Community structure detection in the evolution of the United States airport network

This is the post-print version of the Article. Copyright © 2013 World Scientific PublishingThis paper investigates community structure in the US Airport Network as it evolved from 1990 to 2010 by looking at six bi-monthly intervals in 1990, 2000 and 2010, using data obtained from the Bureau of Transportation Statistics of the US Department of Transport. The data contained monthly records of origin-destination pairs of domestic airports and the number of passengers carried. The topological properties and the volume of people traveling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, is applied and reveals a picture of the communities within. The patterns of communities plotted for each bi-monthly interval reveal some interesting seasonal variations of passenger flows and airport clusters that do not occupy a single US region. The long-term evolution of the network between those years is explored and found to have consistently improved its stability. The more recent structure of the network (2010) is compared with migration patterns among the four US macro-regions (West, Midwest, Northeast and South) in order to identify possible relationships and the results highlight a clear overlap between US domestic air travel and migration

Space-independent community structure detection in United States air transportation

This article presents an evolution-based model for the US airport network. The topological properties and the volume of people travelling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, reveals a better picture of the communities within. © 2012 IFAC

A Neural Network to Identify Driving Habits and Compute Car-Sharing Users’ Reputation

main question in urban environments is the continuous growth of private mobility with its negative effects such as traffic congestion and pollution. To mitigate them, it is important to promote different forms of mobility among the citizens. Car-sharing systems give users the same flexibility and comfort of private cars but at smaller costs. For this reason, car-sharing has continuously increased its market share although rather slowly. To boost such growth, car-sharing systems needs to increase vehicle fleet, improve company profits and, at the same time, make it more affordable for consumers. In this paper the promotion of car-sharing by reputation is proposed. Neural networks have been used to identify drivers’ habits in using car-sharing vehicles. To verify the effectiveness of the proposed approach, some experiments based on real and simulated data were carried out with promising results

Evaluation of OD trip matrices by traffic counts in transit systems

The paper deals with the Origin/Destination (O/D) trip matrices estimation in transit systems using traffic counts. The methodology allows both the correction and improvement of the global demand level and the updating of the demand model parameters. An application is executed for the sub-regional area of Reggio Calabria (Italy), and the transit network is modelled by both frequency-based and schedule-based approaches. The results concern the estimation of the average level and the temporal distribution of the daily demand for the bus and train sub-systems, the improvement of an initial set of modal split demand parameters