Table of Contents Author Guidelines Submit a Manuscript
Applied Computational Intelligence and Soft Computing
Volume 2015, Article ID 578601, 12 pages
Research Article

Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing

1FSTM, Department of Computer Sciences, LIM/IDS Lab, Faculty of Sciences and Technologies of Mohammedia, BP 146, Mohammedia, Morocco
2ENSAK, Boulevard Béni Amir, BP 77, Khouribga, Morocco
3ENCG Casablanca, Beau Site, BP 2725, Ain Sebaâ, Casablanca, Morocco
4EMSI, 217 Boulevard Bir Anzarane, Casablanca, Morocco

Received 15 August 2014; Accepted 9 December 2014

Academic Editor: Yongqing Yang

Copyright © 2015 A. Boulmakoul et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.