摘要：The temporal and spatial prediction of nitrogen dioxide (NO2) is very essential because of its harmful impacts on the environment. Its forecasting would help, for example, to regulate predictively the traffic flow. Traditionally, air quality measurements are performed at fixed locations or dedicated mobile laboratories. In this work, we installed a measurement technology in a vehicle and connected it to the vehicle measuring system in order to be able to evaluate further parameters. To this end, we selected one route profile and continuously measured the NO2 concentration in real-time traffic. We have driven this route profile several times in succession. The rationale of this approach is the idea that several vehicles are equipped with the same measurement technology and drive on the same route profile within the same time. The contribution of this work is to forecast the NO2 concentration for a given route profile under constant weather conditions based on mobile measurements. To this end, we divided the recorded data into training and test data and investigated five different approaches for forecasting the NO2 concentration on the respective route profile. Among other aspects, we used cross-validation methods in order to assess the prediction quality. Results show that sliding-window approaches using the averaging of previous rounds are most suitable for predicting NO2 concentration. Furthermore, our data reveal that the prediction quality is improved when the test data immediately follow the training data.
其他摘要：The temporal and spatial prediction of nitrogen dioxide (NO2) is very essential because of its harmful impacts on the environment. Its forecasting would help, for example, to regulate predictively the traffic flow. Traditionally, air quality measurements are performed at fixed locations or dedicated mobile laboratories. In this work, we installed a measurement technology in a vehicle and connected it to the vehicle measuring system in order to be able to evaluate further parameters. To this end, we selected one route profile and continuously measured the NO2 concentration in real-time traffic. We have driven this route profile several times in succession. The rationale of this approach is the idea that several vehicles are equipped with the same measurement technology and drive on the same route profile within the same time. The contribution of this work is to forecast the NO2 concentration for a given route profile under constant weather conditions based on mobile measurements. To this end, we divided the recorded data into training and test data and investigated five different approaches for forecasting the NO2 concentration on the respective route profile. Among other aspects, we used cross-validation methods in order to assess the prediction quality. Results show that sliding-window approaches using the averaging of previous rounds are most suitable for predicting NO2 concentration. Furthermore, our data reveal that the prediction quality is improved when the test data immediately follow the training data.
