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Information, communication and transport networks have always been in a state of flux, while they also influence each other. Extensive research efforts have been made to investigate the dynamics in the structure and use of networks, e.g., by means of network geometries, small-world effects and scale-free phenomena. We will illustrate these new developments on the basis of airline network evolution. The present paper provides a new contribution to the analysis of topological properties of complex airline networks. Using Lufthansa's networks as an example, this paper aims to show the empirical relevance of various network indicators - such as connectivity and concentration - for understanding changing patterns in airline network configurations. After an extensive discussion of various statistical results, a decision-aid method, viz. multi-criteria analysis, is used to investigate the robustness of our findings. The results highlight the actual strategic choices made by Lufthansa for its own network, as well in combination with its partners in Star Alliance.

This special issue explores the dynamic relationships between transport network design and agents' preferences by outlining the relevance of the spatial components. The seven papers in this volume focus on both changes in the spatial network connectivity structure and changes in consumer preferences/choices due to policies at different spatial levels. The goal is to provide a prospective review on new methodological directions in analyzing transport networks, spatial choices and their related policy implications within the framework of recent European policy interventions and transportation research developments.

The value of travel time savings (VTTS) has been investigated by transport economists since the mid‐1960s. Although the first two decades of research were mainly devoted to VTTS in passenger transport, since the mid‐1980s several studies have analysed the VTTS in freight transport from both a theoretical and an empirical viewpoint. The first part of the paper is devoted to a review of the proposed theoretical models for the value of freight travel time savings (VFTTS), and it discusses the issues arising from the use of different methods to collect empirical VFTTS estimates. The second part is based on a meta‐analysis of a sample of empirical VFTTS observations related to several European and North American countries. It is shown that both the per‐capita gross domestic product and the transport mode are statistically significant variables that can partially explain the variability in VFTTS estimates.

The paper aims to analyse and compare synthetic indices of accessibility in order to measure the impact of high‐speed trains on European cities. In particular, it considers the data and results concerning two previous articles by Gutierrez et al. and Martin et al. that analyse the accessibility gains produced by the construction of Trans‐European Transport Networks (TEN‐Ts). In these two papers, accessibility is studied using, first, a partial approach with three different indicators: location, potential market, and daily accessibility; and, second, data envelopment analysis (DEA) to calculate a synthetic index of global accessibility. The present paper extends these studies by applying principal component analysis (PCA) to obtain a new synthetic accessibility index. DEA and PCA are two good candidates to synthesize the partial information provided by different methods that have been employed in the previous literature that studies accessibility. It is shown that DEA and PCA methodologies yield a...

In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. Neural networks are modern statistical tools based on learning algorithms that are able to process large amounts of data. Neural networks are enjoying increasing interest in several fields because of their effectiveness in handling complex data sets when the functional relationship between dependent and independent variables is not specified explicitly. The present paper compares two NN methodologies. First, it uses NNs to forecast regional employment in both the former West and East Germany. Each model implemented computes single estimates of employment growth rates for each German district, with a 2-year forecasting range. Next, additional forecasts are computed, by combining the NN methodology with shift-share analysis (SSA). Since SSA aims to identify variations observed among the labour districts, its results are used as further explanatory variables in the NN models. The data set used in our experiments consists of a panel of 439 German (NUTS 3) districts. Because of differences in the size and time horizons of the data, the forecasts for West and East Germany are computed separately. The out-of-sample forecasting ability of the models is evaluated by means of several appropriate statistical indicators.