This study presents the result of a traffic simulation analysis based on Floating Car Data and a noise emission assessment to show the impact of mobility restriction for COVID-19 containment on urban vehicular traffic and road noise pollution on the road network of Rome, Italy. The adoption of strong and severe measures to contain the spreading of Coronavirus during March-April 2020 generated a significant reduction in private vehicle trips in the city of Rome (-64.6% during the lockdown). Traffic volumes, obtained through a simulation approach, were used as input parameters for a noise emission assessment conducted using the CNOSSOS-EU method, and an overall noise emissions reduction on the entire road network was found, even if its extent varied between road types.

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This paper presents the results of a study on the Rome mobility system aiming at estimating the impacts of the progressive lockdown, imposed by the government, due to the Covid-19 pandemic as well as to support decision makers in planning the transport system for the restart towards a post-Covid “new normal”. The analysis of data obtained by the transport monitoring system has been fundamental for both investigating effects of the lockdown and feeding transport models to predict the impacts on future actions. At first, the paper focuses on the so-called transport analytics, by describing mobility trends for the multimodal transportation system of Rome. Then, the results of the simulated scenarios to design public transport services, able to ensure passengers social distancing required in the first post-Covid months, are presented and discussed.

Service reliability is one of the most important determinants for shifting people to public transport. In low-frequency services, the reliability is considered in terms of punctuality, which becomes significant also from the standpoint of operators, given that punctuality indicators are generally included in business models. As a support for further improvement in bus punctuality in the urban area of Rome, analyses were carried out using automatic vehicle location (AVL) data of a bus line with services mixed with other traffic components and therefore subject to high degrees of travel time variability. The analyses allowed the systematic components of bus dispatching irregularity to be revealed and to investigate to what extent these components are influenced by road congestion, and hence by delayed arrival times at terminals

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