The structure of verbal sequences analyzed with unsupervised learning techniques

Data mining allows the exploration of sequences of phenomena, whereas one usually tends to focus on isolated phenomena or on the relation between two phenomena. It offers invaluable tools for theoretical analyses and exploration of the structure of sentences, texts, dialogues, and speech. We report here the results of an attempt at using it for inspecting sequences of verbs from French accounts of road accidents. This analysis comes from an original approach of unsupervised training allowing the discovery of the structure of sequential data. The entries of the analyzer were only made of the verbs appearing in the sentences. It provided a classification of the links between two successive verbs into four distinct clusters, allowing thus text segmentation. We give here an interpretation of these clusters by applying a statistical analysis to independent semantic annotations

Road accident analysis in Lisbon

Studies about urban mobility in big European cities have been increasing due to the high volume of data and interest that exists about this topic. As such, competent authorities feel the need to design intelligent solutions that help to mitigate mobility problems. This research work was developed using mobility data from the Câmara Municipal de Lisboa, namely road accidents that occurred in 2019 in this city, using the CRISP-DM approach in Python. The data were previously integrated and cleaned to later be submitted to visualization methods, to identify patterns of occurrence of road accidents in the city of Lisbon.A mobilidade urbana nas grandes cidades europeias tem sido cada vez mais estudada devido ao elevado volume de dados e de interesse que existe sobre a mesma. Como tal, as autoridades competentes sentem a necessidade de desenhar soluções inteligentes que auxiliem na mitigação de problemas de mobilidade. O presente trabalho de investigação foi desenvolvido com os dados de mobilidade da Câmara Municipal de Lisboa, nomeadamente dos acidentes rodoviários ocorridos no ano de 2019 nesta cidade, através da abordagem CRISP-DM em Python. Os dados foram previamente integrados e limpos para posteriormente serem submetidos a métodos de visualização, de forma a identificar padrões de ocorrência de acidentes rodoviários na cidade de Lisboa

Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level

This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users\u27 (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model\u27s estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model

Data‐driven solutions to understand disruptive problems in transportation—the lisbon case study

Albuquerque, V., Oliveira, A., Barbosa, J. L., Rodrigues, R. S., Andrade, F., Dias, M. S., & Ferreira, J. C. (2021). Smart cities: Data‐driven solutions to understand disruptive problems in transportation—the lisbon case study. Energies, 14(11), 1-25. [3044]. https://doi.org/10.3390/en14113044Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP‐DM approach using Python. We focused on mobility problems and interdependence and cascading‐effect solutions for the city of Lisbon. We developed data‐driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.publishersversionpublishe