Exploring Spatio-Temporal Carbon Emission Across Passenger Car Trajectory Data

Carbon emissions caused by passenger cars in cities are essentially responsible for severe climate change and serious environmental problems. Exploring carbon emissions from passenger cars helps to control urban pollution and achieve urban sustainability. However, it is a challenging task to foresee the spatio-temporal distribution of carbon emission from passenger cars, as the following technical issues remain.
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) Vehicle carbon emissions contain complex spatial interactions and temporal dynamics. How to collaboratively integrate such spatial-temporal correlations for carbon emission prediction is not yet resolved.
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) Given the mobility of passenger cars, the hidden dependencies inherent in traffic density are not properly addressed in predicting carbon emissions from passenger cars. To tackle these issues, we propose a Collaborative Spatial-temporal Network (CSTNet) for implementing carbon emissions prediction by using passenger car trajectory data. Within the proposed method, we devote to extract collaborative properties that stem from a multi-view graph structure together with parallel input of carbon emission and traffic density. Then, we design a spatial-temporal convolutional block for both carbon emission and traffic density, which constitutes of temporal gate convolution, spatial convolution and temporal attention mechanism. Following that, an interaction layer between carbon emission and traffic density is proposed to handle their internal dependencies, and further model spatial relationships between the features. Besides, we identify several global factors and embed them for final prediction with a collaborative fusion. Experimental results on the real-world passenger car trajectory dataset demonstrate that the proposed method outperforms the baselines with a roughly 7%-11% improvement.