PhD Fair Incentive Mechanisms for Sustainable and Human-centric Mobility Systems

In the past decades, mobility systems have been facing ever-increasing challenges in terms of users' dissatisfaction, road congestion and environmental pollution. In this context, the advent of vehicle connectivity and autonomy, powertrain electrification, and micromobility vehicles is providing us with unprecedented opportunities to address the aforementioned challenges. Specifically, these technologies enable centralized intermodal routing approaches that can potentially improve the travel experience (e.g., in terms of travel time and comfort), whilst reducing the costs and emissions incurred by operating the mobility system. However, to achieve this, some users must sacrifice part of their welfare for the sake of other users so that, overall, everyone is better off. This contrasts with the status quo, whereby each user behaves selfishly, and the system reaches a less efficient user-centric equilibrium.

Against this backdrop, this project will study fair incentive schemes to influence and steer human users towards the system optimum. With a similar scope, previous works have proposed monetary tolls. Yet money-based approaches are discriminating and inherently lack fairness. To overcome this limitation, this project will investigate the application of alternative approaches such as artificial currencies that cannot be bought nor exchanged but only paid or gained when travelling (see [1] for preliminary results), and other influential mechanisms. To cope with the complexity and uncertainty stemming from human behavior, the candidate will devise learning-based methods to adapt the mechanisms with the goal to steer the users—or at least 'nudge' them—towards the system optimum, whilst guaranteeing robustness and fairness with respect to diverse users' demand and preferences and environmental impact. A crucial part of the project will be the characterization of the societal costs defining the system optima, and of the different users' profiles and their interaction with the available modes of transportation. To account for this human-centric perspective in a mathematically tractable fashion, the candidate will leverage transdisciplinary collaborations within and outside of TU/e.