| DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion | |
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| Author | |
| Abstract |
Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. |
| Year of Publication |
2017
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| Conference Name |
IEEE Big Data
|
| Date Published |
12/2017
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| Publisher |
IEEE
|
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