On Transport Monitoring and Forecasting During COVID-19 Pandemic in Rome

Abstract

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.

Investigating the Effects of Automated Vehicles on Large Urban Road Networks: Some Evidence from Rome

“…Aiming at testing the presented easy-to-use assignment model to capture the effects and impacts of AVs on large urban networks, a system of models able to reproduce the morning peak hour of a typical workday has been implemented. It has been developed starting from the model calibrated by the Rome Transport Agency for its strategic planning studies, including those carried out during the COVID-19 pandemic [23][24][25]. On the demand side, the O-D matrix consists of about 352,000 trips among 499 traffic zones, in which the study area (i.e., the metropolitan area of Rome) has been divided.…”

A Bus Network Design Model under Demand Variation: A Case Study of the Management of Rome’s Bus Network

“…These matrices were estimated in previous studies to support decision-makers in defining actions for a safe restart of activities in the post-COVID-19 period in Rome [46]. They were estimated using multi-step demand models based on a system of generation, distribution and modal choice models, considering four different trip purposes and four mode alternatives.…”

Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece

“…Regarding public transit forecasting, numerous models have been proposed in literature that, depending on their scope and methods, can be divided into long-term, and short- to medium-term models. Long-term models usually include in their modelling various socio-economic factors ( Brinchi et al, 2020 ), recreating analytically the multidimensional relationships between them and transport ridership. A recent example includes Brinchi et al (2020) who monitored and described the impact of SARS‑CoV‑2 on the mobility trends in Rome, developed future socioeconomics scenarios and run simulations using a variant of the four-step travel model.…”