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Many university campuses are suffering from serious mobility problems resulting from excessive use of the private car by students, teachers and administrative staff. This article proposes a methodology based on a revealed and stated preferences survey aimed at estimating the importance of different variables on users mobility choices in order to simulate their reaction to policies such as the introduction of new modes of transport or charging for on campus parking. This estimation was based on a Mixed Logit model considering the possible presence of heterogeneity in user preferences. The introduction of these results into an optimization model has also allowed us to calculate the optimal parking fare that should be charged which would minimize the number of free spaces on campus or maximize the income received. This methodology has been applied to a case study at the campus of the University of Cantabria (Spain). The elasticities calculated using a Mixed Logit model confirm that setting a fare for parking on campus would be a serious disincentive against private car use in favor of more sustainable transport modes. Furthermore, the optimization model allowed us to calculate the fare that would maximize the income obtained from the parking spaces, an income that could then be used to strengthen the campus sustainable mobility policies. • The aim is to propose a methodology to promote sustainable mobility in campus. • The data was collected using a revealed and stated preferences survey. • Mixed logit and optimization models were estimated. • Results showed a fare for parking would disincentive private car use. • The optimization allowed us to calculate the optimal parking fare. [ABSTRACT FROM AUTHOR]

Although the need for more environmentally friendly vehicles was recognized some decades ago, this new market has not yet established itself. Consumer behavior needs to be studied to ascertain when people will decide to purchase hybrid or electric vehicles rather than conventional ones. An in-depth review of the state-of-the-art has identified existing deficiencies and these are addressed in this paper, proposing a new approach that is applied to the case of Santander in Spain. Emphasis is placed on the role of citizens in researching the local market and their requirements with respect to such vehicles; our model assumes variability in user preferences, an utmost requirement as concluded from the literature review. Results suggest that the highest demand for cleaner vehicles would be achieved in two ways: firstly, by penalizing conventional vehicles in terms of costs/km; secondly, by providing incentives directed at lowering the purchasing price of hybrid and electric vehicles. Finally, as demand becomes more elastic, the preferred strategy should initially focus on hybrid vehicles. [ABSTRACT FROM AUTHOR]

The influence of accessibility to opportunities in trip generation continues to be debated in the specialised literature given its relevance to simulate phenomena such as induced demand. This article estimates multiple linear regression models (MLR), spatial autoregressive models (SAR), spatial autoregressive models in the error term (SEM) and spatially filtered Poisson regression models (SPO) to discover whether or not accessibility is a significant factor in trip generation using data from the urban area of Santander (Spain). The results obtained provide evidence which shows that, on an intraurban scale, more accessibility to opportunities decreases trip production in private vehicle for work purpose, whereas it increases trip production in other transport modes for non-mandatory purposes. For the correct interpretation of the estimated parameters it was important to consider the direct and indirect effects of the independent variables in the SAR production models. Finally, the validation of the models showed that the SAR and SEM models had a mean squared error slightly lower than the MLR models in predicting overall trip production. This was because the spatial models reduced the correlation of the residuals present in the MLR models. Furthermore, the SPO models performed better in validation mode than all the continuous models. [ABSTRACT FROM AUTHOR]

This article develops a methodology using a Geographical Information System (GIS) to evaluate the best location to stop a high speed passenger train when faced with an undesired event. The proposed method is based on multicriteria decision-making where different stretches of line which could be chosen as the stopping point are ranked depending on the characteristics of the line, the surrounding area and its accessibility for equipment. The method was integrated into the GIS to develop an expert support system for decision makers faced with different kinds of undesired events. It has been applied to a case study on the high speed line between Valladolid and Madrid (Spain). The proposed method is new and has not previously been applied to high speed railway networks and could be adapted to other case studies. The speed of the algorithm provides an almost instantaneous reply within seconds of an emergency situation occurring. The method can therefore be part of an overall support system for decision making in undesired rail events. [ABSTRACT FROM AUTHOR]