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

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6 Author(s)
Xiaomeng Chang ; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China ; Bi Yu Chen ; Qingquan Li ; Xiaohui Cui
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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.

Published in:

IEEE Transactions on Intelligent Transportation Systems  (Volume:14 ,  Issue: 1 )