Neste trabalho é proposto um método de carregamento incremental dos fluxos veiculares para a estimação de matriz O-D sintética. A principal motivação para o desenvolvimento deste trabalho está na complexidade dos métodos de estimação de matriz sintética pelo método iterativo, que tem conduzido a resultados não satisfatórios. O método de carregamento incremental, ora proposto neste trabalho, pode ser definido como o inverso do método de alocação incremental de viagens aos arcos de uma rede viária. A matriz O-D obtida com este método é o resultado da soma sucessiva das sub-matrizes estimadas através das parcelas dos fluxos observados nos arcos da rede. Este método pode ser aplicado em qualquer tipo de rede viária: congestionada ou não congestionada e com ou sem continuidade de fluxo. Para verificação do desempenho do método foram realizados testes experimentais, nos quais os resultados obtidos foram comparados com os valores observados. O desempenho do método incremental não se mostrou satisfatório. Assim, com o intuito de verificar a sua utilidade como um método alternativo os mesmos dados foram processados através de dois métodos iterativos. Os resultados mostraram que os erros são ainda maiores do que os obtidos pelo método proposto. A principal conclusão deste trabalho é que o método incremental pode ser usado como uma alternativa aos métodos iterativos. / A synthetic O-D matrix estimation method, based on incremental loading of traffic flow, was proposed in this work. This research was motivated because of the complexity of the iterative methods for synthetic matrix estimation that might produce bias accumulation in the results. The referred incremental loading method could be defined as the inverse of the incremental assignment method for trips to links of a traffic network. The O-D matrix is gathered by successively summing the sub-matrices obtained from parts of the traffic flow counted on the links of the traffic network. This method could be applied to any traffic networks: congested or uncongested and with or without volumetric continuity. As a part of verification proceeding, several experimental tests were carried out to evaluate the method performance. In these tests the estimated results were compared with the real values. These results show that the incremental loading method performance was not satisfactory. Thus, aiming to test the utility of the incremental method, a second round of experimental tests was conducted to evaluate two iterative methods. In these second round tests, the results show that theses methods performance was worse those of the incremental method. The main conclusion of this work is that the incremental loading method for synthetic matrices estimation could be used as an alternative to the iterative methods.

Neste trabalho é proposto um método de carregamento incremental dos fluxos veiculares para a estimação de matriz O-D sintética. A principal motivação para o desenvolvimento deste trabalho está na complexidade dos métodos de estimação de matriz sintética pelo método iterativo, que tem conduzido a resultados não satisfatórios. O método de carregamento incremental, ora proposto neste trabalho, pode ser definido como o inverso do método de alocação incremental de viagens aos arcos de uma rede viária. A matriz O-D obtida com este método é o resultado da soma sucessiva das sub-matrizes estimadas através das parcelas dos fluxos observados nos arcos da rede. Este método pode ser aplicado em qualquer tipo de rede viária: congestionada ou não congestionada e com ou sem continuidade de fluxo. Para verificação do desempenho do método foram realizados testes experimentais, nos quais os resultados obtidos foram comparados com os valores observados. O desempenho do método incremental não se mostrou satisfatório. Assim, com o intuito de verificar a sua utilidade como um método alternativo os mesmos dados foram processados através de dois métodos iterativos. Os resultados mostraram que os erros são ainda maiores do que os obtidos pelo método proposto. A principal conclusão deste trabalho é que o método incremental pode ser usado como uma alternativa aos métodos iterativos. / A synthetic O-D matrix estimation method, based on incremental loading of traffic flow, was proposed in this work. This research was motivated because of the complexity of the iterative methods for synthetic matrix estimation that might produce bias accumulation in the results. The referred incremental loading method could be defined as the inverse of the incremental assignment method for trips to links of a traffic network. The O-D matrix is gathered by successively summing the sub-matrices obtained from parts of the traffic flow counted on the links of the traffic network. This method could be applied to any traffic networks: congested or uncongested and with or without volumetric continuity. As a part of verification proceeding, several experimental tests were carried out to evaluate the method performance. In these tests the estimated results were compared with the real values. These results show that the incremental loading method performance was not satisfactory. Thus, aiming to test the utility of the incremental method, a second round of experimental tests was conducted to evaluate two iterative methods. In these second round tests, the results show that theses methods performance was worse those of the incremental method. The main conclusion of this work is that the incremental loading method for synthetic matrices estimation could be used as an alternative to the iterative methods.

Background: In the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium, an IT-based clinical trial recruitment support system was developed based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Currently, OMOP CDM is populated with German Fast Healthcare Interoperability Resources (FHIR) using an Extract-Transform-Load (ETL) process, which was designed as a bulk load. However, the computational effort that comes with an everyday full load is not efficient for daily recruitment. Objective: The aim of this study is to extend our existing ETL process with the option of incremental loading to efficiently support daily updated data. Methods: Based on our existing bulk ETL process, we performed an analysis to determine the requirements of incremental loading. Furthermore, a literature review was conducted to identify adaptable approaches. Based on this, we implemented three methods to integrate incremental loading into our ETL process. Lastly, a test suite was defined to evaluate the incremental loading for data correctness and performance compared to bulk loading. Results: The resulting ETL process supports bulk and incremental loading. Performance tests show that the incremental load took 87.5% less execution time than the bulk load (2.12 min compared to 17.07 min) related to changes of 1 day, while no data differences occurred in OMOP CDM. Conclusions: Since incremental loading is more efficient than a daily bulk load and both loading options result in the same amount of data, we recommend using bulk load for an initial load and switching to incremental load for daily updates. The resulting incremental ETL logic can be applied internationally since it is not restricted to German FHIR profiles.

Engineers frequently use computerized numeric models to calculate and predict water levels and current patterns for rivers, bays, and other bodies of water. This computation often involves an iterative process known as incremental loading that can cause frustration and consume a lot of time. Although the steering module in the Surface-water Modeling System (SMS) automates incremental loading to minimize user interaction, it can still be very time consuming. This thesis examines the steering module and the incremental loading process to improve its efficiency. Specifically, the RMA2 and FESWMS models are utilized. Two methods of improving efficiency are examined. The first includes creating predicted solution files for each step of the incremental loading process. These predictions allow the steering module to take larger steps and decrease the computation time. The second method changes the algorithm used to determine the size of each step. Finally, the interface to the process was examined and simplified to require minimal input and to make the input more intuitive.