A multilayered block network model to forecast large dynamic transportation graphs:An application to US air transport

Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution of relevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data. This paper presents a state-of-the-art latent network model to forecast multilayer dynamic graphs that are increasingly common in transportation and proposes a community-based extension to reduce the computational burden. Flexible time series analysis is obtained by modeling the probability of edges between vertices through latent Gaussian processes. The models and Bayesian inference are illustrated on a sample of 10-year data from four major airlines within the US air transportation system. Results show how the estimated latent parameters from the models are related to the airline's connectivity dynamics, and their ability to project the multilayer graph into the future for out-of-sample full network forecasts, while stochastic blockmodeling allows for the identification of relevant communities. Reliable network predictions would allow policy-makers to better understand the dynamics of the transport system, and help in their planning on e.g. route development, or the deployment of new regulations

The role of London airports in providing connectivity for the UK: regional dependence on foreign hubs

In a context of ongoing debate about the future UK aviation policy and its implications for regional economic development, this paper discusses the role of London Heathrow and the South East airports in providing connectivity for the UK, with particular focus on the international markets that originate from regional UK airports. Using an MIDT dataset of worldwide passenger itineraries served by the European airport network during May 2013, we first establish whether London Heathrow can currently be considered the most important hub for the UK, in terms of traffic generation, connectivity, and centrality, while also measuring the dependence of UK regions on foreign airports and airlines to remain connected with the rest of the world. Results show that, despite the competition, London Heathrow benefits from its massive traffic generation to remain the most central gateway for overall UK air transport markets. However, when only regional markets are considered, significant dependence on foreign hubs appears in many destinations, particularly to Asia–Pacific or the BRIC countries where above 80% of passengers use transfer flights. These results fit nicely with the observed trends of seat de-concentration and hub-bypassing in the airline industry. While dependence on foreign hubs can be interpreted as a sign of vulnerability, there is also the argument that bypassing Heathrow allows regional airports to develop new markets and reduce the level of congestion in the London airport system

An assessment of the potential for self-connectivity at European airports in holiday markets

In a context of intense airport and airline competition, a few European airports have recently started offering self-connection services to price-sensitive holiday passengers travelling with a combination of tickets where the airline/s involved do not handle the transfer themselves. This paper provides an exploratory analysis of the potential and implications of self-connectivity for European airports and airlines using a case study of air travel routes to holiday destinations in the Mediterranean. With the help of a forecasting model based on a zero-inflated Poisson regression, we identify the airports and airlines that have the highest potential to facilitate self-connections in the selected markets. The results also explore some implications of the widespread development of self-connection services in Europe

Vulnerability of the European air transport network to major airport closures from the perspective of passenger delays:Ranking the most critical airports

This paper analyzes the vulnerability of the European air transport network to major airport closures from the perspective of the delays imposed to disrupted airline passengers. Using an MIDT dataset on passenger itineraries flown during February 2013, full-day individual closures of the 25 busiest European airports are simulated and disrupted passengers then relocated to minimum-delay itineraries. Aggregate delays are used to rank the criticality of each airport to the network, with the possibility of disaggregating the impact across geographical markets. The results provide useful reference values for the development of policies aimed at improving the resilience of air transport networks

A Bimodal Discrete Shifted Poisson Distribution. A Case Study of Tourists’ Length of Stay

Although the Poisson distribution is appropriate for modelling equi-dispersed distributions, it reflects bimodality less well. In this paper, we propose a distribution which is more suitable for the latter purpose. It can be fitted to both positively and negatively skewed data and appears to represent overdispersion phenomena correctly in count data models obtained using a Poisson distribution. Furthermore, the distribution can be normalised in terms of its mean value, and therefore covariates can be included. Our empirical results are based on tourists’ length of stay in the Canary Islands (Spain), a popular holiday destination. The study analyses data supplied by the Canary Islands Tourist Expenditure Survey. Our findings show that the model presented is valid and that the fit obtained is reasonably good