Scipediacontent: Scipediacontent moved page Draft Content 204707293 to Parchami et al 2017a

2021-02-01T20:52:59Z

Scipediacontent moved page Draft Content 204707293 to Parchami et al 2017a

Scipediacontent: Created page with " == Abstract == Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffi..."

2021-02-01T20:52:54Z

Created page with " == Abstract == Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffi..."

New page

== Abstract ==

Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or incidents can be beneficial to such systems to effectively manage and direct the traffic to the most appropriate detours. Many of the current traffic flow prediction systems are designed by utilizing a central processing component where the prediction is carried out through aggregation of the information gathered from all measuring stations. However, centralized systems are not scalable and fail provide real-time feedback to the system whereas in a decentralized scheme, each node is responsible to predict its own short-term congestion based on the local current measurements in neighboring nodes. We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in real-time based on the congestion state of the neighboring stations. Moreover, historical data from the deployment site is not required, which makes the proposed method more suitable for newly installed stations. In order to achieve higher performance, we introduce a regularized Euclidean loss function that favors high congestion samples over low congestion samples to avoid the impact of the unbalanced training dataset. A novel dataset for this purpose is designed based on the traffic data obtained from traffic control stations in northern California. Extensive experiments conducted on the designed benchmark reflect a successful congestion prediction.

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1703.01006 http://arxiv.org/abs/1703.01006]

* [http://arxiv.org/pdf/1703.01006 http://arxiv.org/pdf/1703.01006]

* [http://xplorestaging.ieee.org/ielx7/7958416/7965814/07966128.pdf?arnumber=7966128 http://xplorestaging.ieee.org/ielx7/7958416/7965814/07966128.pdf?arnumber=7966128],
: [http://dx.doi.org/10.1109/ijcnn.2017.7966128 http://dx.doi.org/10.1109/ijcnn.2017.7966128]

* [https://dblp.uni-trier.de/db/journals/corr/corr1703.html#FouladgarPEG17 https://dblp.uni-trier.de/db/journals/corr/corr1703.html#FouladgarPEG17],
: [https://ieeexplore.ieee.org/document/7966128 https://ieeexplore.ieee.org/document/7966128],
: [https://arxiv.org/abs/1703.01006 https://arxiv.org/abs/1703.01006],
: [https://ui.adsabs.harvard.edu/abs/2017arXiv170301006F/abstract https://ui.adsabs.harvard.edu/abs/2017arXiv170301006F/abstract],
: [https://arxiv.org/pdf/1703.01006v1 https://arxiv.org/pdf/1703.01006v1],
: [https://doi.org/10.1109/IJCNN.2017.7966128 https://doi.org/10.1109/IJCNN.2017.7966128],
: [https://academic.microsoft.com/#/detail/2604472983 https://academic.microsoft.com/#/detail/2604472983]

← Older revision	Revision as of 20:52, 1 February 2021
(No difference)

Parchami et al 2017a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 204707293 to Parchami et al 2017a

Scipediacontent: Created page with " == Abstract == Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffi..."