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 a adaptive optimization of traffic assignment, provided that classical theories of traffic user equilibrium pose the problem as one of global optimization. We then s...

An analytical model is developed to assign regional freight across a multimodal highway and railway network using geographic information systems. As part of the regional planning process, the model is an iterative procedure that assigns multimodal freight traffic to the network to achieve overall network optimum. The procedure integrates optimization programming with traffic assignment, conside...

Traditionally, traffic assignment allocates trips to links in a traffic network. Nowadays it is also useful to recommend routes. Here, it is interesting to recommend routes that are as close as possible to the system optimum, while also considering the user equilibrium. To compute an approximation of such an assignment, we use a hybrid approach in which an optimization process based on an evolu...