Missing data imputation for traffic congestion data based on joint matrix factorization

In reality, the missing of some traffic data is inevitable due to some unexpected errors, which not only affects traffic management but also hinders the development of traffic data research. In this paper, we propose a novel Imputation Model for traffic Congestion data, CIM for short, based on joint matrix factorization. CIM jointly models the characteristics of traffic congestion patterns, including periodicity, road similarity and temporal coherence to estimate the missing congestion values. In particular, we first construct an order-3 tensor based on the traffic congestion data. Then, we model the periodicity and road similarity via joint matrix factorization by exploiting the spatial and temporal information. Finally, we incorporate the local constraints into the process of matrix factorization to ensure the temporal coherence. Experimental results on a real traffic dataset indicate that modeling the three features of congestion patterns simultaneously is effective and CIM outperforms the baselines for the task of missing traffic data imputation. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a novel Imputation Model for traffic Congestion data, CIM, based on joint matrix factorization to estimate missing congestion values. Experimental results show that modeling the periodicity, road similarity, and temporal coherence of congestion patterns simultaneously is effective.