Estimating And Analyzing Spatiotemporal Patterns Of Vehicle CO₂ Emissions In Urban Road Based On GPS Data

Climate warming caused by the increase of carbon dioxide emission is an important factor that affects and restricts the sustainable development of society.Transportation activity is one of the three main sources of CO₂ emissions from human activities.As the main place of human activities,cities undertake most of the transportation activities.Road vehicle is the main mode of urban traffic,and contributes most of the emissions of urban traffic.Therefore,it plays an important role in energy conservation and emission reduction in the transportation industry to find more accurate methods to estimate urban vehicle emissions and their distribution characteristics.In the aspect of traffic emission estimation at the urban level,the traditional method focuses on the estimation of total emission of different types of traffic activities by using energy consumption statistics data,ignoring the imbalance of traffic emission distribution in time and space.The rise of big data technology makes it possible to explore the spatial and temporal pattern of urban traffic emissions using taxi GPS data.This paper introduces a method to estimate the CO₂ emission of urban road vehicles based on the GPS data of taxis.Starting from the source of urban traffic emissions,this paper uses the GPS data of taxis and road traffic survey data to obtain the road traffic operating information.Combined with the existing emission model,this paper proposes a method with consideration of fuel life cycle to estimate the dynamic CO₂ emission of vehicles with different fuel types based on the road segments.Taking Xi’an as an example,the proposed method for calculating CO₂ emissions of road vehicles is used to measure the CO₂ emissions of different road types in the third ring road of Xi’an and to characterize its distribution.Then,the fuzzy C-means clustering method and spatial autocorrelation model were used to further explore the spatial and temporal distribution pattern of road vehicle CO₂ emissions on the main road in Xi’an,and 12 potential independent variables such as road attributes,urban land use characteristics,built environment and transportation facilities supply were chosen to establish a spatial autoregressive model to analyze the influencing factors of road traffic emissions.The results show that:（1）There is an obvious regularity in the temporal and spatial distribution of CO₂ emission factors of road vehicles.Among the emission factors of different types of roads,the expressway is the lowest,and the arterial road is second lowest,the branch road is the highest.（2）There are significant differences in CO₂emissions contributed by different types of roads,and the arterial roads contribute most of the emissions;the distribution of the emissions in a day between taxis and private cars is significantly different,after 21:00pm,the emissions of private cars decrease obviously,while the taxi will be a small peak at21:00-23:00.（3）In the study area,the road segments with higher emission factors are mainly concentrated in the center of the city and the high-tech zone in the southwest of the city,while the lower road segments are mainly distributed in the area with a lower density of the road network.（4）There is a clear positive correlation between the emissions of road segments,and the high-emission segments in study area are mainly concentrated on the First Ring Road,the Second Ring Road,and the North-South central axis.（5）Short segment,high land use diversity,relatively high commercial and public land use will produce more emissions;during the peak hours on weekdays,one-way road has lower CO₂ emissions than two-way road.The CO₂ emission calculation method of urban road vehicles proposed in this study can dynamically estimate urban road vehicle emissions from the road segment level,achieve more detailed mining of the impact of vehicle traffic emissions on the urban environment from different spatial and temporal dimensions,and help to formulate effective traffic emission reduction strategies.It is of great significance to carry out sustainable urban transportation planning and management.