One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computational capacities have increased the use of Machine Learning to address traffic prediction, which is mostly modelled as a supervised regression problem. Although some studies have presented taxonomies to sort the literature in this field, they are mostly oriented to classify the Machine Learning methods applied and a little effort has been directed to categorise the traffic forecasting problems approached by them. As far as we know, there is no comprehensive taxonomy that classifies these problems from the point of view of both traffic and Machine Learning. In this paper, we propose a taxonomy to categorise the aforementioned problems from both a traffic and a supervised regression learning perspective. The taxonomy aims at unifying and consolidating categorisation criteria related to traffic and it introduces new criteria to classify the problems in terms of how they are modelled from a supervised regression approach. Traffic forecasting literature, from 2000 to 2019, is categorised using this taxonomy to illustrate its descriptive power. From this categorisation, different remarks are discussed regarding current gaps and trends in the addressed traffic forecasting area.

Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.

In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

The impact of food loss and waste (FLW) generation on food supply chains' (FSC) sustainability represents a challenge embodied in the Sustainable Development Goal (SDG) 12.3. This problem requires a methodology to measure such an impact in a rigorous, holistic, and standardized way that can be applied to any FSC. This paper aims to develop and validate a single index to assess the readiness of FSCs to implement FLW prevention strategies and measure their impact: the so-called FOODRUS index. The co-creation methodology followed incorporates experts and FSC stakeholders feedback. The index has been validated in 3 FSCs: The Slovak pilot scored 74.35%, the Spanish pilot reached 68.79%, and the Danish pilot was rated 61.14%. Its calculation, eased by the FOODRUS index self-assessment tool (described in the Appendix), allows quick diagnosis of the FSC capability to implement FLW prevention strategies considering both the knowledge provided by experts and the experience of the FSC stakeholders that participated in its co-creation process. In this way the FSC can assess its FLW prevention performance at a strategic and management level, with the aim of improving its sustainability impact.