Despite traditional four-step Model is the most prominent Model in majority of travel demand analysis, it does not represent the potential correlations within different travel dimensions. As a result, some researches have suggested the use of choice Modelling instead. However, most of them have represented travel dimensions individually rather than jointly. This research aims to fill this gap through employing the Generalized Nested Logit Model for jointly representing three major travel dimensions; destination, departure time and travel mode. The suggested research methodology depends mainly on agglomerating alternatives that have similar error term’ s variances within specific gaps under common nests without any imposed restrictions. Moreover, different variance gaps lead to overlapped nesting system which can enable analysers Modelling inner and inter-correlation. The proposed approach has been examined through Modelling individuals’ choices among the main shopping destinations in Eskisehir city, Turkey. In the light of estimation results, the proposed Model attains a relatively good over-all goodness of fit which reflects a more prominent predictability power. Moreover, individuals in Eskisehir have been found perceiving more interest to the cost rather than time. From another hand, a behaviour of trading-off between performing such trips at peak periods by using transit or making them at off-peak by private car has been detected.

Global Krylov subspace methods are the most e cient and robust methods to solve Generalized coupled Sylvester matrix equation. In this paper, we propose Nested splitting conjugate gradient process for solving this equation. This method has inner and outer iterations, which employs the Generalized conjugate gradient method as inner iteration to approximate each outer iterate, while each outer iteration is induced by a convergence and symmetric positive defi nite splitting of the coe cient matrices. Convergence properties of this method are investigated. Finally, the e ectiveness of the Nested splitting conjugate gradient method is explained by some numerical examples.

in the era of information, data which are worthwhile asset of human, organizations and enterprises have become such sophisticated that the conventional approaches and methods are not usable anymore, or not efficient at least. Such complexity which is known as the Big Data problem is the affordable extraction of value from big data sets that we are encountered in many recent applications e.g., e-business, scientific research, monitoring, search engines, social networking, etc.. Big Data complexities are instantiated by three major dimensions, high Volume, high Variety, and high Velocity (a.k.a.3Vs). The first and most essential step in data management (also for Big Data management) is designing and employing a proper data Model, as the footstone of the other data management activities such as R& D of DB languages, DBMSs, tools, methods, algorithms, etc.. In this paper, a proper data Model for Big Data is designed and proposed in which the properties required for Big Data problem (i.e., to be integrated, complete, scalable, flexible, compatible, and efficient) are considered. As a data Model, data representation is designed and implicit integrity constraints are presented for the proposed HNG (Hyper Nested Graph) data Model. Experimental evaluation results show that the proposed data Model outperforms other currently used data Models such as the document-based, graph document-based, and graph- based data Models in terms of response time.