RESUMO
This survey aimed to obtain and analyze relevant information on the implementation of best practices in information technology governance in Colombian organizations to identify current trends in Information Technology (IT) project management, the impact of IT governance, and the use of emerging technologies. A semistructured survey was conducted among the IT professionals of Colombian companies of different sizes and economic sectors between 2019 and 2022. The survey was designed considering international references, such as ISACA, and following the Kimball methodology to guide the analysis. A total of 252 responses were collected, and 237 records were analyzed. It was concluded that the successful implementation of IT governance can improve efficiency, productivity, decision-making, information security, competitiveness, and customer service quality. However, the Small and Medium-sized Enterprises (SME's) face challenges such as a lack of skilled human resources, resistance to change, and high implementation costs. To address these challenges, strategies such as prioritizing investments, focusing on internal talent, communicating benefits and expected results, and investing in the training of the organization's personnel are suggested.
RESUMO
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.