Abstract
Weather conditions have a major impact on citizens’ daily mobility. Depending on weather conditions trips may be delayed, demand may be changed as well as the modal shift. These variations have a major impact on the use and operation of public transport, particularly in transport systems that operate close to capacity. However, the influence of weather conditions on transport demand is difficult to predict and quantify. For this purpose, an artificial neural network model – the Multilayer Perceptron – is used as a regression model to estimate the demand of urban public transport buses based on weather conditions. Transit bus ridership and weather conditions were collected along a year from a medium-size European metropolitan area (Oporto, Portugal) and linked under the assumption that individuals choose the travel mode based on the weather conditions that are observed during the departure hour, the hour before and two hours before. The transit ridership data were also labelled according to the hour, day of the week, month, and whether there was a strike and/or holiday or not. The results demonstrate that it is possible to predict the demand of public transport buses using the weather conditions observed two hours before with low error for the entire network (MAE = 143 and RMSE = 322). The use of weather conditions allow to decreases the error of the prediction by ~8% for the entire network. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The scarcity of resources is one of the main concerns for the present and the future of the environment and society. The "load factor" in logistic transport has a great potential for improvement, especially in the last-mile deliveries, as the transport of goods is largely fragmented between several small companies using small vehicles. This paper investigates the potential for collaboration to increase efficiency in urban logistics. Based on an overview of the concepts and initiatives regarding vertical and horizontal collaboration, a research agenda is proposed.

Abstract
The multiplicity of stakeholders in urban contexts can greatly increase the complexity of transportation systems. Since all stakeholders depend, to varying degrees, on the same data to get the information for their mobility, this work considers that an integrated information system, focused on their different needs, will significantly improve the efficiency of transportation systems. A stakeholder-focused system makes the provided information more relevant, while an integrated system fosters the sharing of the data that generates this information. To build such a system, a conceptual framework focused on stakeholders and their decision processes was developed. This new framework takes advantage of existing ones, such as the Zachman framework, the Enterprise Architecture Design, and the Multilevel Service Design. The proposed multidisciplinary approach, putting together information systems (IS) and service design concepts, has considerable potential in ensuring that the right information reaches each stakeholder at the right time.

Abstract

Abstract
Societies face complex challenges, which require a harmonious transition to future patterns. A strategic response to reconfigure society should assure the provision of critical resources and the resilience of the socio-technical systems in the long-term. The implementation of a new dominant technology and paradigm in the transport context is a complex process: it is multidimensional, requires seamless integration of various features and entails trade-offs in the decision-making process. The authors use general morphological analysis (GMA) as a theoretical framework that supports decision making in the transition management of transport to a new powertrain technology. This example is just an illustration of a broader representation of all the possible solutions of a large-scale problem as it is the case of any multi-level process of governance, leading to the pursuit of new paradigms. The originality of the paper lies on using a GMA that addresses sustainable challenges in a transport system from a multi-level perspective. Copyright © 2021 Inderscience Enterprises Ltd.

Abstract
Achieving a balanced healthcare workforce requires health planners to adjust the supply of health human resources (HHR). Mathematical programming models have been widely used to assist such planning, but the way uncertainty is usually considered in these models entails methodological and practical issues and often disregards radical yet plausible changes to the future. This study proposes a new socio-technical methodology to factor in uncertainty over the future within mathematical programming modelling. The methodological approach makes use of foresight and scenario planning concepts to build tailor-made scenarios and scenario fit input parameters, which are then used within mathematical programming models. Health stakeholders and experts are engaged in the scenario building process. Causal map modelling and morphological analysis are adopted to digest stakeholders and experts' information about the future and give origin to contrasting and meaningful scenarios describing plausible future. These scenarios are then adjusted and validated by stakeholders and experts, who then elicit their best quantitative estimates for coherent combinations of input parameters for the mathematical programming model under each scenario. These sets of parameters for each scenario are then fed to the mathematical programming model to obtain optimal solutions that can be interpreted in light of the meaning of the scenario. The proposed methodology has been applied to a case study involving HHR planning in Portugal, but its scope far extends HHR planning, being especially suited for addressing strategic and policy planning problems that are sensitive to input parameters.