ULSTM: Multi-Scale and Full-Level Temporal Consistency for Traffic Anomaly Detection

Traffic-Prior-Guided State-Aware Framework for Robust Urban Traffic Anomaly Detection

Urban traffic systems are increasingly vulnerable to non-recurrent congestion and abnormal traffic fluctuations, posing significant challenges to intelligent traffic management and resilient transportation operations. Existing traffic anomaly detection methods often struggle to simultaneously characterize heterogeneous anomaly patterns under dynamically evolving traffic states, while severe class imbalance and limited data plausibility further constrain detection reliability. To address these challenges, this study proposes a traffic-prior-guided state-aware framework for robust urban traffic anomaly detection. A Multi-Scale Natural Neighborhood (MS-NaN) module transforms one-dimensional traffic flow sequences into a nine-dimensional representation integrating sequence dynamics, multiscale statistical deviations, and spatiotemporal phase characteristics, thereby embedding traffic state priors into the detection process. Building upon these representations, the Dual-Branch Context-Gated Network (DB-CGNet) separately captures instantaneous traffic disruptions and trend-evolving congestion patterns. An adaptive context-aware gated fusion mechanism then combines the branch features to enhance robustness under complex and non-stationary traffic conditions. To improve evaluation realism, high-fidelity baseline traffic data are generated through B-spline smoothing and first-order autoregressive residual modeling, and anomaly patterns are constructed under Highway Capacity Manual (HCM)-constrained capacity reduction mechanisms. Experiments conducted on a 91-day urban expressway dataset demonstrate that the proposed method achieves the best overall performance among eight benchmark models under a 72 min observation window, attaining an F1-score of 0.7757 and an area under the receiver operating characteristic curve (AUC) of 0.9112. Ablation studies further reveal the critical role of traffic prior features in detecting short-duration evolving anomalies. The proposed framework provides a robust and interpretable solution for intelligent urban traffic monitoring, anomaly warning, and resilient traffic operation management.

Unsupervised Video Anomaly Detection Based on Graph Attention Propagation and Semantic Information

Video Anomaly Detection (VAD) is a crucial computer vision task for security monitoring and public safety. Unsupervised VAD is more suitable for real-world scenarios with rare unknown anomalies, but existing LLM-based methods suffer from limited temporal modeling, inconsistent video understand ing and inaccurate fine-grained localization, leading to biased anomaly scoring. To solve these problems, we propose a novel unsupervised VAD framework fus ing graph attention propagation and multimodal semantic information: first, fuse video semantic and motion features to construct a dynamic spatiotemporal graph, and refine node features via graph attention propagation with orthogonal con straints; then, split videos into semantically coherent event units by a statistical boundary detection module; finally, guide MLLMs to generate event semantic descriptions and initial anomaly scores through a hierarchical prompting strategy, and refine the scores via video-text semantic alignment to obtain accurate frame level scores. Evaluated on UCF-Crime and XD-Violence datasets with frame level AUC, the proposed framework achieves state-of-the-art performance under unsupervised and zero-shot settings, significantly outperforming existing LLM based VAD methods and even several weakly supervised approaches, which fully verifies its effectiveness and robustness.

MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction

In traffic accident risk prediction, most studies overlook the extra noise that could be incorporated when fusing temporal features into spatial features, and some models struggle to capture global correlations among spatial regions. To address these challenges, we propose a novel traffic accident risk prediction framework named MambaLSTM. First, we develop a squeeze-and-excitation temporal feature fusion module to integrate temporal information without compromising spatio-temporal integrity. Second, we introduce a new patch embedding module for effectively capturing semantic relationships among spatially adjacent regions. Additionally, we introduce a Mamba block based on state-space models to model global spatial semantics in urban regions. Finally, we propose a MambaLSTM unit to efficiently capture long- and short-term temporal dependencies for identifying dynamic risk patterns. Extensive experiments on real-world datasets demonstrate the proposed model's superiority over state-of-the-art methods. The code is released at https://github.com/Zhenzovo/MambaLSTM.

Mgstfn: multi-granularity spatio-temporal-frequency network for traffic flow forecasting

Adaptive Anomaly-Residual Early Warning System for Proactive Detection of Urban Traffic Congestion

Urban traffic congestion remains a major challenge for metropolitan transportation systems because it increases travel delay, fuel consumption, and environmental pollution. Conventional monitoring systems are mostly reactive and report congestion after traffic breakdown has already occurred. This paper proposes a lightweight Adaptive Anomaly-Residual Early Warning System (AAR-EWS) for proactive congestion detection. The framework integrates a neighbor-aware Temporal Convolutional Network (N-TCN), residual-based anomaly scoring, segment-wise adaptive thresholding, uncertainty-gated alert logic, and propagation-aware neighborhood confirmation. Experiments on the METR-LA benchmark show that the proposed method reduces 15-minute MAE by 6.3% compared with the TCN baseline, provides an average congestion lead time of 13.6 minutes, and reduces false alerts by more than 35% compared with threshold-based warning rules. The revised study further clarifies model configuration, operational assumptions, robustness under noisy and missing data, deployment feasibility, and comparison with graph neural network approaches.

Multi-Perspective Spatio-Temporal Feature Fusion Model for Urban Traffic Flow Prediction

Urban traffic flow is difficult to forecast accurately because its evolution is non-linear and governed by dependencies that operate over different spatial and temporal ranges. This paper introduces the Multi-Perspective Spatio-Temporal Feature Fusion Model (MPSTFFM) to describe these dependencies through complementary views. The temporal signal is separated into a slowly varying trend and a residual fluctuation, while the spatial structure is represented by four graphs: first-order adjacency, second-order in-degree, second-order out-degree, and a data-adaptive graph. These graphs respectively encode physical road connectivity, common inflow sources, common outflow destinations, and latent spatial associations. Whereas the first three are constructed from the known network topology, the adaptive graph is learned together with the prediction model and can therefore identify correlations not expressed by physical links. Within each spatio-temporal view, self-attention captures dependencies over long ranges, and convolutional operations extract local patterns. The features learned from all views are subsequently fused into a high-dimensional representation used to predict future flow. Experiments on real-world datasets compare MPSTFFM with twelve methods published during the preceding five years. On these benchmarks MPSTFFM outperforms every baseline, lowering the average MAE, RMSE, and MAPE across the four datasets by 13.04%, 5.28%, and 9.59%, respectively, relative to the best baseline on each one.