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Gravity models have been one of the mathematical models of choice for trip distribution modeling efforts during many decades. Their simplicity offset their drawbacks, as they usually provide a reasonably good rationale for how goods are distributed in a transportation network with relatively little information. These gravity models, however, rely on the definition of a deterrence function that acts as a counterweight of the levels of supply and demand. This function is usually picked from a series of off-the-shelf available functions that only depend on a handful of parameters that need to be calibrated. Because of the limited off-the shelf options, gravity models lack flexibility in some occasions. In this paper, we tackle the use of sparse regression techniques that can accommodate data more flexibly with a reduced number of terms. Using interregional freight origin–destination data from Spain, we test two alternatives, namely, best subset regression and lasso regression. We show that the first one performs better in finding parsimonious deterrence functions and we attain gravity models that fit the data up to 14.5% better than classical deterrence functions. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the temporal dependence in energy prices and demand using daily data of Portugal and Spain over the period 2007–2017. The methodology used is based on a stochastic Hidden Markov Model and the results indicate first that all significant relationships between energy prices and demands were found to be positive; second, spot prices are only time dependent on future prices and spot energy, while future energy is solely time dependent on spot energy behavior; third, future prices are not only autocorrelated but also time-dependent with spot energy and future energy demands level; and finally, spot energy is autocorrelated and time-dependent with future prices and future energy. Policy implications of the results obtained are presented at the end of the article. [ABSTRACT FROM AUTHOR]

The decision making of a suitable heating, ventilating and air conditioning system's set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being's thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system. [ABSTRACT FROM AUTHOR]

Multiple scenario cooperative games model situations where the worth of the coalitions is simultaneously valued in several scenarios or under different states of nature. For multiple scenario cost allocation games, we focus on the identification of those allocations which are stable in the sense that agents have no incentives to abandon the group. The stability of an allocation depends on how the quantities assigned to the coalitions are compared with the vector-valued worth of the coalition. Therefore, different extensions of the notion of core emerge, depending on how these comparisons are made. In this paper different notions of core for multiple scenario cost allocation games are studied, and the relationships between them and the existing core solutions are analyzed. We also address the inclusion in the model of partial information about the relative importance of the scenarios which often refers to the probabilities of occurrence. In order to identify allocations which are also stable in the presence of partial information, we extend the notions of core to this new setting, and provide results which permit the calculation of the corresponding sets of allocations. The potential applicability of our analysis is illustrated with the allocation of the total cost of the waste collection service between the different urban areas in the city of Seville. [ABSTRACT FROM AUTHOR]

This paper introduces a new assessment method classification, in which a third procedure, mixed valuation, is jointly included with the traditional economic and non-economic methodologies. The paper considers a case of multiple actors (from a previous work by the same authors-Aznar et al. (Estudios de Economía Aplicada, 25(2):389-409, ), in which a new technique for multicriteria agriculture valuation (MAVAM) was proposed. The method is specifically designed for situations in which scarce information about the elements being compared (quantified or not) is available. It works in individual and group decision making contexts and attempts to both obtain and incorporate the objective information associated with the tangible aspects of the problem and the subjective knowledge associated with the human factor into the valuation process. It combines two of the most extended multicriteria decision making techniques: the Analytic Hierarchy Process (AHP) and Goal Programming (GP). The first of these enables tangible and intangible information stemming from known elements to be collected by using pairwise comparisons; the second allows the scarce information available and the personal approach to the valuation to be included in the valuation process. The proposed methodology is illustrated by means of its application to a case of individual and group valuation of an agricultural asset in the La Ribera district, Valencia (Spain). [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose an integrated planning model to adequate the offered capacity and system frequencies to attend the increased passenger demand and traffic congestion around urban and suburban areas. The railway capacity is studied in line planning, however, these planned frequencies were obtained without accounting for rolling stock flows through the rapid transit network. In order to provide the problem more freedom to decide rolling stock flows and therefore better adjusting these flows to passenger demand, a new integrated model is proposed, where frequencies are readjusted. Then, the railway timetable and rolling stock assignment are also calculated, where shunting operations are taken into account. These operations may sometimes malfunction, causing localized incidents that could propagate throughout the entire network due to cascading effects. This type of operations will be penalized with the goal of selectively avoiding them and ameliorating their high malfunction probabilities. Swapping operations will also be ensured using homogeneous rolling stock material and ensuring parkings in strategic stations. We illustrate our model using computational experiments drawn from RENFE (the main Spanish operator of suburban passenger trains) in Madrid, Spain. The results show that through this integrated approach a greater robustness degree can be obtained. [ABSTRACT FROM AUTHOR]

This paper develops a stochastic programming model that integrates the most recent regulation rules of the Spanish peninsular system for bilateral contracts in the day-ahead optimal bid problem. Our model allows a price-taker generation company to decide the unit commitment of the thermal and combined cycle programming units, the economic dispatch of the bilateral contract between all the programming units and the optimal sale bid by observing the Spanish peninsular regulation. The model was solved using real data of a typical generation company and a set of scenarios for the Spanish market price. The results are reported and analyzed. [ABSTRACT FROM AUTHOR]

Firstly, in this paper, we analyze how the electors vote in the Senate election in Spain. We show examples in which the election outcome is predictable and not proportional. We also show similar examples for other social choice problems, in which the strategic vote affords a great advantage to those who put it into practice. Secondly, we introduce the definition of a proportional social choice method. We then prove the main result that determines the only Borda type social choice method that has the same proportionality as a given divisor method. We include results with the vote thresholds that guarantee the candidates will be elected under Limited Approval Voting and, finally, compare these thresholds with the proportional Borda Method. [ABSTRACT FROM AUTHOR]

In this paper we introduce an asymmetric model of continuous electricity auctions with limited production capacity and bounded supply functions. The strategic bidding is studied with this model by means of an electricity market game. We prove that for every electricity market game with continuous cost functions a mixed-strategy Nash equilibrium always exists. In particular, we focus on the behavior of producers in the Spanish electricity market. We consider a very simple form for the Spanish electricity market: an oligopoly consisting just of independent hydro-electric power production units in a single wet period. We show that a pure-strategy Nash equilibrium for the Spanish electricity market game always exists. [ABSTRACT FROM AUTHOR]