  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.
