Queue Length Estimation Using Connected Vehicle Technology for Adaptive Signal Control

This paper presents a mathematical model for real-time queue estimation using connected vehicle (CV) technology from wireless sensor networks. The objective is to estimate the queue length for queue-based adaptive signal control. The proposed model can be applied without signal timing, traffic volume, or queue characteristics as basic inputs. The model is also developed so that it can work with both fixed-time signals and actuated signals. Furthermore, a discrete wavelet transform (DWT) is applied to the queue estimation algorithm in this paper for the first time. The purpose of the DWT is to enhance the proposed queue estimation to be more accurate and consistent regardless of the randomness in the penetration ratio. Experimental results are provided to validate the proposed model in both pretimed control and actuated control with a microscopic simulator, i.e., VISSIM. The results indicate that the proposed algorithm is able to estimate the queue length from VISSIM in the test case with pretimed signal control reasonably well. The results in actuated control cases, which have not been studied previously, showed that the proposed algorithm remains as accurate as the pretimed control cases. The accuracy of the proposed queue estimation algorithm is obtained without relying on basic inputs that other models typically require but are often impractical to obtain. Therefore, it is expected that the proposed queue estimation model is applicable for adaptive signal control using CV technology in practice.