摘要: In this paper, we presented a ROad SEgment-based emission model (ROSE) for transportation Green House Gas(GHG) emissions estimation. The objective of this study is to provide a framework for quickly estimatingtraffic-related GHG e... 展开 In this paper, we presented a ROad SEgment-based emission model (ROSE) for transportation Green House Gas(GHG) emissions estimation. The objective of this study is to provide a framework for quickly estimatingtraffic-related GHG emissions and analyzing its spatiotemporal distribution and variation based on real-timetraffic data. The model is carried out a combination of Intelligent Transport System (ITS) technology, GeographicInformation System (GIS) technology, and the International Vehicle Emission Model (IVE). In the ROSE model,the ITS' floating car data (FCD) and loop detector data (LDD) are used as the model input. The IVE model is usedfor providing microscopic vehicle emission rates; and GIS is not only used as a database exchanger, but also usedas a computation and a visualization tool in ROSE model. This paper will discuss two fundamental worksconducted in our ROSE model research project: 1) ITS real-time traffic data collection and geographic-relateddata unification; 2) vehicle driving activity generation & road-segment based CO_2 emission computation. Todemonstrate the effectiveness of the ROSE model, we applied this model in a case study for estimating the dailyCO_2 emissions generated from the highway transportation of Beijing, China during the year of 2008. The resultshows that the ROSE model can provide micro level highly accurate and real-time GHG emission for the wholeurban area such as Beijing city. 收起