  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.
