Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and errors in event identification and reporting make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Nashville Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75% of crashes. Our dataset and all preprocessing code needed to get started are publicly released at https://vu.edu/ft-aed/ to facilitate future research.

翻译：早期准确检测高速公路上的异常事件（如交通事故）能够改善应急响应与清理效率。然而，现有事件识别与报告流程中的延迟和误差使得该问题难以解决。当前大规模高速公路交通数据集并非为异常检测设计，且忽视了这些挑战。本文首次提出了用于异常检测的大规模车道级高速公路交通数据集。该数据集包含田纳西州纳什维尔方向24号州际公路18英里路段上4条车道连续一个月工作日内的雷达检测传感器数据，总计超过370万条传感器测量记录。我们还从纳什维尔交通管理中心收集了官方事故报告，并手动标注了数据集中所有其他潜在异常。为展示本数据集在未来机器学习和交通研究中的应用潜力，我们在数据集上对多种深度学习异常检测模型进行了基准测试。研究发现，无监督图神经网络自编码器是解决该问题的有效方案，而忽略空间关系会导致性能下降。实验表明，我们的方法在检测75%事故的同时，平均可将报告延迟减少10分钟以上。本数据集及全部预处理代码已公开发布于 https://vu.edu/ft-aed/，以促进后续研究。