用生成模型分析实时交通数据，精准识别异常事件。
Learning Traffic Anomalies from Generative Models on Real-Time Observations
- 结合图神经网络与LSTM的STGAN模型捕捉交通时空依赖
- 在42个摄像头数据上实现高精度、低误报的异常检测
- 可识别信号中断、图像伪影及极端天气等真实问题
准确检测交通异常对城市交通管理与拥堵缓解至关重要。本文采用融合图神经网络与长短期记忆网络的时空生成对抗网络（STGAN）框架，捕捉交通数据中的复杂时空依赖关系。模型基于2020年瑞典哥德堡42个交通摄像头的分钟级实时观测数据，持续数月。图像经处理后提取车辆密度流指标作为输入。训练使用2020年4月至11月数据，验证集为11月14日至23日独立数据。结果表明，该模型能有效检测交通异常，具有高精度与低误报率，可识别摄像头信号中断、视觉伪影及影响交通流的极端天气状况。
原文摘要 · Abstract (English)
Accurate detection of traffic anomalies is crucial for effective urban traffic management and congestion mitigation. We use the Spatiotemporal Generative Adversarial Network (STGAN) framework combining Graph Neural Networks and Long Short-Term Memory networks to capture complex spatial and temporal dependencies in traffic data. We apply STGAN to real-time, minute-by-minute observations from 42 traffic cameras across Gothenburg, Sweden, collected over several months in 2020. The images are processed to compute a flow metric representing vehicle density, which serves as input for the model. Training is conducted on data from April to November 2020, and validation is performed on a separate dataset from November 14 to 23, 2020. Our results demonstrate that the model effectively detects traffic anomalies with high precision and low false positive rates. The detected anomalies include camera signal interruptions, visual artifacts, and extreme weather conditions affecting traffic flow.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。