More and more smart-media applications (apps) are social-interactive oriented, allowing users to communicate, exchange information, and learn from each other. In this regard, traffic apps, as a kind of social app, facilitate cooperative navigation in which drivers transmit and receive traffic information. This relatively recent development is expected to have an impact on travel behaviour and mobility of users, where this impact will evidently depend on aspects such as the amount of usage (penetration rates) and users’ response (acceptance, compliance). In this paper, we propose a model framework in order to simulate and evaluate such systems. To this end, a Stackelberg game-theoretic approach is chosen. Here, equipped drivers benefit from accurate traffic information and (within certain bounds) cooperate to achieve minimal personal and societal costs, while non-equipped drivers base their decisions solely on minimising their personal costs (with possibly limited information on the prevailing traffic conditions). The resulting bi-level optimization problem is then applied to a case study and solved efficiently with the Augmented Lagrangian method. (Author/publisher)
Abstract