The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-ramps. Real-time queue information is a vital input for dynamic queue management on metered on-ramps. Accurate and reliable queue information enables the management of on-ramp queues in a manner that adapts to the actual traffic queue size and thus minimizes the adverse impacts of queue flush while increasing the benefit of ramp metering. The proposed algorithm is based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. These projection results are updated with the measurement equation by using the time occupancies from midlink and link entrance loop detectors. This study also proposes a novel singular-point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performance and consistently outperformed the benchmarked single-occupancy Kalman filter (SOKF) method. The improvements over the SOKF method were 62% and 63% on average for the estimation accuracy and reliability, respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in conditions of congested ramp traffic, in which long queues may significantly compromise the benchmark algorithm's performance.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering