Elasticities of Passenger Transport Demand on US Intercity Routes: Impact on Public Policies for Sustainability

Passenger transport is a key sector of the economy, and its sustainability depends on achieving the greatest possible efficiency, avoiding problems of congestion or underuse of infrastructures, and reducing the sector’s environmental impact. Knowing the elasticities of demand is critical to achieving these objectives, estimating the intensity of transport demand, and predicting the effect of different policies on reducing greenhouse gas emissions. This research proposes a relatively simple model for estimating and predicting the elasticity of demand for different modes of transport at the route level. This model could be used by companies and public management to obtain a vision of the different analysed routes and the pressure of their demand, as well as a relative perspective of each of them. Such a model is used to estimate the price and income demand elasticities of passenger transport modes in domestic routes in the United States (2003–2019), where there is competition between road, rail, and air transport. Series of passenger numbers, fares, and budget shares are reconstructed from the available data. A Rotterdam demand model (RDM) is estimated using a seemingly unrelated regression method (SUR). The estimated income elasticities imply that demand for road transport increases somewhat more proportionally than the increase in income, somewhat less than proportionally for air transport, and with very low proportionality for rail transport. This indicates the need to target investment and service improvement efforts, as well as technological solutions, according to this difference in demand pressures. Finally, the demand response of the three modes of transport to price increases is inelastic, and there is little or no pass-through from one mode to another. This implies that fiscal or carbon pricing actions could have a very limited impact and high social costs. Again, strategies based on investments in technological progress, infrastructure development, and normative interventions could be more effective

Evolutionary q-Gaussian radial basis function neural networks for multiclassification

This paper proposes a radial basis function neural network (RBFNN), called the q-Gaussian RBFNN, that reproduces different radial basis functions (RBFs) by means of a real parameter q. The architecture, weights and node topology are learnt through a hybrid algorithm (HA). In order to test the overall performance, an experimental study with sixteen data sets taken from the UCI repository is presented. The q-Gaussian RBFNN was compared to RBFNNs with Gaussian, Cauchy and inverse multiquadratic RBFs in the hidden layer and to other probabilistic classifiers, including different RBFNN design methods, support vector machines (SVMs), a sparse classifier (sparse multinomial logistic regression, SMLR) and a non-sparse classifier (regularized multinomial logistic regression, RMLR). The results show that the q-Gaussian model can be considered very competitive with the other classification methods. © 2011 Elsevier Ltd

A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks

Randomized-based Feedforward Neural Networks approach regression and classification (binary and multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden layer space (randomly generated in its neural network version) for models without direct links and the patterns projected in the hidden layer space along with the original input data for models with direct links. The targets are encoded for the multi-class classification problem according to the 1- of-J encoding (J the number of classes), which implies that the model parameters are estimated to project all the patterns belonging to its corresponding class to one and the remaining to zero. This approach has several drawbacks, which motivated us to propose an alternative optimization model for the framework. In the proposed optimization model, model parameters are estimated for each class so that their patterns are projected to a reference point (also optimized during the process), whereas the remaining patterns (not belonging to that class) are projected as far away as possible from the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four models are then presented: the neural network version of the algorithm and its corresponding kernel version for the neural networks models with and without direct links. In addition, the optimization model has also been implemented in randomization-based multi-layer or deep neural networks. The empirical results obtained by the proposed models were compared to those reported by state-ofthe-art models in the correct classification rate and a separability index (which measures the degree of separability in projection terms per class of the patterns belonging to the class of the others). The proposed methods show very competitive performance in the separability index and prediction accuracy compared to the neural networks version of the comparison methods (with and without direct links). Remarkably, the model provides significantly superior performance in deep models with direct links compared to its deep model counterpart

Una propuesta de optimización multicriterio para la asignación de la ayuda oficial al desarrollo: combinando los intereses de los donantes y de los receptores

It is well known that donors pursue different objectives (altruistic objectives or those based on recipient, and donor interests) in granting their aid. This study proposes an innovative tool that enables a combination of both types of objectives, allowing each donor (bilateral or multilateral) to better understand, optimise and target the distribution of its ODA according to the interests of both parties to the transaction (donor and recipient). This tool uses concentration curves and Suits’ indices to determine an optimal distribution of aid through the development of a constrained optimisation program that encompasses all of its purposes. Furthermore, used at the aggregate level, this tool could facilitate donor coordination to achieve international development goals.Es bien sabido que los donantes persiguen diferentes objetivos al conceder su ayuda oficial al desarrollo (objetivos altruistas, basados en los intereses de los receptores, pero también basados en los suyos propios). Este estudio propone una herramienta que posibilita combinar ambos tipos de objetivos, permitiendo a cada donante (bilateral o multilateral) comprender, optimizar y orientar mejor la distribución de su AOD en función de los intereses de ambas partes de la transacción (donante y receptor). Dicha herramienta utiliza curvas de concentración e índices de Suits para determinar una distribución óptima de la ayuda mediante el desarrollo de un programa de optimización restringido que incluya todos los objetivos perseguidos. Además, utilizada a nivel agregado, esta herramienta podría facilitar la coordinación de los donantes para alcanzar los objetivos internacionales de desarrollo

A multi-criteria optimization proposal for aid allocation: combining donor and recipient interests

