This paper develops a method for estimating carbon-emission specific road networks, considering the presence of electric vehicles (EVs). A mixed equilibrium traffic assignment model is set up to obtain the traffic volume for each link in the network, where oil-fueled vehicles (OFVs) prioritizing travel time minimization, while EVs also consider charging station locations and battery charge state in route selection. A carbon-emission estimation method is then developed, which is calculated by three parameters of traffic volume, average speed, and the road category. A case study is carried out using two networks. It is found that the travel time of the road network has increased by 27%, because EVs tend to choose paths containing charging stations. The route selection of EVs is affected by perceived risk, safe electric quantity, and expected charging electricity. EVs can reduce carbon dioxide emissions but not energy consumption for road network. In addition, it was found that the location of charging stations has a significant impact on traffic flow. After optimizing the location of charging stations, the total travel time, total carbon emissions, and balance of charging station utilization indicators in the transportation network have all relatively decreased. Among them, the total travel time has decreased by 0.2%, the total carbon emissions have decreased by 1.85%, and the balance of charging station utilization has decreased by 0.95%. The research is helpful for determining the locations of charging piles and designing road networks, and it is also helpful for estimating the traffic flow and carbon emissions.


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    Title :

    Impact of Electric Vehicles on Traffic Assignment and Carbon Emission for Road Network: Modeling and Analysis


    Contributors:
    Zhaolei Zhang (author) / Wei Hao (author) / Ye Gong (author) / Wei Wu (author) / Ying Chen (author) / Shuibo Lu (author)


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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