Outlier detection in vehicle traffic data is a practical problem that has gained traction lately due to an increasing capability to track moving vehicles in city roads. In contrast to other applications, this particular domain includes a very dynamic dimension: time. Many existing algorithms have studied the problem of outlier detection at a single instant in time. This study proposes a method for detecting temporal outliers with an emphasis on historical similarity trends between data points. Outliers are calculated from drastic changes in the trends. Experiments with real world traffic data show that this approach is effective and efficient.