This study field-tested two AI-enabled vehicle classification approaches—an inductive loop signature system and a video detection system—to automate commercial truck and broader vehicle volume counts on I‑93 in Concord, New Hampshire. Both technologies achieved over 90% agreement with NHDOT ground‑truth data under free‑flow and congested conditions, though accuracy was affected by occlusion, off‑center vehicles, lane changes, and floating wheels. Additional winter testing is planned to assess performance in severe weather, and results can help state DOTs select cost‑effective tools for automated truck volume and classification data collection.