In mobile communication systems, mobility modeling is involved in several aspects such as signaling and traffic load analysis. In particular, accurate mobility models are essential for the evaluation of the system design alternatives and network implementation cost issues. In this paper, we propose a mobility model that is appropriate for practical analysis of the mobile systems' design issues. Our model provides different levels of details for the user mobility behavior and can be adapted to any environmental topology. This model considers direction and velocity of the user mobility. The motion direction is modeled based on real street patterns and is described by discrete state Gauss–Markov technique whose parameters are calculated by combining the information derived by the topological map and wireless network performance report (WNPR) of the simulation area. In the proposed method, the user velocity is assumed to be the average velocity that is found based on traffic condition and type of the street. The computational complexity of our model is to the extent that can be used not only in mobility management test procedures but also in order to calculate some useful parameters of these procedures. In this paper, we apply real data to validate the proposed model by simulation which is used to confirm the accuracy of the proposed model and its analytical analysis. Copyright © 2008 John Wiley & Sons, Ltd.

In mobile communications, mobility modeling is involved in several aspects such as signaling and traffic load analysis. In particular, the accuracy of mobility models become essential for the evaluation of system design alternatives and network implementation cost issues. In this paper, we propose a mobility model which is appropriate for the practical analysis of the full range of mobile communications design issues. The model provides different levels of details for the user mobility behavior and can be adapted to any environment topology. This random direction based model can be applied in analytical purpose. This model is described by motion direction and user velocity. The motion direction of users is related to real street patterns and is modeled by discrete time and discrete state Gauss Markov. The velocity of users is modeled as Gaussian with variable mean and variance with respect to traffic factor. Simulation results confirm the preferences of the proposed method in both simulation and analytical estimations.

In this paper we propose a new vehicle mobility modeling method in which many important real world environment issues such as topologies and traffic conditions have been considered to model the user movement in a real environment. The proposed model is based on model in [7] by modification in it's velocity control system. The proposed velocity control system can consider many of the real environment condition for vehicle velocity control on the street. This model can be used in mobile communication and intelligent transport system.

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In mobile communications, mobility modeling is involved in several aspects such as signaling and traffic load analysis. In particular, accuracy of mobility models become essential for the evaluation of system design alternatives and network implementation cost issues. In thispaper wepropose a new method in which many important issues such as streets topology and traffic conditions are considered to model the user movement in a real environment. By using this model we also propose a new intelligent algorithm based on a sub-optimum path finding method tofind a properpath between source and destination. Theproposed model is suitablefor modification for random waypoint model and can be applied in mobile network simulation and intelligent transport system control.