Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection
This addresses traffic safety and efficiency by enabling faster accident detection for rescue and route planning, but it is incremental as it applies an existing LSTM method to a specific domain.
The paper tackled the problem of automatic traffic accident detection using deep representation of imbalanced spatio-temporal traffic flow data, achieving detection in less than 18 minutes with a true positive rate of 0.71 and a false positive rate of 0.25, outperforming other methods.
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.