GPS Data Correction Through The IVE Model For Use In Vehicle Emission Estimates for Congestion Pricing

摘要

Environmental pollution as a result of vehicle emissions has become a growing problem around the world due to the accelerated increase of cars on the road. This affects the health and life quality of citizens, especially in highly populated areas. Despite this situation, some cities have been very effective in controlling emission sources. According to the state of the art related to environment and private transportation, congestions taxes are powerful policies in terms of reducing the pollution generated by vehicles. However, when applying this kind of policies it is not easy to demonstrate emissions reduction, so that, its effectiveness is debated. Measuring vehicle emissions on the road is not an easy task. Hence, there are many models to approach emissions estimation. One of them is the International Emission Model (IVE), the problem with this and other models is the lack of data accuracy and the difficulties in the preprocessing of it. The objective of this study is to foster the development of emissions inventories from mobile sources using the IVE model, through making data processing easy. To this purpose, it was addressed the main problem concerning the low quality of velocity and altitude data registered with GPS and proposed the development of an application capable of automatically filtering the atypical route data and softening the curve data with the Smoothing Spline method. The application calculated the vehicle-specific power (VSP) Bins according to the IVE model and automatically exported the results to the database of the model. As a result, it was possible to show the importance of adequately processing the data from the GPS equipment and the significant variations in the results of the emissions calculations if no data correction is conducted. This application can motivate more cities to apply vehicle congestion policies, since the estimation of emissions is easy to get and provided with more accurate results.