Dangerous ideas: at the cutting edge of our knowledge

Abstract:

Digital twins are used to understand how mobility works at city scale and how decisions are taken at individual level. Here we will focus first on an example built in Barcelona to study different low emission zones configurations. The work presented here has a twofold objective: first, to demonstrate the capability of mobile phone records to feed activity-based transport models, and, second, to assert the advantages of using activity-based models to estimate the effects of traffic demand management policies. Activity diaries for the metropolitan area of Barcelona are reconstructed from mobile phone records. This information is then employed as input for building a transport model of the city. The model calibration and validation process proves the quality of the activity diaries obtained. The possible impacts of a cordon toll policy applied to two different areas of the city and at different times of the day are then studied. Our results show the way in which the modal share is modified in each of the considered scenarios. The possibility of evaluating the effects of the policy at both aggregated and traveller level, together with the ability of the model to capture policy impacts beyond the cordon toll area confirm the advantages of activity-based models for the evaluation of traffic demand management policies. After this, we will also discuss the limits of these approaches and which tools will be needed to overcome them.

C2S2 Seminars: Dangerous ideas: at the cutting edge of our knowledge

Il Center for Computational Social Sciences and Human Dynamics C2S2, istituito a settembre del 2021 con un accordo tra il Dipartimento di Sociologia e Ricerca Sociale, il Dipartimento di Economia e Management dell’Università di Trento e la Fondazione Bruno Kessler, e composto da un gruppo interdisciplinare di sociologi, informatici, economisti, statistici, psicologi sociali e comportamentali, filosofi, linguisti, umanisti con ambito di ricerca comune la computational social science e la data science.