Real-time headway state identification and saturation flow rate estimation: a hidden Markov Chain model

Saturation flow rate (SFR) denotes the maximum sustainable flow rate during the green signal. Calibration of SRF is not a problem that can be solved once and for all. Due to various reasons such as degrading infrastructure or changes in the surrounding environment, a well-calibrated SFR could become outdated and it is expensive to recalibrate following traditional methods. This manuscript proposes a model to calculate saturation flow rate in a real-time fashion from loop detector-data that is readily available. The problem is formulated as a Markov Chain model with the goal of identifying traffic headway states. A total of five states and the transitional behavior are defined. The distribution of headway given the underlying state is also presented. The SFR estimation is converted to the identification of stable headway. The proposed model is tested and validated, which shows the proposed model is able to generate satisfactory results.