Sparse link travel time estimation using big data of floating car

被引：0
|
作者：
Zhang F. [1 ]
Zhu X. [1 ,2 ]
Guo W. [1 ,2 ]
Hu T. [1 ,2 ]
机构：
[1] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan
[2] Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2017年 / 42卷 / 01期
关键词：
Artificial neural network; Big data of floating car; Data sparsity; Spatiotemporal association relationship features; Travel time inference;
D O I：
10.13203/j.whugis20150425
中图分类号：
学科分类号：
摘要：
Although thereare massive quantities of floating car GPS data, partial links lack real data during certain periods of time. Therefore, we cannot estimate target link travel time. Consideringthe sparse data problemwhen using floating car data toestimate link travel time, we put forward aninferred method based on big floating car data. We designed a three-layer artificial neural network modelwhose input i and output information are the feature relationship and the travel time ratio between target link and adjacent link, respectively. We obtained spatiotemporal traffic association relationships using big historical floating car data and then inferred link travel times. The model was verified using big historical floating car data for Wuhanfrom March to July, 2014. The MAPE of estimated values of link travel time was less than 25% demonstrating the effectiveness of the proposed method. © 2017, Research and Development Office of Wuhan University. All right reserved.
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页码：56 / 62
页数：6
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