Spatiotemporal Characteristics Mining of CO2 Emissions from Residential Trips Based on Trajectory Matching of Multi-fuel Taxis
As an important complement to public transport travel,the fuel consumption and CO2 emission patterns generated by taxi op-erations are consistent with the travel footprint of urban residents.In the context of public transport electrification,it is of great practical significance to accurately calculate the CO2 emissions of taxi trips with various fuel types and explore their spatio-temporal character-istics in different urban areas to understand the spatial characteristics of CO2 emissions from urban residents'trips and realize urban CO2 emission reduction.In this paper,the trajectory data of taxis in Lanzhou city are used to accurately identify the trajectory and path of resident taxis by using hidden Markov model trajectory matching.The CO2 emissions of taxis with gasoline,CNG and oil-gas hybrid fuel types are calculated by using COPERT model,and the spatio-temporal characteristics of CO2 emissions from residents'travel are analyzed at different spatio-temporal scales.The results show that,due to the reduction of the number of gasoline and oil vehicles in the electrification process,the CO2 emission of the three fuel types of taxis is the highest,and that of the CNG vehicles is the lowest,and the CO2 emission of the gasoline vehicles is higher in the morning and evening peak hours on working days than in non-working days,and the CO2 emission is lower in the morning hours.The hot spots of CO2 emission are mainly concentrated in the vicinity of transportation hubs,business districts and residential areas,and decline from the central areas of cities in Lanzhou along the belt to the outskirts of the city.The high emissions in these areas reflect the travel demand and activity pattern of urban residents.The conclusions of this study can be used as the basis for accurate calculation of greenhouse gas emissions of multi-fuel taxis and research on emission reduction paths of urban public transportation,and also provide a basis for mining the spatial-temporal characteristics of carbon emis-sions of residents'travel and promoting low-carbon travel of urban transportation.
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