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Automatic detection of traffic accidents has a crucial effect on improvingtransportation, public safety, and path planning. Many lives can be saved bythe consequent decrease in the time between when the accidents occur and whenrescue teams are dispatched, and much travelling time can be saved by notifyingdrivers to select alternative routes. This problem is challenging mainlybecause of the rareness of accidents and spatial heterogeneity of theenvironment. This paper studies deep representation of loop detector data usingLong-Short Term Memory (LSTM) network for automatic detection of freewayaccidents. The LSTM-based framework increases class separability in the encodedfeature space while reducing the dimension of data. Our experiments on realaccident and loop detector data collected from the Twin Cities Metro freewaysof Minnesota demonstrate that deep representation of traffic flow data usingLSTM network has the potential to detect freeway accidents in less than 18minutes with a true positive rate of 0.71 and a false positive rate of 0.25which outperforms other competing methods in the same arrangement.

Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection