气候变化领域集成服务门户(CCPortal): Computing the CO2 Emissions of Taxi Trajectories and Exploring Their Spatiotemporal Patterns in Wuhan City [武汉市出租车轨迹二氧化碳排放的时空模式分析]

CCPortal

DOI	10.13203/j.whugis20170334
	Computing the CO2 Emissions of Taxi Trajectories and Exploring Their Spatiotemporal Patterns in Wuhan City [武汉市出租车轨迹二氧化碳排放的时空模式分析]
	Jia T.; Li Q.; Ma C.; Li Y.
发表日期	2019
ISSN	16718860
起始页码	1115
结束页码	1123
卷号	44 期号:8
英文摘要	The intensive usage of vehicles has led to many urban problems including the traffic jams and the environmental pollutions. To handle these problems, most previous studies have used macro-based models to estimate and analyze the CO2 emission inventories, but very few studies have focused on computing vehicle CO2 emissions using a micro-based model. This paper presents an in-depth study on computing the CO2 emissions from taxi trajectory data and further analyzing their spatiotemporal patterns from three aspects. Taking the Wuhan city as a case study, this paper uses the comprehensive modal emission model to quantitatively compute the CO2 emissions from the taxi trajectory, and statistical results suggest that CO2 emissions of the entire city has experienced a remarkable regularity in times of the day and days of the week. Specifically, the spatiotemporal patterns of CO2 emissions from taxi trajectories are analyzed from three different aspects. Firstly, a spatial clustering algorithm is used to aggregate the taxi trajectory points with high CO2 emissions, and the number of clusters displayed a regular change pattern. For example, the cluster number is gradually increased from workdays to weekends or holidays and from normal time period to peak time period in one day. Secondly, the data field model is used to allocate the CO2 emissions of taxi trajectory to the individual streets, and the emission from street network exhibited a remarkable distribution in space and time. For instance, streets with high CO2 emission tend to appear in the morning peak time period of workdays and evening peak time period of weekends or holidays, and they are spatially adjacent to the universities, railway stations, or central business districts. Thirdly, a spatiotemporal autocorrelation technique is adopted to examine the concentration of taxi trajectory emissions in space, and different regions are positively auto-correlated with each other in both times of the day and days of the week. These results can help to the proposal of efficient CO2 emissions reduction strategies, and they can provide guidance for taxis management, low-carbon traveling, and so on., Research and Development Office of Wuhan University. All right reserved.
英文关键词	CO2 emissions; Comprehensive modal emission model; Spatiotemporal pattern; Taxi trajectory
scopus关键词	Carbon dioxide; Clustering algorithms; Emission control; Tantalum compounds; Taxicabs; Traffic congestion; Trajectories; Autocorrelation techniques; Central business districts; CO2 emissions; Emission model; Environmental pollutions; Number of clusters; Spatiotemporal patterns; Trajectory points; Air pollution; algorithm; carbon emission; emission inventory; GIS; spatiotemporal analysis; taxi transport; trajectory; urban transport; China; Hubei; Wuhan
来源期刊	Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/176374
作者单位	School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China; Hubei Institute of Surveying and Mapping Engineering, Wuhan, 430072, China
推荐引用方式 GB/T 7714	Jia T.,Li Q.,Ma C.,等. Computing the CO2 Emissions of Taxi Trajectories and Exploring Their Spatiotemporal Patterns in Wuhan City [武汉市出租车轨迹二氧化碳排放的时空模式分析][J],2019,44(8).
APA	Jia T.,Li Q.,Ma C.,&Li Y..(2019).Computing the CO2 Emissions of Taxi Trajectories and Exploring Their Spatiotemporal Patterns in Wuhan City [武汉市出租车轨迹二氧化碳排放的时空模式分析].Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University,44(8).
MLA	Jia T.,et al."Computing the CO2 Emissions of Taxi Trajectories and Exploring Their Spatiotemporal Patterns in Wuhan City [武汉市出租车轨迹二氧化碳排放的时空模式分析]".Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University 44.8(2019).