Real-time Estimation of Queue Length Based on Fused Data Using Connected Vehicle Technology and A Detector

Abstract

This paper proposes an algorithm about real-time estimation of queue length using the data from both connected vehicle (CV) and a detector for both under-saturated and over-saturated situations. None of the penetration ratio, signal timing plan or traffic volume is needed as input, making the model more applicable. The resolution reaches second level, depending on the sampling rate of devices. The detector is placed a certain distance away from the stop line so that vehicle's queuing behavior is more predictable. It greatly improved the accuracy especially when there is few CVs. To make the results more robust and accurate, the upper bound of the queue length is estimated using the data of moving CVs and car following model for the first time. The estimation algorithm is verified by the simulation in VISSIM. The relationship between estimation accuracy and market penetration ratio, traffic volume is also analyzed. Results show that only 10% CVs are needed in under-saturated traffic flow and 30% CVs are needed in over-saturated traffic flow.