Attention based stack resnet for citywide traffic accident prediction
Z Zhou - 2019 20th IEEE International Conference on Mobile …, 2019 - ieeexplore.ieee.org
2019 20th IEEE International Conference on Mobile Data Management …, 2019•ieeexplore.ieee.org
The fine-grained citywide traffic accident prediction is of great significance for urban traffic
management. Existing approaches mainly apply classic machine learning methods based
on historical accident records. Thus they failed to involve the cross-domain data, which
contains spatial and temporal dependency. Recently, with more cross-domain urban data
available, leveraging the cross-domain data by deep learning algorithms to predict fine-
grained accidents becomes possible, we propose an attention based ResNet framework to …
management. Existing approaches mainly apply classic machine learning methods based
on historical accident records. Thus they failed to involve the cross-domain data, which
contains spatial and temporal dependency. Recently, with more cross-domain urban data
available, leveraging the cross-domain data by deep learning algorithms to predict fine-
grained accidents becomes possible, we propose an attention based ResNet framework to …
The fine-grained citywide traffic accident prediction is of great significance for urban traffic management. Existing approaches mainly apply classic machine learning methods based on historical accident records. Thus they failed to involve the cross-domain data, which contains spatial and temporal dependency. Recently, with more cross-domain urban data available, leveraging the cross-domain data by deep learning algorithms to predict fine-grained accidents becomes possible, we propose an attention based ResNet framework to model the sophisticated correlation between urban data.
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