In this paper, a bottom–up vehicle emission model is proposed to estimate real-time $\hbox{CO}_{2}$ 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 $ \hbox{CO}_{2}$ 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 $\hbox{CO}_{2}$ emissions with a high spatiotemporal resolution.


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    Title :

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


    Contributors:
    Chang, Xiaomeng (author) / Chen, Bi Yu (author) / Li, Qingquan (author) / Cui, Xiaohui (author) / Tang, Luliang (author) / Liu, Cheng (author)


    Publication date :

    2013-03-01


    Size :

    976547 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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