Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways.

摘要

This dissertation presents the vehicle reidentification problem formulated as a lexicographic optimization problem. The lexicographic optimization formulation is a preemptive multi-objective formulation that combines goal programming, classification, and Bayesian analysis techniques. The details of field implementation and data collection design are also presented. The solution of the vehicle reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of specific dynamic origin/destination demands as well as the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems) implementations of the approach using conventional surveillance infrastructure. Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions. A discussion is also presented of the application of section densities in a dynamic origin/destination demand estimation framework as an example of the usefulness of this approach. The use of existing surveillance infrastructure coupled with this approach could allow development of widespread applications in Intelligent Transportation Systems (ITS).