Paper Info
Title
Tensor Decomposition for Spatial-Temporal Traffic Flow Prediction with Sparse Data.
Abstract
Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.
Year
DOI
Venue
2020
10.3390/s20216046
SENSORS
Keywords
DocType
Volume
tensor decomposition,traffic flow,sparse data,traffic correlation pattern
Journal
20
Issue
ISSN
Citations 
21
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Funing Yang111.38
Guoliang Liu200.34
Liping Huang343.87
Cheng Siong Chin400.68