Scipediacontent: Scipediacontent moved page Draft Content 122260136 to Xiang et al 2020a

2021-02-12T13:32:52Z

Scipediacontent moved page Draft Content 122260136 to Xiang et al 2020a

Scipediacontent: Created page with " == Abstract == In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become..."

2021-02-12T13:32:49Z

Created page with " == Abstract == In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become..."

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== Abstract ==

In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_122260136-beopen361-6297-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/a13040084 http://dx.doi.org/10.3390/a13040084] under the license https://creativecommons.org/licenses/by

* [https://www.mdpi.com/1999-4893/13/4/84/pdf https://www.mdpi.com/1999-4893/13/4/84/pdf] under the license http://creativecommons.org/licenses/by/3.0/

* [https://www.mdpi.com/1999-4893/13/4/84 https://www.mdpi.com/1999-4893/13/4/84],
: [https://dblp.uni-trier.de/db/journals/algorithms/algorithms13.html#ZhangLLY20 https://dblp.uni-trier.de/db/journals/algorithms/algorithms13.html#ZhangLLY20],
: [https://academic.microsoft.com/#/detail/3014489385 https://academic.microsoft.com/#/detail/3014489385] under the license cc-by

* [https://www.mdpi.com/1999-4893/13/4/84 https://www.mdpi.com/1999-4893/13/4/84],
: [https://doaj.org/toc/1999-4893 https://doaj.org/toc/1999-4893]

* [https://www.mdpi.com/1999-4893/13/4/84/pdf https://www.mdpi.com/1999-4893/13/4/84/pdf],
: [http://dx.doi.org/10.3390/a13040084 http://dx.doi.org/10.3390/a13040084]

under the license https://creativecommons.org/licenses/by/4.0/

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Xiang et al 2020a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 122260136 to Xiang et al 2020a

Scipediacontent: Created page with " == Abstract == In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become..."