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As mobile phones advance in functionality and capability, they are increasingly being used as instruments for personal monitoring. Applications are being developed that take advantage of the sensing capabilities of mobile phones - many have accelerometers, location capabilities, imagers, and microphones - to infer contextual information. We focus on one type of context, the transportation mode of an individual, with the goal of creating a convenient (no requirement to place sensors externally or have specific position/orientation settings) classification system that uses a mobile phone with a GPS receiver and an accelerometer sensor to determine if an individual is stationary, walking, running, biking, or in motorized transport. The target application for this transportation mode inference involves assessing the hazard exposure and environmental impact of an individual's travel patterns. Our prototype classification system consisting of a decision tree followed by a first-order hidden Markov model achieves the application requirement of having accuracy level greater than 90% when testing with our dataset consisting of twenty hours of data collected across six individuals.