Sentence examples for classifications of traffic from inspiring English sources

There are three main classifications of traffic models according to the approach used in the analysis.

Automatic detection and classification of traffic signs is an important task in smart and autonomous cars.

Convolutional Neural Networks has shown a great success in classification of traffic signs and they have surpassed human performance on a challenging dataset called the German Traffic Sign Benchmark.

Open image in new window Fig. 7 Classification of traffic zones by number of attracted trips.

Another classification of traffic models is defined by their statistical point of view.

Three different methods are applied, respectively, for classification of traffic conditions using linear regression.

Open image in new window Fig. 10 Classification of traffic conditions.

The last and the most important classification of traffic models is the level of detail in terms of traffic flow, which are microscopic, mesoscopic, and macroscopic simulations.

The following section provides a classification of traffic sensor technologies based on their operating principles and the traffic metrics they can potentially collect.

M. Creusen et al. 2010; Jahangiri and Rakha 2014; Xie et al. 2009), neural networks, and cascaded classifiers trained with some type of boosting (Balali and Golparvar-Fard 2015a; Overett et al. 2014; Pettersson et al. 2008) are used for classification of traffic signs.

Crucially, cross-classification succeeded in V2 and V3 in two out of three combinations, i.e., training the classifier for the pair "traffic 1" versus "people 1" lead to successful classification of "traffic 2" versus "people 2," and training the classifier for the pair "traffic 2" versus "people 2" lead to successful classification of "airplane" versus "kids".