We develop and test algorithms for assessing the similarity of a person’s days based on location traces recorded from GPS. An accurate similarity measure could be used to find anomalous behavior, to cluster similar days, and to predict future travel. We gathered an average of 46 days of GPS traces from 30 volunteer subjects. Each subject was shown random pairs of days and asked to assess their similarity. We tested eight different similarity algorithms in an effort to accurately reproduce our subjects’ assessments, and our statistical tests found two algorithms that performed better than the rest. We also successfully applied one of our similarity algorithms to clustering days using location traces.