A Rolling-Horizon Lane-Level Queue Prediction System: Algorithm Validation and Field Deployment in a Cooperative Intelligent Transport System (C-ITS) Testbed

Lane selection and lane-change decisions by connected and autonomous vehicles (CAVs) require queue-length information at lane resolution, which corridor-level estimation methods do not provide. This paper presents the Lane-by-lane Queue Information Prediction System (LQS), which predicts per-lane queue length from upstream movement-traffic volume and signal-phase data by combining a platoon dispersion model with a shockwave-theory queue model on a rolling-horizon scheme. We describe its integration into a live C-ITS stack through an SAE J2735 message extension and its deployment at a testbed in Hwaseong, Republic of Korea, where processing latency was measured (313.6 ms average) but field accuracy was not. In an independent microsimulation testbed (Goyang, Republic of Korea), with parameters calibrated on signal cycles 6–15 and evaluated on held-out cycles 16–21, the mean cycle-maximum relative error on the four signal-controlled lanes is 24.5% (17.1–30.0%), against 33.2–52.8% for baseline methods; the right-turn lane, which discharges on red, is reported separately (51.5%). Because cycle-maximum and full-series errors rank lanes differently, we also report on RMSE normalized by mean queue, bias, and correlation, which reconcile the two metrics. An ablation shows that the two empirically tuned parameters are coupled.