Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4943258 | Expert Systems with Applications | 2017 | 16 Pages |
Abstract
We also provide a broad review of the literature and illustrate the complex dependencies at intersections and discuss the issues of data broadcasted by road network sensors. The lowest prediction error was observed for TRU-VAR, which outperforms ARIMA in all cases and the equivalent univariate predictors in almost all cases for both datasets. We conclude that forecasting accuracy is heavily influenced by the TDAM, which should be tailored specifically for each dataset and network type. Further improvements are possible based on including additional data in the model, such as readings from different metrics.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Florin Schimbinschi, Luis Moreira-Matias, Vinh Xuan Nguyen, James Bailey,