Dynamically estimating saturation flow rate at signalized intersections: a data-driven technique

Connected Vehicles (CVs) could enhance traffic management systems by providing detailed and real-time information. Theoretically, such information can be exploited for the provision of efficient movement of traffic, especially at intersections identified as the bottlenecks of traffic systems. Aimed at the same purpose, this paper uses information of CVs to estimate the Saturation Flow Rate (SFR), particularly in the transition period during which CVs and conventional vehicles will coexist. To this end, we retain the advantages of data-driven techniques to capture the underlying dynamics of the SFR by considering information of CVs as the only input. In this regard, we correlate the dynamic variations of the SFR to the mutual interactions among the contributing parameters extracted from the limited pieces of CVs' information using a neural network. Comprehensive simulations under precisely designed settings in VISSIM show a hoped-for SFR estimation accuracy level, which can further augment intelligent intersection controller initiatives.