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.

Spatial Generalized linear mixed Models are used for Modeling geostatistical discrete spatial responses and spatial correlation of the data is considered via latent variables. The most important interest in these Models is estimation of the parameters and prediction of the latent variables. In this paper, first, a prediction method is presented. Then a Bayesian approach and MCMC algorithms are proposed. Since these Models are complicated and Monte Carlo sampling is used in the Bayesian inference of these Models, computation time is long. In order to resolve this problem, the Approximate Bayesian methods are considered. Finally, the proposed methods are applied to a case study on rainfall data observed in the weather stations of Semnan in 1391.

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.

Road crashes and their consequences are one of the most important problems that affect people's lives. In order to reduce the fatalities and related costs of crashes, traffic safety researchers are continuously investigating approaches to reduce the occurrence and consequences of crashes. Crash-type Modeling is one of the most common tools for road safety goals in transportation facilities, and the purpose of crash-type Modeling is to establish a relationship between the frequency of crashes based on its type and other effective variables. One of the advantages of crash-type Models is that with the help of these Models, it is possible to identify the places where there is a possibility of a certain type of crashes and to examine the effect of different variables on different types of crashes. In this research, using the data of freeway crashes in Iran, the type of crash was identified with a new approach called the Nested logit Model. To this aim, crashes were initially divided into two categories of single-vehicle and multi-vehicle crashes, and then single-vehicle crashes were divided into three categories of collision with a fixed object, run-off road crashes, and overturning crashes, and multi-vehicle crashes were divided into two categories of collision with a vehicle and multi-vehicle collision crashes. Then the effect of different variables of environment, road, driver, and causes of crashes with different types of crashes were investigated and the effect of significant variables on each type of crash was explored with marginal effect.

In contrast to the classical discrete choice experiment, the respondent in a rank-order discrete choice experiment, is asked to rank a number of alternatives instead of the preferred one. In this paper, we study the information matrix of a rank order Nested multinomial logit Model (RO.NMNL) and introduce local D-optimality criterion, then we obtain Locally D-optimal design for RO.NMNL Models in the discrete choice experiment.