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On October 26, 2016, a large-scale mass movement occurred surrounding the Medellín-Bogotá highway, near the municipality of Copacabana. This paper investigates the physical and legal conditions of the place where the landslide occurred, and thus, to corroborate the causes, the possibility of avoiding or preventing the phenomenon and the influence of anthropic activities in the sector. To determine the susceptibility to mass movements and verify the physical conditions, it was carried out by means of geological mapping, geomorphology and interpretation of satellite images. The legal and bibliographic review was consulted in the reports of the event, the PBOT of the municipality of Copacabana and the mining file of the Cantera Las Nieves. The area was found to be highly susceptible to mass movements, and exploitation in the Cantera Las Nieves triggered the landslide, which could be avoided. Similar phenomena could occur in the near future in the area. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]