Estimating Real-Time Traffic Carbon Dioxide Emissions Based on Intelligent Transportation System Technologies

In this paper, a bottom–up vehicle emission model is proposed to estimate real-time CO₂ emissions using intelligent transportation system (ITS) technologies. In the proposed model, traffic data that were collected by ITS are fully utilized to estimate detailed vehicle technology data (e. g. , vehicle type) and driving pattern data (e. g. , speed, acceleration, and road slope) in the road network. The road network is divided into a set of small road segments to consider the effects of heterogeneous speeds within a road link. A real-world case study in Beijing, China, is carried out to demonstrate the applicability of the proposed model. The spatiotemporal distributions of CO₂ emissions in Beijing are analyzed and discussed. The results of the case study indicate that ITS technologies can be a useful tool for real-time estimations of CO₂ emissions with a high spatiotemporal resolution.