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3rd International Conference on Transport Infrastructure and Systems, TIS ROMA 2022 ; 69:727-734, 2022.
Article in English | Scopus | ID: covidwho-2322250


Travel choices in terms of means of transport and frequencies have changed during the recent pandemic period due to mobility restrictions, the growing fear of contagion and, especially in some months, the reduction of public transport capacity during the phases of the pandemic (especially for low demand areas). These trends must be analysed in order to optimize the implementation of possible complementary solutions to fill the deficit of local public transport (TPL) by introducing for example the Demand Responsive Transport services (DRT). A preliminary analysis is useful to identify the most efficient, effective and sustainable solutions in the various contexts, taking into account users and their motivation to travel. A growing need for "on-demand" mobility is linked to the increase in the number of elderly and disabled people. With a lack of alternative services and a reluctance to bear the burden and cost of ownership of vehicles, transport infrastructure will be particularly important to this aging population. Therefore, the improvement of transport services must consider some main characteristics of this modal choice are: being user-oriented;guarantee the accessibility of the service via the web, on specific platforms available on fixed and mobile devices and also enjoy the versatility of use with reference to the areas and users to be served. The present work, therefore, focuses on an evaluation of the literature, defining the main characteristics of DRT in Europe over the last twenty years. The results lay the foundations for a better planning of the service in the post-pandemic phase and a diffusion of bottom-up approaches for the calibration of the service itself through the dissemination of survey campaigns. © 2023 The Authors. Published by ELSEVIER B.V.

International Conference of Computational Methods in Sciences and Engineering 2021, ICCMSE 2021 ; 2611, 2022.
Article in English | Scopus | ID: covidwho-2160434
1st Workshop on Agent-Based Modeling and Policy-Making, AMPM 2021 ; 3182, 2022.
Article in English | Scopus | ID: covidwho-2011339
21st International Conference on Computational Science and Its Applications, ICCSA 2021 ; 12958 LNCS:603-618, 2021.
Article in English | Scopus | ID: covidwho-1446085
21st International Conference on Computational Science and Its Applications, ICCSA 2021 ; 12953 LNCS:699-714, 2021.
Article in English | Scopus | ID: covidwho-1446058
AIP Conf. Proc. ; 2343, 2021.
Article in English | Scopus | ID: covidwho-1177155
Sci Rep ; 11(1): 5304, 2021 03 05.
Article in English | MEDLINE | ID: covidwho-1118815


We propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area and for identifying high-risk areas within a country. Our risk index is evaluated as a function of three different components: the hazard of the disease, the exposure of the area and the vulnerability of its inhabitants. As an application, we discuss the case of COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions by using available historical data on air pollution, human mobility, winter temperature, housing concentration, health care density, population size and age. We find that the epidemic risk is higher in some of the Northern regions with respect to Central and Southern Italy. The corresponding risk index shows correlations with the available official data on the number of infected individuals, patients in intensive care and deceased patients, and can help explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto have suffered much more than the rest of the country. Although the COVID-19 outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at the same time, when the first cases were officially certified at the beginning of 2020, the disease has spread faster and with heavier consequences in regions with higher epidemic risk. Our framework can be extended and tested on other epidemic data, such as those on seasonal flu, and applied to other countries. We also present a policy model connected with our methodology, which might help policy-makers to take informed decisions.

COVID-19/epidemiology , Data Science/methods , Pandemics/prevention & control , COVID-19/prevention & control , COVID-19/transmission , COVID-19/virology , Geography , Health Policy , Humans , Italy/epidemiology , Pandemics/statistics & numerical data , Policy Making , Preventive Medicine/standards , Risk Assessment/methods , Risk Factors , SARS-CoV-2/pathogenicity , Time Factors