Citation:
Mehrannia, P. , Bagi, S. Shirahmad, Moshiri, B. , & Al-Basir, O. Adam. (2021). Deep Representation of Imbalanced Spatio-temporal Traffic Flow Data for Traffic Accident Detection. arXiv. Retrieved from https://arxiv.org/abs/2108.09506
Abstract:
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.
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.
