Comparative Validation of Spatial Interpolation Methods for Traffic Density for Data-driven Travel-time Prediction
Comparative Validation of Spatial Interpolation Methods for Traffic Density for Data-driven Travel-time Prediction
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DOI：
10.1007/s13177-022-00326-0
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发表时间：
2022-10
影响因子：
2.1
通讯作者：
Hiroki Katayama;Shohei Yasuda;T. Fuse
中科院分区：
文献类型：
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作者：
Hiroki Katayama;Shohei Yasuda;T. Fuse
在数据驱动的走时预测中，以往的研究主要是以速度作为输入。然而，从交通工程的角度来看，考虑到在自由流状态下速度变化很小，交通密度可以准确地表示从自由流状态到非自由流状态的交通状况，因此作为输入是优选的。在这项研究中，我们比较了使用空间统计和机器学习方法空间内插的交通密度的准确性，并验证了它们作为旅行时间预测输入的有效性。结果表明，即使交通密度插值简单的空间插值有助于行程时间预测的准确性，是上级比速度的早期检测交通拥挤。
In data-driven travel-time prediction, previous studies have mainly used speed as the input. However, from a traffic engineering perspective, given that speed varies little in the free-flow regime, traffic density, which can accurately represent traffic conditions from the free-flow regime to the congested-flow regime, is preferable as an input. In this study, we compared the accuracy of traffic densities spatially interpolated using spatial statistical and machine learning methods, and validated their effectiveness as inputs for travel-time prediction. The results show that even traffic density interpolated by simple spatial interpolation contributes to the accuracy of travel-time prediction and is superior to speed for early detection of traffic congestion.