Evolutionary algorithms: Overview and applications to European transport

The present paper aims to analyse the research potential of Evolutionary Algorithms (EAs) in the light of their possible applications in the space-economy. For this purpose the first part of the paper will be devoted to an overview and illustration of EAs, also in comparison with other recent tools emerging form bio-computing, like Neural Networks (NNs). The second part of the paper will then focus on empirical applications concerning analyses and forecasts of European freight transport flows (at a regional level). In this context, the results stemming from an integrated approach combining EAs with NNs will be compared with those from conventional methodologies, like logit models, as well as with the "usual" NN models. We will analyze the sensitivity of various results by using different environmental policy on scenarios on European transport. The empirical experiments highlight the advantages and limitations of these approaches from both a methodological and empirical viewpoint, by offering a plausible range of values of outcomes that may be useful for planners and operators in this field.

Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets

This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical-econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models -based on classes of Neural Network (NN) techniques-that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests

A comparative study on innovation in European cities by means of multicriteria analysis

This paper addresses the issue of innovative behaviour of firms in an urban European context. It aims to identify key factors for innovation at thelocal level, based on micro survey information from firms. In seeking for prominent explanatory variables for entrepreneurial innovation in variousclasses of European cities, a particular multivariate method - i.e., Regime analysis - is employed. This special type of multicriteria method appears to be a fruitful tool for comparative analysis and generates a wide range of interesting empirical results on innovation factors in European cities

Did Zipf Anticipate Socio-Economic Spatial Networks?

An avalanche of empirical studies has addressed the validity of the rank-size rule (or Zipf’s law) in a multi-city context in many countries. City size in most countries seems to obey Zipf’s law, but the question under which conditions (e.g. sample size, spatial scale) this ‘law’ holds remained largely underinvestigated. Another complementary question is whether socio-economic networks in space also show a similar hierarchical pattern. Against this background, the present paper investigates – from a methodological viewpoint – the relationship between network connectivity and the rank-size rule (or Zipf’s law) in an urban-economic network constellation. After a review of the literature, we address in particular the following methodological issues: (i) the (aggregate) behavioural foundation underlying the rank-size rule/Zipf’s law in the light of spatial-economic network theories (e.g. entropy maximization, spatial interaction theory, etc.); (ii) the nature of the analytical relationship between social-spatial network analysis and the rank-size rule/Zipf’s law. We argue that the rank size rule is compatible with conventional economic foundations of spatial network models. Consequently, a spatial-economic interpretation – as well as a network connectivity interpretation – of the rank-size rule coefficient is provided. Our methodological contribution forms the foundation for the subsequent empirical analysis applied to spatial networks in a socio-economic context. The aim here is to test the sensitivity of empirical findings for changes in scale, functional forms, time periods, and network structures. Our application is concerned with an extensive spatio-temporal panel database related to the evolution of urban population in Germany. We test the relevance of the rank-size rule/Zipf’s law, and its evolution over the years, and – in parallel – the related ‘socio-economic’ connectivity in these urban networks. In particular, we will show that Zipf’s law (i.e., with the rank-size coefficient equal to 1) is only valid under particular conditions of the sample size. The paper concludes with some retrospective and prospective remarks

An Application of the Multiple Criteria Decision Making (MCDM) Analysis to the Selection of a New Hub Airport

The paper illustrates the application of three Multiple-Criteria Decision-Making (MCDM)methods to the problem of the selection of a new hub airport for a hypothetical EuropeanUnion (EU) airline assumed to operate within the EU liberalised air transport market. Thethree MCDM methods used are SAW (Simple Additive Weighting), TOPSIS (Technique forOrder Preference by Similarity to the Ideal Solution) and AHP (Analytic Hierarchy Process),and they are applied to a preselected set of alternative airports. The attributes (criteria) aredefined to express the performance of particular alternatives (airports) relevant for aDecision-Maker (DM), in this case the EU airline in question.In addition to illustrating the three methods, this application of three different MCDMmethods is intended to lead to a preliminary judgment about their usefulness assupplementary decision-making tools for eventual practical use. The example in which sevenpreselected European airports are ranked according to nine performance criteria, indicatesthat all three methods, if applied to the same problem and using the same method fordetermining the importance of the different criteria, produce the same result

Editorial

Mobility behaviour (e.g. car driving) is a complex phenomenon, the future of which is fraught with uncertainties and unpredictabilities, as the transport system is influenced by a many different key variables such as type of spatial network, (non-)governmental organisations and institutions, or local regional or national regulatory system. As a consequence, we observe on the one hand rapid growth trends in mobility (in relation to e.g. globalisation factors), but on the other hand also many barriers and impediments (e.g. congestion). Hence, there is a need for a thorough reflection on the various effects (per mode, region/city, spatial network system, policy measure, etc.) of the ever increasing traffic volume. Clearly, the emerging negative externalities (like congestion, pollution, noise annoyance and accidents) play a critical role in this framework. Keeping in mind that the ultimate Kyoto objectives focus inter alia on the achievement of a sustainable transport system, pricing of mobility is a key issue, since it aims to reach allocative efficiency and to raise social welfare within the context of political and social feasibility

Neural Networks for Cross-Sectional Employment Forecasts: A Comparison of Model Specifications for Germany

In this paper, we present a review of various computational experiments – and consequent results – concerning Neural Network (NN) models developed for regional employment forecasting. NNs are widely used in several fields because of their flexible specification structure. Their utilization in studying/predicting economic variables, such as employment or migration, is justified by the ability of NNs of learning from data, in other words, of finding functional relationships – by means of data – among the economic variables under analysis. A series of NN experiments is presented in the paper. Using two data sets on German NUTS 3 districts (326 and 113 labour market districts in the former West and East Germany, respectively), the results emerging from the implementation of various NN models – in order to forecast variations in full-time employment – are provided and discussed In our approach, single forecasts are computed by the models for each district. Different specifications of the NN models are first tested in terms of: (a) explanatory variables; and (b) NN structures. The average statistical results of simulated out-of-sample forecasts on different periods are summarized and commented on. In addition to variable and structure specification, the choice of NN learning parameters and internal functions is also critical to the success of NNs. Comprehensive testing of these parameters is, however, limited in the literature. A sensitivity analysis is therefore carried out and discussed, in order to evaluate different combinations of NN parameters. The paper concludes with methodological and empirical remarks, as well as with suggestions for future research.neural networks, sensitivity analysis, employment forecasts, Germany

Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms

The aim of this paper is to develop and apply Neural Network (NN) models in order to forecast regional employment patterns in Germany. NNs are statistical tools based on learning algorithms with a distribution over a large amount of quantitative data. NNs are increasingly deployed in the social sciences as a useful technique for interpolating data when a clear specification of the functional relationship between dependent and independent variables is not available. In addition to traditional NN models, a further set of NN models will be developed in this paper, incorporating Genetic Algorithm (GA) techniques in order to detect the networks’ structure. GAs are computer-aided optimization tools that imitate natural biological evolution in order to find the solution that best fits the given case. Our experiments employ a data set consisting of a panel of 439 districts distributed over the former West and East Germany,. The West and East data sets have different time horizons, as employment information by district is available from 1987 and 1993 for West and East Germany, respectively. Separate West and East models are tested, before carrying out a unified experiment on the full data set for Germany. The above models are then evaluated by means of several statistical indicators, in order to test their ability to provide out- of-sample forecasts. A comparison between traditional and GAenhanced models is ultimately proposed. The results show that the West and East NN models perform with different degrees of precision, because of the different data sets’ time horizons.forecasting; neural networks; regional labour markets