The ever growing availability of high resolution mobility data has triggered the development of a number of data-driven solutions, leading to a significant improvement in the body of knowledge on Intelligent Transportation Systems (ITS). Nevertheless, to date, an ITS practitioner willing to perform data analytics studies has to face a number of technological challenges, due to a plethora of different data formats. Indeed, while in other data-driven domains a number of well-established tools, such as KNIME or RapidMiner, is available to support the definition of Knowledge Discovery from Data (KDD) pipelines, when dealing with spatio-temporal data, a lot of steps have to be manually implemented, significantly hindering productivity. To address this issue, we propose a solution we developed to support ITS practitioners in the definition of KDD processes on mobility data. Indeed, by exploiting the modular capabilities of the KNIME Analytics Platform, we developed a collection of new components specifically designed to automatize some standard KDD steps in the ITS domain, such as map-matching, trajectory partitioning, flexible routing algorithms, and map coverage analysis. To show the effectiveness of these components, we report also on how we applied it on a real-world massive trajectory dataset. All the components we developed are open-source and freely downloadable, as we hope that they could further foster the data-driven ITS research.

Extending KNIME with Floating Car Data Analytics Capabilities (dataset and materials)

Sergio Di Martino;Luigi Libero Lucio Starace;
2021

Abstract

The ever growing availability of high resolution mobility data has triggered the development of a number of data-driven solutions, leading to a significant improvement in the body of knowledge on Intelligent Transportation Systems (ITS). Nevertheless, to date, an ITS practitioner willing to perform data analytics studies has to face a number of technological challenges, due to a plethora of different data formats. Indeed, while in other data-driven domains a number of well-established tools, such as KNIME or RapidMiner, is available to support the definition of Knowledge Discovery from Data (KDD) pipelines, when dealing with spatio-temporal data, a lot of steps have to be manually implemented, significantly hindering productivity. To address this issue, we propose a solution we developed to support ITS practitioners in the definition of KDD processes on mobility data. Indeed, by exploiting the modular capabilities of the KNIME Analytics Platform, we developed a collection of new components specifically designed to automatize some standard KDD steps in the ITS domain, such as map-matching, trajectory partitioning, flexible routing algorithms, and map coverage analysis. To show the effectiveness of these components, we report also on how we applied it on a real-world massive trajectory dataset. All the components we developed are open-source and freely downloadable, as we hope that they could further foster the data-driven ITS research.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/857481
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