Traffic flow prediction for vehicle emission calculation based on graph convolutional networks

Monitoring the distribution of vehicle exhaust emissions within the city is a very challenging problem since it is affected by many complex factors, such as spatial-temporal correlation and the other environment conditions. In addition, the technology of using sensors to directly monitor vehicle exhaust emissions is still in the initial stage, and it is hard to implement direct monitoring in a large area. Thus, we use the existing environmental theory to measure the distribution of vehicle exhaust emissions in cities by traffic volume. In this paper, the problem we need to solve is how to use the data of sparse monitoring stations and inherent traffic network to infer the spatial-temporal distribution of traffic volume. In order to solve this problem, we propose a graph convolutional network model to extract the characteristics of traffic data and other features. We have done a lot of experiments on real traffic data sets. The experimental results show that the proposed method performs better than the existing methods.