Article ID: | iaor2001388 |
Country: | United Kingdom |
Volume: | 7C |
Issue: | 5 |
Start Page Number: | 281 |
End Page Number: | 303 |
Publication Date: | Oct 1999 |
Journal: | Transportation Research. Part C, Emerging Technologies |
Authors: | Peeta Srinivas, Zhou Chao |
Keywords: | traffic flow |
This paper focuses on the off-line stochastic dynamic traffic assignment (DTA) problem as part of a hybrid framework that combines off-line and on-line strategies to solve the on-line DTA problem. The primary concept involves the explicit recognition of stochasticity in origin–destination (O–D) demand and/or network supply conditions to determine a robust off-line a priori solution that serves as the initial solution on-line. This strategy ensures that the computationally intensive components, which exploit historical data, are executed off-line while circumventing the need for very accurate on-line O–D demand forecast models. Thereby, efficient on-line reactive strategies could be used to address unfolding traffic conditions. The paper investigates the robustness of the off-line a priori DTA solution under plausible on-line situations. The results illustrate the superiority of the a priori solution over the currently used mean O–D demand-based solution for on-line route guidance applications.