Joint imputation of multi-source traffic data based on shared multi-dimensional spatiotemporal feature

WANG Jiyu; CHEN Ruixiang; HE Zhaocheng; ZHU Yiting; WU Zhigang; XU Huanting

doi:10.13471/j.cnki.acta.snus.ZR20240091

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Joint imputation of multi-source traffic data based on shared multi-dimensional spatiotemporal feature

Research articles | 更新时间：2024-11-07

- Joint imputation of multi-source traffic data based on shared multi-dimensional spatiotemporal feature
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 63, Issue 5, Pages: 167-176(2024)
- 作者机构：
  
  1.中山大学智能工程学院 / 广东省智能交通系统重点实验室，广东深圳 518107
  2.鹏城实验室，广东深圳 518055
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.ZR20240091
  CLC： U491
- Published：25 September 2024，
  
  Published Online：22 July 2024，
  
  Received：31 March 2024，
  
  Accepted：22 April 2024
- 稿件说明：
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王纪禹,陈锐祥,何兆成等.基于时空特征共享的多源交通数据修复[J].中山大学学报(自然科学版)(中英文),2024,63(05):167-176.

WANG Jiyu,CHEN Ruixiang,HE Zhaocheng,et al.Joint imputation of multi-source traffic data based on shared multi-dimensional spatiotemporal feature[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(05):167-176.
王纪禹,陈锐祥,何兆成等.基于时空特征共享的多源交通数据修复[J].中山大学学报(自然科学版)(中英文),2024,63(05):167-176. DOI： 10.13471/j.cnki.acta.snus.ZR20240091.

WANG Jiyu,CHEN Ruixiang,HE Zhaocheng,et al.Joint imputation of multi-source traffic data based on shared multi-dimensional spatiotemporal feature[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(05):167-176. DOI： 10.13471/j.cnki.acta.snus.ZR20240091.

摘要

本文提出了一种基于多维度特征共享的多层稀疏张量分解模型（MFS-MSTD）。该模型在CP（CANDECOMP/PARAFAC）分解的基础上，对时空因子矩阵施加低秩正则化；并采用共享低秩因子矩阵的机制表达多源交通数据之间的互补性，在非随机缺失或路段级缺失的场景下完成了因子矩阵的梯度更新。实例验证表明：在速度数据非随机缺失场景下，MFS-MSTD在RMSE、MAE和MAPE三个误差指标上相较于基线方法平均降低17%、21%和18%；在流量数据非随机缺失场景下，RMSE、MAE和MAPE平均降低52%、54%和33%。面对更复杂的路段级缺失场景，MFS-MSTD的修复性能优于TGMC-S和MTNTF两个基线模型，能很好地拟合出未观测路段的交通流量变化。

Abstract

This paper proposes a multi-layer sparse tensor decomposition model based on multidimensional feature sharing（MFS-MSTD）. On the basis of CP（CANDECOMP/PARAFAC） decomposition， this model applies low rank regularization to the spatiotemporal factor matrix. The mechanism of sharing a low rank factor matrix is adopted to express the complementarity between multi-source traffic data， and the gradient update of the factor matrix can be completed in non-random missing or segment level missing scenarios. A real experment results show that in the scenario of non-random missing speed data， MFS-MSTD reduces the RMSE， MAE， and MAPE by an average of 17%， 21%， and 18% compared to the baseline method； in the scenario of non-random missing traffic data， RMSE， MAE， and MAPE decreased by an average of 52%， 54%， and 33%. In the face of more complex road segment missing scenarios， the imputation performance of MFS-MSTD is superior to the baseline models TGMC-S and MTNTF， and it can well fit the trend of traffic volume changes in unobserved road segments.

关键词

交通数据修复多源数据共享因子矩阵CP分解

Keywords

traffic data imputationmulti-source traffic datasharing factor matrixCP decomposition

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