Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing
We put forward architecture of a framework for integration of data from moving objects related to urban transportation network. Most of this research refers to the GPS outdoor geolocation technology and uses distributed cloud infrastructure with big data NoSQL database. A network of intelligent mobi...
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Online Access: | http://dx.doi.org/10.1155/2015/578601 |
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doaj-327b9b832f1d4ea1a37cc6f49ee626ca2020-11-24T23:40:45ZengHindawi LimitedApplied Computational Intelligence and Soft Computing1687-97241687-97322015-01-01201510.1155/2015/578601578601Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns WarehousingA. Boulmakoul0L. Karim1M. Mandar2A. Idri3A. Daissaoui4FSTM, Department of Computer Sciences, LIM/IDS Lab, Faculty of Sciences and Technologies of Mohammedia, BP 146, Mohammedia, MoroccoFSTM, Department of Computer Sciences, LIM/IDS Lab, Faculty of Sciences and Technologies of Mohammedia, BP 146, Mohammedia, MoroccoFSTM, Department of Computer Sciences, LIM/IDS Lab, Faculty of Sciences and Technologies of Mohammedia, BP 146, Mohammedia, MoroccoFSTM, Department of Computer Sciences, LIM/IDS Lab, Faculty of Sciences and Technologies of Mohammedia, BP 146, Mohammedia, MoroccoEMSI, 217 Boulevard Bir Anzarane, Casablanca, MoroccoWe put forward architecture of a framework for integration of data from moving objects related to urban transportation network. Most of this research refers to the GPS outdoor geolocation technology and uses distributed cloud infrastructure with big data NoSQL database. A network of intelligent mobile sensors, distributed on urban network, produces congestion traffic patterns. Congestion predictions are based on extended simulation model. This model provides traffic indicators calculations, which fuse with the GPS data for allowing estimation of traffic states across the whole network. The discovery process of congestion patterns uses semantic trajectories metamodel given in our previous works. The challenge of the proposed solution is to store patterns of traffic, which aims to ensure the surveillance and intelligent real-time control network to reduce congestion and avoid its consequences. The fusion of real-time data from GPS-enabled smartphones integrated with those provided by existing traffic systems improves traffic congestion knowledge, as well as generating new information for a soft operational control and providing intelligent added value for transportation systems deployment.http://dx.doi.org/10.1155/2015/578601 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
A. Boulmakoul L. Karim M. Mandar A. Idri A. Daissaoui |
spellingShingle |
A. Boulmakoul L. Karim M. Mandar A. Idri A. Daissaoui Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing Applied Computational Intelligence and Soft Computing |
author_facet |
A. Boulmakoul L. Karim M. Mandar A. Idri A. Daissaoui |
author_sort |
A. Boulmakoul |
title |
Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing |
title_short |
Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing |
title_full |
Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing |
title_fullStr |
Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing |
title_full_unstemmed |
Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing |
title_sort |
towards scalable distributed framework for urban congestion traffic patterns warehousing |
publisher |
Hindawi Limited |
series |
Applied Computational Intelligence and Soft Computing |
issn |
1687-9724 1687-9732 |
publishDate |
2015-01-01 |
description |
We put forward architecture of a framework for integration of data from moving objects related to urban transportation network. Most of this research refers to the GPS outdoor geolocation technology and uses distributed cloud infrastructure with big data NoSQL database. A network of intelligent mobile sensors, distributed on urban network, produces congestion traffic patterns. Congestion predictions are based on extended simulation model. This model provides traffic indicators calculations, which fuse with the GPS data for allowing estimation of traffic states across the whole network. The discovery process of congestion patterns uses semantic trajectories metamodel given in our previous works. The challenge of the proposed solution is to store patterns of traffic, which aims to ensure the surveillance and intelligent real-time control network to reduce congestion and avoid its consequences. The fusion of real-time data from GPS-enabled smartphones integrated with those provided by existing traffic systems improves traffic congestion knowledge, as well as generating new information for a soft operational control and providing intelligent added value for transportation systems deployment. |
url |
http://dx.doi.org/10.1155/2015/578601 |
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