Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle traffic state estimation and prediction based on limited CV measurements is presented. Furthermore, the proposed formulation enables fusion of CV with other data sources and their integration in any UTC system. The developed Extended Observer (EO) is a discrete-time, variable-dimension implementation of the Extended Kalman Filter. It does not require loop detectors and is independent of the type of signal control. The evaluation focuses on the queue length estimation. Especially in oversaturation, the EO outperforms the CV measurements for all examined penetration rates. Moreover, the highest benefit is observed for the lowest penetration rates. The results show that for oversaturation and low penetration rates the EO improves the CV measurements 13-31%. In addition, the EO is tested with an adaptive UTC system by feeding the fused estimation from CV and camera measurements. The error in queue length estimation from the EO is significantly lower than the error based on stochastic arrivals. Additionally, the results show a reduction in delays at the examined signal and the complete intersection. Overall, this paper sheds light on the potential benefits from enhancing limited CV measurements in order to contribute immediately to current UTC systems.    «

Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle traffic state estimation and prediction based on limited CV measurements is presented. Further...    »