A MapReduce framework for on-road mobile fossil fuel combustion CO2 emission estimation

Abstract

Research of global climate change has an urgent need for the distribution of CO2 emissions with high spatial resolution. Traffic is an important carbon source in the urban development. Real-time data collected by the Intelligent Traffic System (ITS) plays a more and more significant role in the CO2 spatial-temporal distribution data production. However, the amount of data is so large that the data processing task has become a great challenge to the traditional data warehouse. MapReduce is a powerful framework for huge dataset processing on clusters of computers. In this paper, we proposed a MapReduce framework for on-road mobile fossil fuel combustion CO2 emission estimation. We implemented the emission estimation tool suite of our prototype based on Hadoop. The experiment result shows that the system is efficient and is suitable for this kind of applications.