Please use this identifier to cite or link to this item:
|Title:||Multivariate traffic forecasting technique using cell transmission model and SARIMA model|
|Authors:||Szeto, W.Y. |
|Citation:||Szeto, W.Y., Ghosh, B., Basu, B., O'Mahony, M. (2009). Multivariate traffic forecasting technique using cell transmission model and SARIMA model. Journal of Transportation Engineering 135 (9) : 658-667. ScholarBank@NUS Repository. https://doi.org/10.1061/(ASCE)0733-947X(2009)135:9(658)|
|Abstract:||The paper develops a short-term space-time traffic flow forecasting strategy integrating the empirical-based seasonal autoregressive integrated moving average (SARIMA) time-series forecasting technique with the theoretical-based first-order macroscopic traffic flow model-cell transmission model. A case study in Dublin city center which has serious traffic congestion is performed to test the effectiveness of the proposed multivariate traffic forecasting strategy. The results show that the forecasts at the junctions only deviate around 10% at a maximum from the original observations and seem to indicate that the proposed strategy is one of the effective approaches to predict the real-time traffic flow level in a congested network especially at the locations where no continuous data collection takes place. © 2009 ASCE.|
|Source Title:||Journal of Transportation Engineering|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 9, 2019
WEB OF SCIENCETM
checked on Jan 1, 2019
checked on Dec 28, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.