  Accident detection is a vital part of traffic safety. Many road users suffer
from traffic accidents, as well as their consequences such as delay,
congestion, air pollution, and so on. In this study, we utilize two advanced
deep learning techniques, Long Short-Term Memory (LSTM) and Gated Recurrent
Units (GRUs), to detect traffic accidents in Chicago. These two techniques are
selected because they are known to perform well with sequential data (i.e.,
time series). The full dataset consists of 241 accident and 6,038 non-accident
cases selected from Chicago expressway, and it includes traffic spatiotemporal
data, weather condition data, and congestion status data. Moreover, because the
dataset is imbalanced (i.e., the dataset contains many more non-accident cases
than accident cases), Synthetic Minority Over-sampling Technique (SMOTE) is
employed. Overall, the two models perform significantly well, both with an Area
Under Curve (AUC) of 0.85. Nonetheless, the GRU model is observed to perform
slightly better than LSTM model with respect to detection rate. The performance
of both models is similar in terms of false alarm rate.
