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reasons such as degrading infrastructure or changes in the surrounding environment, a well-calibrated SFR could become outdated and it is expensive to recalibrate following traditional methods.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.

中文翻译：

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实时车头时距状态识别和饱和流量估计：隐马尔可夫链模型

饱和流速 (SFR) 表示绿色信号期间的最大可持续流速。SRF的校准不是一劳永逸的问题。由于基础设施退化或周围环境变化等各种原因，校准良好的恒星形成率可能会过时，并且按照传统方法重新校准成本高昂。这份手稿提出了一个模型，可以根据现成的环路检测器数据以实时方式计算饱和流量。该问题被表述为马尔可夫链模型，其目标是识别交通车头状态。总共定义了五个状态和过渡行为。还介绍了给定基础状态的进度分布。SFR 估计被转换为稳定车头的识别。