Real-Time Saturation Flow Rate Estimation Using Connected Vehicle Data

Conventional Adaptive Traffic Signal Control (ATSC) systems typically use data from fixed traffic sensors (usually Inductive Loop Detectors (ILD)) to estimate the traffic flow parameters required for their signal timing optimization algorithms. These parameters usually include traffic demand, approach speed, turning movement counts or ratios, and saturation flow rate. Obtaining these parameters using fixed point detectors requires multiple detectors on each approach, increasing the deployment, operating, and maintenance costs of these systems. Connected Vehicle (CV) technology enables individual connected vehicles to wirelessly broadcast their real-time travel data, which can be used in ATSC systems and to potentially reduce their dependency on fixed point detectors. This paper presents an enhanced method for estimating lane saturation flow rates in real-time using CV data. The methodology has been evaluated using microsimulation on a multilane signalized arterial corridor for a range of approach geometries, market penetration rates of connected vehicles, and traffic demands. Regression analysis showed that there is a non-linear relationship between the market penetration rate of connected vehicles and the mean absolute relative error of the estimated saturation flow rate using the proposed methodology at each level of service. In general, estimation error increases as level of service (LOS) becomes worse and decreases as the CV level of market penetration (LMP) rate increases. The maximum error was observed for LOS F and LMP = 10% but was only 6%. These results suggest that the methodology is likely sufficiently accurate for practical applications across a wide range of geometries, traffic conditions, and CV LMPs.