Unraveling pedestrian mobility on a road network using ICTs data during great tourist events

Tourist flows in historical cities are continuously growing in a globalized world and adequate governance processes, politics and tools are necessary in order to reduce impacts on the urban livability and to guarantee the preservation of cultural heritage. The ICTs offer the possibility of collecting large amount of data that can point out and quantify some statistical and dynamic properties of human mobility emerging from the individual behavior and referring to a whole road network. In this paper we analyze a new dataset that has been collected by the Italian mobile phone company TIM, which contains the GPS positions of a relevant sample of mobile devices when they actively connected to the cell phone network. Our aim is to propose innovative tools allowing to study properties of pedestrian mobility on the whole road network. Venice is a paradigmatic example for the impact of tourist flows on the resident life quality and on the preservation of cultural heritage. The GPS data provide anonymized georeferenced information on the displacements of the devices. After a filtering procedure, we develop specific algorithms able to reconstruct the daily mobility paths on the whole Venice road network. The statistical analysis of the mobility paths suggests the existence of a travel time budget for the mobility and points out the role of the rest times in the empirical relation between the mobility time and the corresponding path length. We succeed to highlight two connected mobility subnetworks extracted from the whole road network, that are able to explain the majority of the observed mobility. Our approach shows the existence of characteristic mobility paths in Venice for the tourists and for the residents. Moreover the data analysis highlights the different mobility features of the considered case studies and it allows to detect the mobility paths associated to different points of interest. Finally we have disaggregated the Italian and foreigner categories to study their different mobility behaviors

How future surgery will benefit from SARS-COV-2-related measures: a SPIGC survey conveying the perspective of Italian surgeons

COVID-19 negatively affected surgical activity, but the potential benefits resulting from adopted measures remain unclear. The aim of this study was to evaluate the change in surgical activity and potential benefit from COVID-19 measures in perspective of Italian surgeons on behalf of SPIGC. A nationwide online survey on surgical practice before, during, and after COVID-19 pandemic was conducted in March-April 2022 (NCT:05323851). Effects of COVID-19 hospital-related measures on surgical patients' management and personal professional development across surgical specialties were explored. Data on demographics, pre-operative/peri-operative/post-operative management, and professional development were collected. Outcomes were matched with the corresponding volume. Four hundred and seventy-three respondents were included in final analysis across 14 surgical specialties. Since SARS-CoV-2 pandemic, application of telematic consultations (4.1% vs. 21.6%; p < 0.0001) and diagnostic evaluations (16.4% vs. 42.2%; p < 0.0001) increased. Elective surgical activities significantly reduced and surgeons opted more frequently for conservative management with a possible indication for elective (26.3% vs. 35.7%; p < 0.0001) or urgent (20.4% vs. 38.5%; p < 0.0001) surgery. All new COVID-related measures are perceived to be maintained in the future. Surgeons' personal education online increased from 12.6% (pre-COVID) to 86.6% (post-COVID; p < 0.0001). Online educational activities are considered a beneficial effect from COVID pandemic (56.4%). COVID-19 had a great impact on surgical specialties, with significant reduction of operation volume. However, some forced changes turned out to be benefits. Isolation measures pushed the use of telemedicine and telemetric devices for outpatient practice and favored communication for educational purposes and surgeon-patient/family communication. From the Italian surgeons' perspective, COVID-related measures will continue to influence future surgical clinical practice

Rome vehicle accidents analysis and city riskiness prediction by Artificial Intelligence

Rome municipality publishes geo-referenced vehicle accidents reports and detailed road traffic counters that, in conjunction with Open Street Map roads details, enable deep analyses of the tragic phenomenon. This study focuses on the full year 2021, during which a total of 62,081 vehicles casualties were registered by Rome traffic authorities. The broad Rome territory, overcoming a bunch of Italian chief towns, the wide and capillary set of arteries and the municipality roads extension greater with respect to many European capitals, enhance the statistical relevance of the analyzed data. Artificial intelligence method takes into account three potential accidents predictors as the level of road traffic, the event timing, and the driving complexity of the different zones. The proposed method has the capability of linking time/traffic/complexity indexes of a specific zone of a city with the accident riskiness, moreover, is applicable to whatever urban complex scenario and takes care of the result format in order to facilitate road safety decision makers in smoothing the tragic sequence of accidents. Further possible improvements directions are discussed

Individual mobility deep insight using mobile phones data

Abstract The data sets provided by Information and Communication Technologies have been extensively used to study the human mobility in the framework of complex systems. The possibility of detecting the behavior of individuals performing the urban mobility may offer the possibility of understanding how to realize a transition to a sustainable mobility in future smart cities. The Statistical Physics approach considers the statistical distributions of human mobility to discover universal features. Under this point of view the power laws distributions has been extensively studied to propose model of human mobility. In this paper we show that using a GPS data set containing the displacements of mobile devices in an area around the city Rimini (Italy), it is possible to reconstruct a sample of mobility paths and to study the statistical properties of urban mobility. Applying a fuzzy c-means clustering algorithm, we succeed to detect different mobility types that highlight the multilayer structure of the road network. The disaggregation into homogeneous mobility classes explains the power law distributions for the path lengths and the travel times as an overlapping of exponential distributions, that are consistent with a maximum entropy Principle. Under this point of view it is not possible to infer other dynamical properties on the individual mobility, except for the average values of the different classes. We also study the role of the mobility types, when one restricts the analysis to the an origin-destination framework, by analyzing the daily evolution of the mobility flows