Paolo Cintia

A Gravity Model for Speed Estimation over Road Network

The availability of inexpensive tracking devices, such as GPS-enabled devices, gives the opportunity to collect large amounts of trajectory data from vehicles. In this context, we are interested in the problem of generating the traffic information in time-dependent networks using this kind of data. This problem is not trivial since several works in literature use strong assumptions on the error distribution we want to drop, proposing a gravitational model method to compute road segment average speed from trajectory data. Furthermore we show how to generate travel-time functions from the computed average speeds useful for time-dependent networks routing systems. Our approach allows creating an accurate picture of the traffic conditions in time and space. The method we present in this paper tackles all this aspect showing how its performance over a synthetic dataset and a real case.

Discovering and Understanding City Events with Big Data: The Case of Rome

The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists.

Towards a Boosted Route Planner Using Individual Mobility Models

Route planners generally return routes that minimize either the distance covered or the time traveled. However, these routes are rarely considered by people who move in a certain area systematically. Indeed, due to their expertise, they very often prefer different solutions. In this paper we provide an analytic model to study the deviations of the systematic movements from the paths proposed by a route planner. As proxy of human mobility we use real GPS traces and we analyze a set of users which act in Pisa and Florence province. By using appropriate mobility data mining techniques, we extract the GPS systematic movements and we transform them into sequences of road segments. Finally, we calculate the shortest and fastest path from the origini¾źto the destination of each systematic movement and we compare them with the routes mapped on the road network. Our results show that about 30---35i¾ź% of the systematic movements follow the shortest paths, while the others follow routes which are on average 7i¾źkm longer. In addition, we divided the area object of study in cells and we analyzed the deviations in the flows of systematic movements. We found that, these deviations are not only driven by individual mobility behaviors but are a signal of an existing common sense that could be exploited by a route planner.