Adaptive-Grained CO2 Emission Estimation Using Spatiotemporal Cells with Heterogeneous Vehicles

Jan 2021

This study presents an adaptive-grained method for estimating CO2 emissions using spatiotemporal cells, focusing on heterogeneous vehicles. It leverages PEMS technology to collect emissions data and applies DNN and LSTM models for carbon emission prediction in varying grid sizes, aimed at improving accuracy and adaptability in different urban scenarios.

  • The research introduces a novel approach for estimating CO2 emissions from heterogeneous vehicles in urban areas. Utilizing Portable Emission Measurement Systems (PEMS) for data collection, it proposes an adaptive-grained method using spatiotemporal cells. Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) models are applied to predict emissions at different scales, providing insights into environmental impacts of urban traffic and offering a scalable solution for city planners and environmental researchers.
    • CO2 Emissions
    • Heterogeneous Vehicles
    • PEMS Technology
    • DNN
    • LSTM
    • + more
    We use cookies and similar technologies to provide certain features, enhance the user experience and deliver content that is relevant to your interests. Depending on their purpose, analysis and marketing cookies may be used in addition to technically necessary cookies. By clicking on "Agree and continue", you declare your consent to the use of the aforementioned cookies. you can make detailed settings or revoke your consent (in part if necessary) with effect for the future. For further information, please refer to ourPrivacy Policy.

    Early to bed and early to rise, makes a man healthy, wealthy, and wise. ——Benjamin Franklin

    © 2023 Yuyang Wang's Site. Powered by React.js