Dynamic Traffic Assignment: Genetic Algorithms Approach

Abstract

Real-time route guidance is a promising approach to alleviating congestion on the nation’s highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithms (GAs) is used to solve a dynamic traffic assignment model developed for a real-world routing scenario in Hampton Roads, Virginia. The results of the GA approach are presented and discussed, and the performance of the GA program is compared with an example of commercially available nonlinear programming (NLP) software. Among the main conclusions is that GAs offer tangible advantages when used to solve the dynamic traffic assignment problem. First, GAs allow the relaxation of many of the assumptions that were needed to solve the problem analytically by traditional techniques. GAs can also handle larger problems than some of the commercially available NLP software packages.

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