Name
Title | ||
|---|---|---|
Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting |
Abstract | ||
|---|---|---|
Traffic state prediction provides key information for intelligent transportation systems (ITSs) for proactive traffic management, the importance of which has become the reason for the tremendous number of research papers in this field. Over the last few decades, the decomposition-reconstruction (DR) hybrid models have been favored by numerous researchers to provide a more robust framework for short-term traffic state prediction for ITSs. This study surveyed DR-based works for short-term traffic state forecasting that were reported in the past circa twenty years, particularly focusing on how decomposition and reconstruction strategies could be utilized to enhance the predictability and interpretability of basic predictive models of traffic parameters. The reported DR-based models were classified and their applications in this area were scrutinized. Discussion and potential future directions are also provided to support more sophisticated applications. This work offers modelers suggestions and helps to choose appropriate decomposition and reconstruction strategies in their research and applications. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22145263 | SENSORS |
Keywords | DocType | Volume |
decomposition-reconstruction, traffic state forecasting, intelligent transportation system, predictability, interpretability | Journal | 22 |
Issue | ISSN | Citations |
14 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Chen | 1 | 24 | 4.62 |
| Wei Wang | 2 | 93 | 11.54 |
| Xuedong Hua | 3 | 0 | 0.34 |
| De Zhao | 4 | 0 | 0.34 |