DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks
This addresses inefficiencies in manual traffic monitoring for cities, though it is incremental as it builds on prior data-driven solutions with a focus on small datasets.
The paper tackles the problem of detecting abnormal traffic flows in urban surveillance systems by proposing DeepFlow, a Siamese network-based model that achieves an F1 score of 78% in simulations, outperforming existing methods like DTW, GAK, and iForest.
Nowadays, many cities are equipped with surveillance systems and traffic control centers to monitor vehicular traffic for road safety and efficiency. The monitoring process is mostly done manually which is inefficient and expensive. In recent years, several data-driven solutions have been proposed in the literature to automatically analyze traffic flow data using machine learning techniques. However, existing solutions require large and comprehensive datasets for training which are not readily available, thus limiting their application. In this paper, we develop a traffic anomaly detection system, referred to as DeepFlow, based on Siamese neural networks, which are suitable in scenarios where only small datasets are available for training. Our model can detect abnormal traffic flows by analyzing the trajectory data collected from the vehicles in a fleet. To evaluate DeepFlow, we use realistic vehicular traffic simulations in SUMO. Our results show that DeepFlow detects abnormal traffic patterns with an F1 score of 78%, while outperforming other existing approaches including: Dynamic Time Warping (DTW), Global Alignment Kernels (GAK), and iForest.