Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-05090-0_10 | ADMA |
Field | DocType | Citations |
Data mining,Large networks,Traffic flow,Computer science,Curse of dimensionality,Autoregressive integrated moving average,Tucker decomposition,Smart city,Artificial intelligence,Intelligent transportation system,TRIPS architecture,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manish Bhanu | 1 | 0 | 0.68 |
| Shalini Priya | 2 | 3 | 1.73 |
| Sourav Kumar Dandapat | 3 | 37 | 7.45 |
| Joydeep Chandra | 4 | 26 | 10.77 |
| João Mendes-Moreira | 5 | 317 | 29.50 |