Exploring a Local Linear Model Tree Approach to Car‐Following

Because car‐following (CF) models are fundamental to replicating traffic flow they have received considerable attention over the last 50 years. They are in a continuous state of improvement due to their significant role in traffic microsimulations, intelligent transportation systems, and safety engineering models. This article uses the local linear model tree (LOLIMOT) approach to model driver's CF behavior to incorporate human perceptual imperfections into a CF model. This model defines some localities in the input space. These localities are fuzzy and have overlaps with each other. Specific models for each of the localities are then defined and combined in a fuzzy manner to predict the final output. The model was developed using real world dynamic data sets. Three different data sets were used for training, testing, and validating the model. The performance of the model was compared to a number of existing CF models. The results showed very close agreement between the real data and the LOLIMOT outputs.     

Evolving artificial neural network structure using grammar encoding and colonial competitive algorithm

Generalization performance of support vector machines (SVM) with Gaussian kernel is influenced by its model parameters, both the error penalty parameter and the Gaussian kernel parameter. After researching the characteristics and properties of the parameter simultaneous variation of support vector machines with Gaussian kernel by the parameter analysis table, a new area distribution model is proposed, which consists of optimal straight line, reference point of area boundary, optimal area, transition area, underfitting area, and overfitting area. In order to improve classification performance of support vector machines, a genetic algorithm based on change area search is proposed. Comparison experiments show that the test accuracy of the genetic algorithm based on change area search is better than that of the two-linear search method.