Traortation facilities represent an essential infrastructure needed by any society seeking growth and development. Considering its dimensions, development of traortation facilities requires large investments, and this highlights the necessity of planning to solve traortation problems. One of the important tools for planning for traortation is the so-called travel demand matrix. Considering high costs associated with collecting required data for travel demand matrix via direct approaches, a large deal of research has been dedicated to the estimation of this matrix via indirect methods within the recent three decades. One of the methods for estimating the travel demand matrix indirectly is to use counted arcs traffic volume information across the traortation network. Often times, lack of available data makes these methods end up with multiple travel demand matrices, so that complementary data (e.g. data in an old travel demand matrix) is frequently used to estimate the travel demand matrix. Suburban traortation networks are among uncongested networks which have gained smaller deals of attention in terms of planning because of lower number problems arisen across them, so that no comprehensive information exists on their origin-destination travels. In this thesis, a method is presented to estimate travel demand matrix across suburban traortation networks. This method is composed of two stages. In the first stage, a mathematical model of travel demand is built upon the basis of local socioeconomic information along with available information on arcs’ traffic volume across the network. Then, the model is used to assess an initial travel demand matrix. In the second stage, a limited number of arcs are selected, by a statistical method, for counting, and using these arcs’ traffic volume information, the initial travel demand matrix is improved. The mathematical model of travel demand which is selected to assess the initial travel demand matrix is a gravity model. Results indicate that the aforementioned model can well estimate the initial (base) travel demand matrix. Furthermore, a Bayesian network-based statistical method is used to select optimum arcs for counting, using whose traffic volume information the initial travel demand matrix is improved, with the results presented in terms of a case study in this thesis.