HALT, a human-in-the-loop label acquisition framework for event-based traffic causality discovery, is presented and it is suggested that metric-aware active labeling can make supervised traffic-causality discovery practical under limited expert-labeling budgets.
PEMSB-3V, a public benchmark suite that preserves raw flow, speed, and occupancy measurements from PeMS detectors for flow prediction, and RiskTraf, a model-agnostic risk-extrapolated residual plug-in that consistently improves diverse forecasting backbones and outperforms debiasing and distribution-shift adaptation me...
Urban traffic congestion imposes significant costs on commuters, the local economy, and the environment, with its prediction remaining a challenging problem due to the complex spatiotemporal dynamics of traffic flow. In this paper, we present a congestion prediction approach for short-term forecasting that leverages lo...
Robert-Lucian Kis, Adrian S. Roman, Teri Lenard· 2026 IEEE 4th Conference on...· 0 citations
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 datas...
D. Chimba, Therezia Matongo, Afia Yeboah et al.· Big Data and Cognitive Compu...· 0 citations
This work presents a protocol-aware empirical assessment across three settings: a C-MAPSS degradation-risk proxy, normal-only training for anomalous-sound detection on MIMII, and BDG2 forecasting-residual diagnostics with synthetic target perturbations.
Urban rail passenger flow forecasting, based on large-scale automatic fare collection (AFC) sensor networks, is typically evaluated on ordinary days, yet operators most need accurate predictions during event days. The AFC infrastructure comprises 191 sensor-equipped stations with turnstile transducers that generate 95....
Ahmed Raza, Guang-Jie Liu, Jie Cheng et al.· Italian National Conference...· 0 citations
Diagnosing collective anomalies from urban trajectories is increasingly important for traffic governance, as it reveals what happened, who was involved, and where and when the event occurred. Existing detectors efficiently produce scores or labels, whereas vision--language pipelines provide richer semantics; neither co...
Jiahai Wu, Zhen-Qun Yang, Chen Jason Zhang et al.· 0 citations
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