On the estimation of connected vehicle penetration rate based on single-source connected vehicle data

Abstract

With more connected vehicles (CVs) in the networks, the big data era leads to the availability of abundant data from CVs. CV penetration rate is the fundamental building block of tremendous applications, such as traffic data estimation, CV-based adaptive signal control and origin-destination estimation. While CV penetration rate is a random variable unknown in nature, the current estimation method of penetration rate mainly relies on two sources of data —detector and CV data. Penetration rate across the link is computed as CV flow divided by all traffic flow over a certain period of time. However, the current method is constrained by availability and quality of detector data. This paper proposes a simple, analytical, non-parametric, and most importantly, unbiased single-source data penetration rate (SSDPR) estimation method for estimating penetration rate solely based on CV data. It subtly and simultaneously fuses two estimation mechanisms—(1) the measurement of the probability of the first vehicle in a queue being a CV and (2) the direct estimation of the penetration rate of a sample queue—to constitute a single estimator to handle all the possible sample queue patterns. Applicability of the proposed method is not confined to a specific arrival pattern. It solely utilizes the number of the observed CVs and the number of vehicles before the last observed CV in a sample queue. Combining with bridging the queue algorithm, the proposed SSDPR estimation method is extended to overflow or oversaturated conditions. Simulation results show that the proposed method is able to accurately estimate penetration rate as low as 0.1% for all the situations considered. To illustrate the applicability of the proposed method, a case study of fundamental diagram estimation of a link without being installed with any detector is presented.