Adoption dynamics in social networks for green mobility

Scientific scope and objectives

Tomorrow's mobility is being reinvented by the increased usage of less polluting modes of transport, the diffusion of shared means of transportation, and the provision of information to users who must be actors of their mobility. The various mobility or communication technologies require time to be adopted and the full spectrum of their consequences must be understood. In order to analyze the long-term effects of a technology on overall mobility, models with different time scales must be integrated: on the one hand, the daily mobility that can be captured by macroscopic models such, and on the other hand, the gradual adoption of the technology, whose dynamics can be accelerated by the incentives from policy makers.

Adoption dynamics for green or shared means of transportation are also challenging because they involve, together with human choice, physical dynamics and constraints. A new technology, such as hydrogen vehicles, requires a significant investment in terms of infrastructure. How to achieve an optimal deployment in time and space of the infrastructure in view of the uncertainty of the adoption of the technology? In the face of the recurrent discussion about free public transport, how can the overall system be impacted in a sustainable way? Coupling technology adoption models (such as the Bass model) with traffic models is a crucial research issue for understanding the complex systems of multi-modal transport. Taking into account the specificities of suburban areas is also a fundamental issue. The adoption of a technology is also strongly dependent on space and on various socio-economic factors. We will study adoption dynamics that involve various components: an adoption model, possibly game-theoretic in nature in order to readily account for incentives; a social network, where dynamics of social influence takes place; and the dynamics of infrastructural changes and investments. These dynamics involve time-scales in the order of months (or longer) and spatial scales from the neighborhood to the whole country.

Our contribution will have strong focus on the network aspects (transportation networks and, where relevant, social networks), with due consideration to their structure: we will for instance investigate when and where incentives are most effective to deploy (Bini 2022). Considering the PEPR focus on large scales, suitable averaging techniques need to be used for the model to be tailored to the right geographical scale: candidate techniques developed by the team include continuation (Nikitin 2022) and graphon-based models (Vizuete 2020).

Literature

[Nikitin 2022] Denis Nikitin, Carlos Canudas-de-Wit, and Paolo Frasca. A continuation method for large-scale modeling and control: from ODEs to PDE, a round trip. IEEE Transactions on Automatic Control, 67 (10): 5118–5133, 2022 [Vizuete 2020] Renato Vizuete, Paolo Frasca, and Federica Garin. Graphon-based sensitivity analysis of SIS epidemics. IEEE Control Systems Letters, 4 (3), 542–547, 2020 [Bini 2022] Massimo Bini, Paolo Frasca, Chiara Ravazzi, and Fabrizio Dabbene. Graph structure-based heuristics for optimal targeting in social networks. IEEE Transaction on Control of Network Systems, 9 (3): 1189–1201, 2022 [Moyo 2021] T. Moyo, A. Kibangou, and W. Musakwa (2021). Societal context-dependent multimodal transportation network augmentation in Johannesburg, South Africa. PLOS ONE 16(4): e0249014. [Taia Alaoui 2022] F. Taia Alaoui, H. Fourati, A. Kibangou, B. Robu and N. Vuillerme (2022). Urban transportation mode detection from inertial and barometric data in pedestrian mobility. IEEE Sensors Journal 22 (6), 4772 - 4780. [Goulart 2017] J.H.M. de Goulart, A.Y. Kibangou, and G. Favier (2017). Traffic data imputation via tensor completion based on soft thresholding of Tucker core. Transportation Research Part C: Emerging technologies, vol. 85, pp. 348-362 [Canudas2015] C. Canudas de Wit, F. Morbidi, L. Leon Ojeda, A.Y. Kibangou, I. Bellicot, and P. Bellemain (2015). Grenoble traffic lab: An experimental platform for advanced traffic monitoring and forecasting. IEEE Control Systems Magazine, vol. 35, No 3, pp. 23-39, 2015.