Adaptive speed estimation using transfer function models for real-time dynamic traffic assignment operation

The application of a transfer function model (TFM) in a real-time dynamic traffic assignment system is investigated. The motivation is to improve the speed estimation method to enable better system consistency with reality in real-time operation. The study is conducted by adopting a TFM derived from actual detector data in San Antonio, Texas. This model is then used in the traffic simulation module of the DYNASMART-X dynamic traffic assignment system to update the network link speeds. A nonlinear least-squares optimization algorithm, implemented for this study, is coupled with DYNASMART-X to enable adaptive estimation of the TFM parameters. Simulation-based experiments are carried out on the Fort Worth test network. These experiments are designed to evaluate the TFM performance and to gain insight into its operational properties under different conditions. The results show that the TFM, both adaptive and nonadaptive, can consistently approximate the true underlying speed-density dynamics. Of significant importance is the transferability and robustness of TFMs in different settings. The outcome of this research substantiates the premise that good speed estimation can be achieved through the use of TFMs.