This chapter demonstrates an application of agent-based selection dynamics to the traffic assignment problem. We introduce an evolutionary dynamic approach that acquires payoff data from multi-agent reinforcement learning to enable an adaptive optimization of traffic assignment, provided that classical theories of traffic user equilibrium pose the problem as one of global optimization. We then show how these data can be employed to define the conditions for evolutionary stability and Nash equilibria. The validity of this method is demonstrated by studies in traffic network modeling, including an integrated application using geographic information systems applied to a complex road network in the San Francisco Bay area.
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