کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6936189 | 1449662 | 2018 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition](/preview/png/6936189.png)
چکیده انگلیسی
Missing data is an inevitable and ubiquitous problem in data-driven intelligent transportation systems. While there are several studies on the missing traffic data recovery in the last decade, it is still an open issue of making full use of spatial-temporal traffic patterns to improve recovery performance. In this paper, due to the multi-dimensional nature of traffic speed data, we treat missing data recovery as the problem of tensor completion, a three-procedure framework based on Tucker decomposition is proposed to accomplish the recovery task by discovering spatial-temporal patterns and underlying structure from incomplete data. Specifically, in the missing data initialization, intrinsic multi-mode biases based traffic pattern is extracted to perform a robust recovery. Thereby, the truncated singular value decomposition (SVD) is introduced to capture main latent features along each dimension. Finally, applying these latent features, the missing data is eventually estimated by the SVD-combined tensor decomposition (STD). Empirically, relying on the large-scale traffic speed data collected from 214 road segments within two months at 10-min interval, our experiment covers two missing scenarios - element-like random missing and fiber-like random missing. The impacts of different initialization strategies for tensor decomposition are evaluated. From numerical analysis, a sensitivity-driven rank selection can not only choose an appropriate core tensor size but also determine how much features we actually need. By comparison with two baseline tensor decomposition models, our method is shown to successfully recover missing data with the highest accuracy as the missing rate ranges from 20% to 80% under two missing scenarios. Moreover, the results have also indicated that an optimal initialization for tensor decomposition could suggest a better performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Transportation Research Part C: Emerging Technologies - Volume 86, January 2018, Pages 59-77
Journal: Transportation Research Part C: Emerging Technologies - Volume 86, January 2018, Pages 59-77
نویسندگان
Xinyu Chen, Zhaocheng He, Jiawei Wang,