Evolutionary and Preferential Attachment Models of Demand Growth and Their Use in Transportation Planning

This paper considers a dual time scale transportation planning model for which demand evolves on a day-to-day time scale and traffi c flows on arcs of the transportation network fluctuate on a within-day time scale. We show the problem is particularly amenable to a demand learning approach when using a demand model motivated by evolutionary game theory. We then argue that an alternative model of demand growth based on the paradigm of preferential attachment familiar from network science may be fit into the same mathematical structure and computed with equal effi ciency. Numerical experiments for comparing the impact of these two models of day-to-day demand dynamics are proposed.