Abstract
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.
Abstract (translated)
准确检测交通异常对于有效的城市交通管理和缓解拥堵至关重要。我们使用结合了图神经网络和长短期记忆网络的时空生成对抗网络（STGAN）框架来捕捉交通数据中的复杂空间和时间依赖关系。我们将STGAN应用于瑞典哥德堡42个交通摄像头收集的真实时钟、分钟级观测数据，这些数据于2020年数月内采集。通过处理图像计算出代表车辆密度的流量指标作为模型输入。训练在2020年4月至11月的数据上进行，验证则使用了2020年11月14日至23日的独立数据集。我们的结果显示，该模型能够以高精度和低假阳性率有效地检测交通异常。所检测到的异常包括摄像头信号中断、视觉伪影以及影响车流的极端天气状况。
URL
https://arxiv.org/abs/2502.01391
