Scipediacontent: Scipediacontent moved page Draft Content 485696228 to Zhou et al 2018b

2021-02-03T14:52:25Z

Scipediacontent moved page Draft Content 485696228 to Zhou et al 2018b

Scipediacontent: Created page with " == Abstract == Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on tra..."

2021-02-03T14:52:22Z

Created page with " == Abstract == Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on tra..."

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== Abstract ==

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.

== Original document ==

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

* [http://arxiv.org/abs/1709.09585 http://arxiv.org/abs/1709.09585]

* [http://arxiv.org/pdf/1709.09585 http://arxiv.org/pdf/1709.09585]

* [http://dx.doi.org/10.1109/ijcnn.2018.8489600 http://dx.doi.org/10.1109/ijcnn.2018.8489600]

* [http://xplorestaging.ieee.org/ielx7/8465565/8488986/08489600.pdf?arnumber=8489600 http://xplorestaging.ieee.org/ielx7/8465565/8488986/08489600.pdf?arnumber=8489600],
: [http://dx.doi.org/10.1109/ijcnn.2018.8489600 http://dx.doi.org/10.1109/ijcnn.2018.8489600]

* [https://dblp.uni-trier.de/db/journals/corr/corr1709.html#abs-1709-09585 https://dblp.uni-trier.de/db/journals/corr/corr1709.html#abs-1709-09585],
: [https://arxiv.org/abs/1709.09585 https://arxiv.org/abs/1709.09585],
: [https://arxiv.org/pdf/1709.09585.pdf https://arxiv.org/pdf/1709.09585.pdf],
: [https://ieeexplore.ieee.org/document/8489600 https://ieeexplore.ieee.org/document/8489600],
: [https://ui.adsabs.harvard.edu/abs/2017arXiv170909585C/abstract https://ui.adsabs.harvard.edu/abs/2017arXiv170909585C/abstract],
: [https://doi.org/10.1109/IJCNN.2018.8489600 https://doi.org/10.1109/IJCNN.2018.8489600],
: [https://academic.microsoft.com/#/detail/2963440544 https://academic.microsoft.com/#/detail/2963440544]

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Zhou et al 2018b - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 485696228 to Zhou et al 2018b

Scipediacontent: Created page with " == Abstract == Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on tra..."