Low-dimensional models for missing data imputation in road networks
Author(s)Asif, Muhammad Tayyab; Mitrovic, Nikola; Garg, Lalit; Dauwels, Justin H. G.; Jaillet, Patrick
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Intelligent transport systems (ITS) require data with high spatial and temporal resolution for applications such as modeling, traffic management, prediction and route guidance. However, field data is usually quite sparse. This problem of missing data severely limits the effectiveness of ITS. Missing values are usually imputed by either using historical data of the road or current information from neighboring links. In most scenarios, information from some or all of neighboring links might not be available. Furthermore, historical data may also be incomplete. To overcome these issues, we propose methods which can construct low-dimensional representation of large and diverse networks, in presence of missing historical and neighboring data. We use these low-dimensional models to reconstruct data profiles for road segments, and impute missing values. To this end we use Fixed Point Continuation with Approximate SVD (FPCA) and Canonical Polyadic (CP) decomposition for incomplete tensors to solve the problem of missing data. We apply these methods to expressways and a large urban road network to assess their performance for different scenarios.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Institute of Electrical and Electronics Engineers (IEEE)
Asif, Muhammad Tayyab, Nikola Mitrovic, Lalit Garg, Justin Dauwels, and Patrick Jaillet. “Low-Dimensional Models for Missing Data Imputation in Road Networks.” 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (n.d.).
Author's final manuscript