# FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection

arXiv: https://arxiv.org/abs/2406.15283 · HTML: https://mlsanity.com/paper/2406.15283/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting](https://mlsanity.com/paper/2306.08259/) (2023)
- [Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey](https://mlsanity.com/paper/1901.08292/) (2019)
- [When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos](https://mlsanity.com/paper/2004.03044/) (2020)

## Built on by (most cited first, 8 of 8)

- [TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents](https://mlsanity.com/paper/2407.11477/) (2024)
- [Know Unreported Roadway Incidents in Real-time: Early Traffic Anomaly Detection](https://mlsanity.com/paper/2412.10892/) (2024)
- [Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective](https://mlsanity.com/paper/2503.11963/) (2025)
- [Lane-Wise Highway Anomaly Detection](https://mlsanity.com/paper/2505.02613/) (2025)
- [Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimation](https://mlsanity.com/paper/2512.02920/) (2025)
- [Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction](https://mlsanity.com/paper/2602.02072/) (2026)
- [RAD: Rule-Augmented Relational Anomaly Detection](https://mlsanity.com/paper/2608.23468/) (2026)
- [Bayesian Tensor Autoencoder with Physics-informed Predictive Prior for Multi-dimensional Time Series Anomaly Detection](https://mlsanity.com/paper/2609.31157/) (2026)

## Abstract (arXiv)

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.