The vehicle reidenti®cation problem is the task of matching a vehicle detected at one location with the same vehicle detected at another location from a feasible set of candidate vehicles detected at the other location. This paper formulates and solves the vehicle reidenti®cation problem as a lexicographic optimization problem. Lexicographic optimization is a preemptive multi-objective formulation, and this lexicographic optimization formulation combines lexicographic goal programming, classi®cation, and Bayesian analysis techniques. The solution of the vehicle reidenti®cation problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/ destination demands. Implementation of this approach using conventional surveillance infrastructure permits the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems). Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under dierent trac ¯ow conditions.