Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. A common class of approaches relies upon object tracking and trajectory analysis. A key challenge is the ability to effectively handle occlusions among objects and their trajectories. Another challenge is the detection of joint anomalies between multiple moving objects. Recently sparse reconstruction techniques have been used for image classification, and shown to provide excellent robustness to occlusion. This paper proposes a new joint sparsity model for anomaly detection that effectively addresses both the robustness to occlusion and the detection of joint anomalies involving multiple objects. Experimental results on real and synthetic data demonstrate the effectiveness of our approach for both single-object and multi-object anomalies.