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Human mobility modeling for either transportation system development or individual location based services has a tangible impact on people's everyday experience. In recent years cell phone data has received a lot of attention as a promising data source because of the wide coverage, long observation period, and low cost. The challenge in utilizing such data is how to robustly extract people's trip sequences from sparse and noisy cell phone data and endow the extracted trips with semantic meaning, i.e., trip purposes.In this study we reconstruct trip sequences from sparse cell phone records. Next we propose a Bayesian trip purpose classification method and compare it to a Markov random field based trip purpose clustering method, representing scenarios with and without labeled training data respectively. This procedure shows how the cell phone data, despite their coarse granularity and sparsity, can be turned into a low cost, long term, and ubiquitous sensor network for mobility related services.