Analysis of the effects of the first phase of COVID-19 pandemic on mobility choices in Italy by a multi-criteria approach

COVID-19 pandemic has caused changes in logistics and mobility. Concerning Italian road mobility, between March and April 2020, there has been a reduction in traffic for both light vehicles and heavy goods vehicles. Italy was the first European country to implement a total lockdown, starting on March 9th, causing a deep contraction in road traffic. This paper explores the main differences in mobility choices before and after the COVID-19 pandemic in 2020. A multi-criteria decision-making method was selected for the analysis of questionnaire survey data. The fuzzy Analytic Hierarchy Process was applied, considering eight mobility types: bus, tram, taxi, train, shared vehicles, multiple modes, walking and car. An evaluation process was adopted for the modal mobility choices of the residents of Sicily, Italy. The results show a significant decline in mobility demand during the first phase of the pandemic, especially in public transport mode. The findings provide a deeper understanding of the need to implement strategies to respect the constraints generated by the pandemic and revive the penalised transport and mobility-related sectors. Furthermore, the study's findings provide valuable insights for the policymakers, both national and local, about the mobility results of the lockdown and can be used as a forecast benchmark for planning the restrictions in the future, in case of another unexpected phenomenon, e.g., pandemic. © 2022 The Author(s).

Priority Degrees and Distance Measures of Complex Hesitant Fuzzy Sets With Application to Multi-Criteria Decision Making

The notion of a complex hesitant fuzzy set (CHFS) is one of the better tools in order to deal with complex information. Since distance plays a crucial role in order to differentiate between two things or sets, in this paper, we first develop a priority degree for the comparison between complex hesitant fuzzy elements (HFEs). Then a variety of distance measures are developed, namely, Complex hesitant normalized Hamming-Hausdorff distance (CHNHHD), Complex hesitant normalized Euclidean-Hausdorff distance (CHNEHD), Generalized complex hesitant normalized Hausdorff distance (GCHNHD), Complex hesitant hybrid normalized Hamming distance (CHHNHD), Complex hesitant hybrid normalized Euclidean distance (CHHNED), Generalized complex hesitant hybrid normalized distance (GCHHND) and their weighted forms. Moreover, the continuous form of the proposed distances is also developed. Further, the proposed distances are applied to medical diagnosis problems for their effectiveness and application. Furthermore, a multi-criteria decision making (MCDM) approach is developed based on the TOPSIS method and proposed distances. Finally, a practical example related to the effectiveness of COVID-19 tests is presented for the application and validity of the proposed method. A comparison study was also done with the method that was already in place to see how well the new method worked.

Priority Degrees and Distance Measures of Complex Hesitant Fuzzy Sets with Application to Multi-Criteria Decision Making

The notion of a complex hesitant fuzzy set (CHFS) is one of the better tools in order to deal with complex information. Since distance plays a crucial role in order to differentiate between two things or sets, in this paper, we first develop a priority degree for the comparison between complex hesitant fuzzy elements (HFEs). Then a variety of distance measures are developed, namely, Complex hesitant normalized Hamming-Hausdorff distance (CHNHHD), Complex hesitant normalized Euclidean-Hausdorff distance (CHNEHD), Generalized complex hesitant normalized Hausdorff distance (GCHNHD), Complex hesitant hybrid normalized Hamming distance (CHHNHD), Complex hesitant hybrid normalized Euclidean distance (CHHNED), Generalized complex hesitant hybrid normalized distance (GCHHND) and their weighted forms. Moreover, the continuous form of the proposed distances is also developed. Further, the proposed distances are applied to medical diagnosis problems for their effectiveness and application. Furthermore, a multi-criteria decision making (MCDM) approach has been developed based on the TOPSIS method. Finally, a practical example related to the effectiveness of COVID-19 tests is presented for the application and validity of the proposed method. A comparison study was also done with the method that was already in place to see how well the new method worked. Author