Visual Analytics of CO \(_2\) Emissions from Individuals’ Daily Travel Based on Large-Scale Taxi Trajectories

Understanding the patterns of traffic-related carbon dioxide (CO \(_2\) ) emissions from different trip purposes is of great significance for the development of low-carbon transportation. However, most existing research ignores the traffic-related CO \(_2\) emissions from daily trip. Accurately inferring trip purposes is a prerequisite for analyzing the patterns of traffic-related CO \(_2\) emissions from daily trip. The existing research on inferring trip purposes has been proven effective, but it ignores door-to-door service (DTD) and the time-varying characteristics of the attractiveness of Points of Interest (POIs). In this paper, we propose a Bayesian-based method to infer trip purposes. It identifies DTD through spatial relation operations and constructs the dynamic function of POIs attractiveness using kernel density estimation (KDE). A visual analysis system is also developed to help users explore the spatio-temporal patterns of traffic-related CO \(_2\) emissions from daily trip. Finally, the effectiveness of the method and the system is verified through case study based on real data and positive feedback from experts.