Maremoti in bacini limitati

The work of my thesis is focused on the impact of tsunami waves in limited basins. By limited basins I mean here those basins capable of modifying significantly the tsunami signal with respect to the surrounding open sea. Based on this definition, we consider limited basins not only harbours but also straits, channels, seamounts and oceanic shelves. I have considered two different examples, one dealing with the Seychelles Island platform in the Indian Ocean, the second focussing on the Messina Strait and the harbour of the Messina city itself (Italy). The Seychelles platform is differentiated at bathymetric level from the surrounding ocean, with rapid changes from 2 km to 70 meters over short horizontal distances. The study of the platform response to the tsunami propagation is based on the simulation of the mega-event occurred on 26 December 2004. Based on a hypothesis for the earthquake causative fault, the ensuing tsunami has been numerically simulated. I analysed synthetic tide gauge records at several virtual tide gauges aligned along the direction going from the source to the platform. A substantial uniformity of tsunami signals in all calculated open ocean tide-gauge records is observed, while the signals calculated in two points of the Seychelles platform show different features both in terms of amplitude and period of the perturbation. To better understand the content in frequency of different calculated marigrams, a spectral analysis was carried out. In particular the ratio between the calculated tide-gauge records spectrum on the platform and the average tide-gauge records in the open ocean was considered. The main result is that, while in the average spectrum in the open ocean the fundamental peak is related to the source, the platform introduces further peaks linked both to the bathymetric configuration and to coastal geometry. The Messina Strait represents an interesting case because it consists in a sort of a channel open both in the north and in the south and furthermore contains the limited basin of the Messina harbour. In this case the study has been carried out in a different way with respect to the Seychelles case. The basin was forced along a boundary of the computational domain with sinusoidal functions having different periods within the typical tsunami frequencies. The tsunami has been simulated numerically and in particular the tide-gauge records were calculated for every forcing function in different points both externally and internally of the channel and of the Messina harbour. Apart from the tide-gauge records in the source region that almost immediately reach stationarity, all the computed signals in the channel and in the Messina harbour present a transient variable amplitude followed by a stationary part. Based exclusively on this last part, I calculated the amplification curves for each site. I found that the maximum amplification is obtained for forcing periods of approximately 10 minutes.

A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis

An extensive sample (2%) of private vehicles in Italy are equipped with a GPS device that periodically measures their position and dynamical state for insurance purposes. Having access to this type of data allows to develop theoretical and practical applications of great interest: the real-time reconstruction of traffic state in a certain region, the development of accurate models of vehicle dynamics, the study of the cognitive dynamics of drivers. In order for these applications to be possible, we first need to develop the ability to reconstruct the paths taken by vehicles on the road network from the raw GPS data. In fact, these data are affected by positioning errors and they are often very distanced from each other (~2 Km). For these reasons, the task of path identification is not straightforward. This thesis describes the approach we followed to reliably identify vehicle paths from this kind of low-sampling data. The problem of matching data with roads is solved with a bayesian approach of maximum likelihood. While the identification of the path taken between two consecutive GPS measures is performed with a specifically developed optimal routing algorithm, based on A* algorithm. The procedure was applied on an off-line urban data sample and proved to be robust and accurate. Future developments will extend the procedure to real-time execution and nation-wide coverage.

Statistical Physics and Modeling of Human Mobility

In this thesis, we extend some ideas of statistical physics to describe the properties of human mobility. By using a database containing GPS measures of individual paths (position, velocity and covered space at a spatial scale of 2 Km or a time scale of 30 sec), which includes the 2% of the private vehicles in Italy, we succeed in determining some statistical empirical laws pointing out "universal" characteristics of human mobility. Developing simple stochastic models suggesting possible explanations of the empirical observations, we are able to indicate what are the key quantities and cognitive features that are ruling individuals' mobility. To understand the features of individual dynamics, we have studied different aspects of urban mobility from a physical point of view. We discuss the implications of the Benford's law emerging from the distribution of times elapsed between successive trips. We observe how the daily travel-time budget is related with many aspects of the urban environment, and describe how the daily mobility budget is then spent. We link the scaling properties of individual mobility networks to the inhomogeneous average durations of the activities that are performed, and those of the networks describing people's common use of space with the fractional dimension of the urban territory. We study entropy measures of individual mobility patterns, showing that they carry almost the same information of the related mobility networks, but are also influenced by a hierarchy among the activities performed. We discover that Wardrop's principles are violated as drivers have only incomplete information on traffic state and therefore rely on knowledge on the average travel-times. We propose an assimilation model to solve the intrinsic scattering of GPS data on the street network, permitting the real-time reconstruction of traffic state at a urban scale.