  Automatic detection of traffic accidents has a crucial effect on improving
transportation, public safety, and path planning. Many lives can be saved by
the consequent decrease in the time between when the accidents occur and when
rescue teams are dispatched, and much travelling time can be saved by notifying
drivers to select alternative routes. This problem is challenging mainly
because of the rareness of accidents and spatial heterogeneity of the
environment. This paper studies deep representation of loop detector data using
Long-Short Term Memory (LSTM) network for automatic detection of freeway
accidents. The LSTM-based framework increases class separability in the encoded
feature space while reducing the dimension of data. Our experiments on real
accident and loop detector data collected from the Twin Cities Metro freeways
of Minnesota demonstrate that deep representation of traffic flow data using
LSTM network has the potential to detect freeway accidents in less than 18
minutes with a true positive rate of 0.71 and a false positive rate of 0.25
which outperforms other competing methods in the same arrangement.
