Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/24547
Title: Low-dimensional models for missing data imputation in road networks
Authors: Asif, Muhammad Tayyab
Mitrovic, Nikola
Garg, Lalit
Dauwels, Justin
Jaillet, Patrick
Keywords: Missing observations (Statistics)
Low-dimensional semiconductors
Computer networks -- Monitoring
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Asif, M. T., Mitrovic, N., Garg, L., Dauwels, J., & Jaillet, P. (2013). Low-dimensional models for missing data imputation in road networks. IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver. 3527-3531.
Abstract: 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.
URI: https://www.um.edu.mt/library/oar//handle/123456789/24547
Appears in Collections:Scholarly Works - FacICTCIS

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