Short-Term Air Pollution Forecasting Using Embeddings in Neural Networks

Air quality is a highly relevant issue for any developed economy. The high incidence of pollution levels and their impact on human health has attracted the attention of the machine-learning scientific community. We present a study using several machine-learning methods to forecast NO2 concentration using historical pollution data and meteorological variables and apply them to the city of Erfurt, Germany. We propose modelling the time dependency using embedding variables, which enable the model to learn the implicit behaviour of traffic and offers the possibility to elaborate on local events. In addition, the model uses seven meteorological features to forecast the NO2 concentration for the next hours. The forecasting model also uses the seasonality of the pollution levels. Our experimental study shows that promising forecasts can be achieved, especially for holidays and similar occasions which lead to shifts in usual seasonality patterns. While the MAE values of the compared models range from 4.3 to 15, our model achieves values of 4.4 to 7.4 and thus outperforms the others in almost every instance. Those forecasts again can for example be used to regulate sources of pollutants such as, e.g., traffic

Documentation for LinTim 2021.10

LinTim is a scientific software toolbox that has been under development since 2007, giving the possibility to solve the various planning steps in public transportation. Although the name originally derives from "Lineplanning and Timetabling", the available functions have grown far beyond this scope. This document is the documentation for version 2021.10. For more information, see https://www.lintim.ne

Documentation for LinTim 2021.10

LinTim is a scientific software toolbox that has been under development since 2007, giving the possibility to solve the various planning steps in public transportation. Although the name originally derives from "Lineplanning and Timetabling", the available functions have grown far beyond this scope. This document is the documentation for version 2021.10. For more information, see https://www.lintim.ne

Documentation for LinTim 2022.08

LinTim is a scientific software toolbox that has been under development since 2007, giving the possibility to solve the various planning steps in public transportation. Although the name originally derives from "Lineplanning and Timetabling", the available functions have grown far beyond this scope. This document is the documentation for version 2022.08. For more information, see https://www.lintim.ne

Documentation for LinTim 2022.08

LinTim is a scientific software toolbox that has been under development since 2007, giving the possibility to solve the various planning steps in public transportation. Although the name originally derives from "Lineplanning and Timetabling", the available functions have grown far beyond this scope. This document is the documentation for version 2022.08. For more information, see https://www.lintim.ne

LinTim: An integrated environment for mathematical public transport optimization

LinTim is a scientific algorithm and dataset library that has been under development since 2007 and offers the possibility to carry out the various planning steps in public transportation. Although the name originally derives from "Line planning and Timetabling", the available functions have grown far beyond this scope. This is the documentation for version 2024.08. For more information, see https://www.lintim.net

Documentation for LinTim 2023.12

LinTim is a scientific algorithm and dataset library that has been under development since 2007 and offers the possibility to carry out the various planning steps in public transportation. Although the name originally derives from "Line planning and Timetabling", the available functions have grown far beyond this scope. This is the documentation for version 2023.12. For more information, see https://www.lintim.net