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Until recently, urban cities have faced an increasing demand for an efficient system able to help drivers to discover the congested roads and avoid the long queues. In this paper, an Intelligent Traffic Congestion Prediction System (ITCPS) was developed to predict traffic congestion states in roads. The system embeds a Neural Network architecture able to handle the variation of traffic changes. It takes into account various traffic patterns in urban regions as well as highways during workdays and free-days. The developed system provides drivers with the fastest path and the estimated travel time to reach their destination. The performance of the developed system was tested using a big and real-world Global Positioning System (GPS) database gathered from vehicles circulating in Sfax city urban areas, Tunisia as well as the highways linking Sfax and other Tunisian cities. The results of congestion and travel time prediction provided by our system show promise when compared to other non-parametric techniques. Moreover, our model performs well even in cross-regions whose data were not used during training phase. [ABSTRACT FROM AUTHOR]

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning.

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning. [ABSTRACT FROM AUTHOR]