Benchmarking LLM-Based Time-Series Foundation Models for Minute-Resolution Turning-Movement Traffic Dynamics on an Urban Arterial

Abstract

Large language models (LLMs) and time-series foundation models have advanced traffic forecasting, but almost exclusively on freeway sensors at five-minute, aggregate-flow resolution; their behavior on minute-resolution, movement-level counts at signalized arterials is unknown. This study assembled a real corridor dataset—6449 gap-free one-minute turning-movement records (255,364 counted vehicles) at 20 signalized intersections along 13 miles of Nolensville Pike, Nashville, Tennessee—and ran a controlled benchmark across three model families (classical, deep, and LLM/foundation models). We evaluate in-sample accuracy at 5/10/15 min horizons (RQ1), cross-date leave-intersection-out transfer to morning-only sites (RQ2). On the full-day sites, a per-series ARIMA attains the best short-horizon accuracy (MASE 0.87 at h = 5), while a zero-shot foundation model (Chronos) is the most stable across horizons; under leave-intersection-out transfer the ordering reverses—foundation models transfer best (MASE ≈ 0.61, improving to ≈0.56 with few-shot adaptation) whereas globally trained deep networks fail catastrophically (MASE > 4). All benchmark values reported here are measured on the corridor data; foundation-model families that we could not execute end-to-end in this environment are discussed qualitatively and are not included in the quantitative comparison. The dataset and protocol establish an honest, cost-aware basis for judging whether language and foundation models genuinely forecast an urban corridor at minute resolution.