with results from support vector machines, random forest, and k nearest neighbors. The mechanistic procedure outperformed these methods in correctly classifying freight activity using second-by-second GPS data.