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The use of mobile phone data for planning of transport infrastructure has been shown to have great potential in providing a means of analyzing the efficiency of a transportation system and assisting in the formulation of transport models to predict its future use. In this paper we describe how this type of data can be processed and used in order to act as both enablers for traditional transportation analysis models, and provide new ways of estimating travel behavior. Specifically, we propose a technique for describing the travel demand by constructing time sliced origin destination matrices which respect the level of detail available in Call Detail Records (CDR) from mobile phone use. When analyzing large quantities of human mobility traces, the aspects of sensitivity of traces to be analyzed, and the scale at which such analysis can be accounted for is of high importance. The sensitivity implies that identifiable information must not be inferred from the data or any analysis of it. Thus, prompting the importance of maintaining privacy during or post-analysis stages. We aggregate the raw data with the goal to retain relevant information while at the same time discard sensitive user specifics, through site sequence clustering and frequent sequence extraction. These techniques have at least three benefits: data reduction, information mining, and anonymization. Further, the paper reviews the aggregation techniques with regard to privacy in a post-processing step. The approaches presented in the paper for estimation of travel demand and route choices, and the additional privacy analysis, build a comprehensive framework usable in the processing of mobile phone data for transportation planning. The project presented in this paper a part of the D4D-Senegal challenge.