It is well known that donors pursue different objectives (altruistic objectives or those based on recipient, and donor interests) in granting their aid. This study proposes an innovative tool that enables a combination of both types of objectives, allowing each donor (bilateral or multilateral) to better understand, optimise and target the distribution of its ODA according to the interests of both parties to the transaction (donor and recipient). This tool uses concentration curves and Suits’ indices to determine an optimal distribution of aid through the development of a constrained optimisation program that encompasses all of its purposes. Furthermore, used at the aggregate level, this tool could facilitate donor coordination to achieve international development goals

Job Stability and Gender Perspectives: Application of a Logistic Regression Model

Several changes have been taking place in the labour market since the 1970s that have created the right climate to spur organisations and workers to demand greater flexibility in employment. In this context, temporary employment has been the focus of many research papers and temporary contracts have been used as a tool to achieve labour flexibility. In order to understand the situation in Andalusia (Spain), this paper aims to identify the decisive factors in permanent employment. To this end, starting hypotheses will be defined about the decisive factors in permanent employment and the positive or negative significance of their influence; the starting hypotheses will then be tested empirically using a logistic regression model on a sample population of wage earners in Andalusia. In the second stage, given that the ratio of temporary contracts is much higher among women, the variable ‘gender’ is likely to be decisive in the construction of the regression model, therefore the decisive factors for permanent employment in Andalusia will be evaluated separately for men and women, in order to calibrate the impact of gender on job stability. Finally, based on the estimated probabilities of having a permanent job depending on gender, the degree of labour discrimination faced by women in the Andalusian labour market will be analysed

A multi-class classification model with parametrized target outputs for randomized-based feedforward neural networks.

Randomized-based Feedforward Neural Networks approach regression and classification (binary and multi-class) problems by minimizing the same optimization problem. Specifically, the model parameters are determined through the ridge regression estimator of the patterns projected in the hidden layer space (randomly generated in its neural network version) for models without direct links and the patterns projected in the hidden layer space along with the original input data for models with direct links. The targets are encoded for the multi-class classification problem according to the 1-of- encoding ( the number of classes), which implies that the model parameters are estimated to project all the patterns belonging to its corresponding class to one and the remaining to zero. This approach has several drawbacks, which motivated us to propose an alternative optimization model for the framework. In the proposed optimization model, model parameters are estimated for each class so that their patterns are projected to a reference point (also optimized during the process), whereas the remaining patterns (not belonging to that class) are projected as far away as possible from the reference point. The final problem is finally presented as a generalized eigenvalue problem. Four models are then presented: the neural network version of the algorithm and its corresponding kernel version for the neural networks models with and without direct links. In addition, the optimization model has also been implemented in randomization-based multi-layer or deep neural networks.Funding for open access charge: Universidad de Málaga / CBU

¿Cómo ven la universidad privada los estudiantes preuniversitarios? Un estudio exploratorio mediante la combinación de redes semánticas naturales y despliegue multidimensional

This research studies the determinant attributes of the construction of the perception of private universities generically without referencing brands using a convenience sample of high school seniors in Andalusia. The methodology proposed is an exploratory research study combining two non-habitual techniques: natural semantic networks, a quantitative technique, and multidimensional unfolding, a scaling quantitative technique. The results point to the presence of two different dimensions: the associations with the expected results from private university (training, employment and language) and the required involvement of the student (effort and exchanges) and differences perceived according to sex and the nature of the student’s center of belonging. In addition, there is evidence of the advantages of combining techniques as a triangulation mechanism. Better comprehension of a research problem is verified by using different analysis procedures about the same phenomenon.Se presenta una investigación en la que se estudian los atributos determinantes en la construcción de imagen de la universidad privada, de forma genérica sin alusiones a marcas, recurriendo a una muestra de conveniencia de preuniversitarios en Andalucía. Como metodología se propone una investigación exploratoria con la combinación de dos técnicas poco habituales: la técnica Redes Semánticas Naturales, y una técnica cuantitativa de escalamiento denominada despliegue multidimensional. Los resultados apuntan hacia la presencia de dos dimensiones diferentes: asociados a los resultados esperados de la universidad privada (formación, empleo e idioma) y según la implicación requerida del discente (esfuerzo e intercambios) y se perciben diferencias según el sexo y la naturaleza del centro de pertenencia del alumnado. Además, se aportan evidencias de las ventajas de la combinación de técnicas como mecanismo de triangulación, pues utilizando procedimientos de análisis diferentes sobre el mismo fenómeno se constata una mejor comprensión del problema de investigación

The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession

The traditional machine-part cell formation problem simultaneously clusters machines and parts in different production cells from a zero–one incidence matrix that describes the existing interactions between the elements. This manuscript explores a novel alternative for the well-known machine-part cell formation problem in which the incidence matrix is composed of non-binary values. The model is presented as multiple-ratio fractional programming with binary variables in quadratic terms. A simple reformulation is also implemented in the manuscript to express the model as a mixed-integer linear programming optimization problem. The performance of the proposed model is shown through two types of empirical experiments. In the first group of experiments, the model is tested with a set of randomized matrices, and its performance is compared to the one obtained with a standard greedy algorithm. These experiments showed that the proposed model achieves higher fitness values in all matrices considered than the greedy algorithm. In the second type of experiment, the optimization model is evaluated with a real-world problem belonging to Human Resource Management. The results obtained were in line with previous findings described in the literature about the case study