Urban Network Traffic Analysis, Data Imputation, and Flow Prediction based on Probabilistic PCA Model of Traffic Volume Data

The growth of vehicle mobility in the past decades and increased traffic complexity leads to a need for traffic management systems, especially in large-scale urban traffic networks. The erroneous data problems are common problems that affect traffic management systems. The traffic management systems also relied on traffic prediction particularly in traffic signal control and route guidance. This paper investigated probabilistic principal component analysis (PPCA) methods to impute missing traffic count data and predict future data. We also investigated the resulting principal components' significance in urban traffic analysis. These methods are applied to traffic count data from vehicle detectors in the urban network of Surabaya city, Indonesia. The results show that the PPCA-based data imputation method is able to impute missing data with imputation error under 20% WMAPE. The resulting principal components analysis demonstrates that 1
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principal component scores can be seen as a fundamental temporal pattern of the Surabaya urban network while the characteristic of the link can be derived from the 1
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principal component coefficient. We also demonstrate that 1
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principal component coefficient of the link might detect outliers or anomalies such as detector malfunction and unique temporal pattern. PPCA can also be used to predict future data based on observed data, but experiments show that even though the majority of the links can be predicted accurately, some links are having large errors that might be caused by different temporal patterns between future data and observed data.