This research is committed to providing methodological guidelines for the simulation of urban land use dynamics using GIS, RS and CA models. Urban-CA modeling experiments have been conducted for a medium-sized city (Shahr-e-Kord) in Iran over a thirty-five year time span. Global transition probabilities obtained from the Markov chain model and Unique Conditions Map were derived from WoE. Local transition probabilities were estimated using infrastructural factors by two different probabilistic empirical methods: the WoE approach, based on Bayesian theory; and logistic regression. The final land use transition rules drove an Urban-CA model, built upon basis of stochastic land use allocation algorithms. These Urban-CA models drive a CA model based on eight cell Moore neighborhoods. The simulation outputs were statistically validated according to a new compound method based on a Multiple Resolution Model (MRM). After achieving simulations for the 1999-2002 and 2002-2006 time periods along the whole time series, forecast simulations were carried out up to 2025 (1404) and for various urban planning scenarios. For all simulation periods, the best results were obtained from a combined Markov chain and logistic regression with 0.5 Gama to derive the transition rules. Different simulation outputs for the case study indicate their possible further applicability for generating simulation of growth trends both for Iranian cities and cities world-wide.