Forecasting Traffic Flow in Big Cities Using Modified Tucker Decomposition

• Published in: Advanced Data Mining and Applications Vol. 11323; pp. 119 - 128
• Main Authors: Bhanu, Manish, Priya, Shalini, Dandapat, Sourav Kumar, Chandra, Joydeep, Mendes-Moreira, João
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: CP decomposition; CUR; ODM; Time-series; Traffic flow; Tucker
• ISBN: 9783030050894; 3030050890
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-05090-0_10

An efficient traffic-network is an essential demand for any smart city. Usually, city traffic forms a huge network with millions of locations and trips. Traffic flow prediction using such large data is a classical problem in intelligent transportation system (ITS). Many existing models such as ARIMA, SVR, ANN etc, are deployed to retrieve important characteristics of traffic-network and for forecasting mobility. However, these methods suffer from the inability to handle higher data dimensionality. The tensor-based approach has recently gained success over the existing methods due to its ability to decompose high dimension data into factor components. We present a modified Tucker decomposition method which predicts traffic mobility by approximating very large networks so as to handle the dimensionality problem. Our experiments on two big-city traffic-networks show that our method reduces the forecasting error, for up to 7 days, by around 80% as compared to the existing state of the art methods. Further, our method also efficiently handles the data dimensionality problem as compared to the existing methods.