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

SP surveys to estimate Airport Shuttle demand in an Urban Air Mobility context

Electric vertical take-off and landing vehicles (eVTOL) are expected to be the key drivers for Urban Air Mobility (UAM) scenarios, by satisfying on-demand air travel needs in the short or mid-term. Despite the high number of eVTOL prototypes, nowadays only few studies have focused on UAM travel demand estimation, in particular Airport Shuttle demand estimation. The aim of this work is to use Stated Preference methods to collect data necessary to understand the main features of the potential UAM Airport Shuttle trip demand, also by calibrating some discrete choice models. Data were collected by both on-line surveys and face-to-face interviews, which captured mainly the Italian context. Three different Multinominal Logit models and a Mixed Logit model have been calibrated in order to identify the main variables driving people's choices for Airport Shuttle services. The results show the positive impact of income, air travel frequency and shared ride in increasing the willingness to use Airport Shuttle services. On the other hand, user that still prefers the ground transportation modes to reach the airport, high ratio between the number of cars and the driving licenses per family unit and the lack of experience with autonomous systems (i.e., driving assistance systems) seem to have a negative impact on people intention to use Airport Shuttle services

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

Urban mobility and transportation

Urban areas are developing quickly, innovative technologies grant enlarged scope for mobility management. According to literature, 50% of world population and as much as 75% of EU population live in cities, where the majority of GDP is generated. CO2 is responsible of 75% GHG worldwide and transportation is worth around 20% of this share and the contribution is rising, in particular in urban areas. Besides pollution and noise, also collisions (70% of which in urban areas) and congestion - which is worth around 1% of EU GDP in terms of time lost due to delay suffered - are negative externalities. Finally, due to urban sprawl induced by car-centric cultural regimen under the justification of cheaper land costs, the need to travel has been growing notwithstanding economic downturns, resulting in an increased threat of social exclusion for those who cannot afford a car. The attitude towards urban transportation has shifted from laissez-faire to deep concern: as far as EU is concerned, the Action plan on Urban Mobility (2009) recommended the adoption of Sustainable Urban Mobility Plans (SUMPs), the 2011 White Paper envisaged SUMPs to become mandatory for cities over 100,000 inhabitants and a base requisite to access to EU Funds. The 2013 Guidelines and the 2015 EC Urban Mobility Package have further established the SUMP policy. In 2015, UN adopted the “Agenda for sustainable development 2030” (7 out of 17 objectives deal with transportation) and a new worldwide agreement on climate has been signed in Paris. Finally, the funding foreseen by EU research project H2020 (8,2% of the total budget allocated on transportation) will further encourage the investigation of new strategies and technologies. SUMPs emphasize long term vision, the active involvement of citizen and stakeholders (Priester et al., 2014), the setting of targets, measures and a radical reform of regulatory and funding framework to avoid start-and-stop approach (Hickman et al., 2013; Stephenson et al., 2018). Nevertheless, the commitment level is different: developing countries would rather urge to build more and modern infrastructures, leaving the environment as a secondary priority. SUMPs are expected to find solution to road congestion and policy fragmentation between documents (Baidan, 2016). According to EU CIVITAS project’s outcomes, the implementation of SUMPs can be hindered by pro-car & infrastructure building lobbyism, inefficient planning - monitoring – dissemination, lack of stakeholder involvement and support, excessive outsourcing, fluctuation of political commitment over time (Ibeas et al., 2011; Persia et al., 2016), inadequate coordination among policy tiers and plans (Stephenson et al., 2018), unsupportive or inappropriate regulation and financial structures, poor or missing data and reliance to business-as-usual scenarios. The topics facing less acceptance have been accessibility, logistic, traffic control, cycling and walking measures (Bruhova Foltynova & Jordova, 2014)

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