Abstract
This study presents an innovative procedure to assess the evolution of the social performance of firms over time using a Benefit-of-the-Doubt Composite indicator specified with a Directional Distance Function and the Malmquist index. In recent years, the social indicators of large corporations are increasingly being used to evaluate corporate performance. Reputation issues associated with the firms’ impact on society, including local employment and contribution to local economic development are considered critical. This paper develops a composite indicator of social performance that can be used both for benchmarking comparisons among firms within an industry and to monitor the evolution of performance over time. Both desirable and undesirable factors can be taken into account in the performance evaluation. An illustrative application involving the assessment of 24 large mining firms in the years 2011 and 2012 is discussed. The specification of indicators reflecting social burdens and benefits of mining firms is based on international standards and guidelines for large corporations. The managerial implications of the results obtained are discussed. © 2020 Elsevier Ltd

Abstract
In recent decades, the concept of urban metabolism has been widely applied at different scales. This paper proposes an optimization model, based on Data Envelopment Analysis, for the evaluation and benchmarking of countries' metabolism. The EU-28 countries are analyzed based on economic and environmental indicators, including the resources consumed (energy and materials) and environmental pressures (GHG emissions and waste) associated with the value-added from the economic activities. The empirical results produced a ranking of countries' based on their metabolic performance underlying the creation of wealth, along with the targets for the countries with lower metabolic performance. This new metabolic approach is a contribution to the design of policies for the promotion of sustainable and resilient services.

Abstract

Abstract
Fleet tracking technology collects real-time information about geolocation of vehicles as well as driving-related data. This information is typically used for location monitoring as well as for analysis of routes, vehicles and drivers. From an operational point of view, the geolocation simply identifies the state of a vehicle in terms of positioning and navigation. From a management point of view, the geolocation may be used to infer the state of a vehicle in terms of process (e.g., driving, fueling, maintenance, or lunch break). Meaningful information may be extracted from these inferred states using process mining. An innovative methodology for inferring process states from geolocation data is proposed in this paper. Also, it is presented the potential of applying process mining techniques on geolocation data for process discovery. © 2020 The Authors. Published by Elsevier B.V.

Abstract
This work apply a deep learning artificial neural network model-the Multilayer Perceptron- A s a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: Individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays). © 2020 Tânia Fontes et al., published by Sciendo.

Abstract
Accessibility is one of the key measures of urban transportation planning, which quantify how easy is the access to a facility. Public transport accessibility concerns of the access level of geographical locations to public transport. In this paper, accessibility is used as an indicator to estimate social exclusion based on the maximum distance that someone has to walk to reach the public transport. The concept of the 6-minute walking distance (6MWD) is applied to measure accurately the walking ability for different groups of the population. A real life case study is conducted to get insight into the transportation network of the Porto Metropolitan Area, Portugal. For this purpose, geographic, demographic and infrastructure data were collected and integrated. Also, webservices are used to measure walking distances between locations. The results of this study allowed to characterize regions by different levels of accessibility, providing insight into the social exclusion in public transport. This assessment is used not only to identify inequities but also to get an overview of the service quality of public transport. © 2020 The Authors. Published by ELSEVIER B.V.