Name
Abstract | ||
|---|---|---|
Existing time-series models that are used for short-term traffic condition forecasting are mostly univariate in nature. Generally, the extension of existing univariate time-series models to a multivariate regime involves huge computational complexities. A different class of time-series models called structural time-series model (STM) (in its multivariate form) has been introduced in this paper to develop a parsimonious and computationally simple multivariate short-term traffic condition forecasting algorithm. The different components of a time-series data set such as trend, seasonal, cyclical, and calendar variations can separately be modeled in STM methodology. A case study at the Dublin, Ireland, city center with serious traffic congestion is performed to illustrate the forecasting strategy. The results indicate that the proposed forecasting algorithm is an effective approach in predicting real-time traffic flow at multiple junctions within an urban transport network. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TITS.2009.2021448 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
computational complexity,time series,traffic control,computational complexity,multivariate short-term traffic flow forecasting,time-series analysis,Multivariate,prediction methods,time series,traffic flow | Time series,Traffic flow,Demand forecasting,Multivariate statistics,Simulation,Intelligent transportation system,Engineering,Artificial neural network,Univariate,Traffic congestion | Journal |
Volume | Issue | ISSN |
10 | 2 | 1524-9050 |
Citations | PageRank | References |
49 | 3.86 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bidisha Ghosh | 1 | 85 | 9.13 |
| Biswajit Basu | 2 | 101 | 8.28 |
| Margaret O'Mahony | 3 | 123 | 10.41 |