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.

Travel Time Reliability Estimation in Urban Road Networks: Utilization of Statistics Distribution and Tensor Decomposition

“…One efective solution for handling data sparsity is constructing models, such as matrix and tensor factorization methods [12]. Tensor factorization is suitable for predicting historical missing data [13].…”

Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition

“…Yang et al pointed out that the main challenge of traffic flow prediction was the data sparsity problem. To tackle this problem, they proposed the representation of the traffic flow using a tensor and utilized the gradient descent strategy to design a traffic flow prediction Algorithm [35].…”

Missing Traffic Data Imputation with a Linear Generative Model Based on Probabilistic Principal Component Analysis

“…Traffic data generated by loop detectors or floating cars in urban road networks serve as the foundation for various data-driven applications in intelligent transportation systems, including traffic forecasting and traffic control [ 1 , 2 , 3 ]. However, even with ubiquitous sensing data, the missing data problem is almost inevitable due to either detector faults or a limited number of probe vehicles operating as mobile sensors in road networks, which means not each road in the network is covered by a detector or traveled by a probe vehicle in each minute [ 4 , 5 , 6 ]. Such an issue of missing traffic data poses obstacles for many further data-driven explorations in both academic and industrial fields, e.g., the link-based traffic status modeling, and network-wise traffic dynamics capturing [ 7 , 8 ].…”