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In this paper, we present a game-theoretic modeling framework for studyingthe travel behavior in mobility systems, by incorporating prospect theory. Aspart of our motivation, we conducted an experiment in a scaled smart city toinvestigate the frequency of errors in actual and perceived probabilities of ahighway route under free flow conditions. Based on these findings, we provide agame that captures how travelers distribute their traffic flows in atransportation network with splittable traffic, utilizing the Bureau of PublicRoads function to establish the relationship between traffic flow and traveltime cost. Given the inherent non-linearities, we propose a smoothapproximation function that helps us estimate the prospect-theoretic costfunctions. As part of our analysis, we characterize the best-fit parameters andderive an upper bound for the error. We then show a Nash Equilibrium existence.Finally, we present a numerical example and simulations to verify thetheoretical results and demonstrate the effectiveness of our approximation.

Modeling Travel Behavior in Mobility Systems with an Atomic Routing Game and Prospect Theory