IGSNRR OpenIR
Fine-grained analysis on fuel-consumption and emission from vehicles trace
Kan, Zihan1; Tang, Luliang1; Kwan, Mei-Po2,3; Ren, Chang1; Liu, Dong2; Pei, Tao4; Liu, Yu5; Deng, Min6; Li, Qingquan7
2018-12-01
Source PublicationJOURNAL OF CLEANER PRODUCTION
ISSN0959-6526
Volume203Pages:340-352
Corresponding AuthorTang, Luliang(tll@whu.edu.cn)
AbstractTraffic-related fuel consumption and emissions pose a severe problem with adverse impact on human health and urban sustainability. GPS trajectory data can provide useful insights into the quantities and distributions of fuel consumption and emissions. Previous research has primarily focused on understanding the spatiotemporal distributions of fuel consumption and emissions with GPS trajectory data, but has not paid adequate attention to estimation accuracy. Thus, this study proposes a method that estimates vehicular fuel consumption and emissions at a fine-grained level based on analysis of vehicles' mobile activities, stationary activities with engine-on, and stationary activities with engine-off. Using the analytical framework of space-time paths in time geography, this study first builds space-time paths of individual vehicles, extracts moving parameters and analyzes the activities from each space-time path segment (SIPS). Based on the activity analysis, we then estimate fuel consumption and emissions using a microscopic model (CMEM), and distinguish between the cold-start phases and the hot phases in the space-time paths. In the case study, the fuel consumption and emissions for individual trajectories and a road network were estimated and analyzed. The distribution of activity-related fuel consumption was also explored. The effectiveness of the proposed methodology is illustrated using three datasets that were collected from vehicles with various types of engines, with estimation accuracy of over 90%. (C) 2018 Elsevier Ltd. All rights reserved.
KeywordFuel consumption Emissions Big data Activity analysis GPS trace CMEM
DOI10.1016/j.jclepro.2018.08.222
WOS KeywordGEOGRAPHICAL INFORMATION-SYSTEMS ; SPACE-TIME PRISM ; TRAVEL PATTERNS ; BIG DATA ; CHINA ; SHANGHAI ; MODELS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Plan of China[2017YFB0503604] ; National Key Research and Development Plan of China[2016YFE0200400] ; National Natural Science Foundation of China[41671442] ; National Natural Science Foundation of China[41571430] ; National Natural Science Foundation of China[41271442] ; National Natural Science Foundation of China[41529101] ; Joint Foundation of Ministry of Education of China[6141A02022341] ; John Simon Guggenheim Memorial Foundation Fellowship
Funding OrganizationNational Key Research and Development Plan of China ; National Natural Science Foundation of China ; Joint Foundation of Ministry of Education of China ; John Simon Guggenheim Memorial Foundation Fellowship
WOS Research AreaScience & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
WOS SubjectGREEN & SUSTAINABLE SCIENCE & TECHNOLOGY ; Engineering, Environmental ; Environmental Sciences
WOS IDWOS:000447568700026
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52790
Collection中国科学院地理科学与资源研究所
Corresponding AuthorTang, Luliang
Affiliation1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China
2.Univ Illinois, Dept Geog & Geog Informat Sci, Urbana, IL 61801 USA
3.Univ Utrecht, Dept Human Geog & Spatial Planning, NL-3508 TC Utrecht, Netherlands
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
5.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China
6.Cent S Univ, Dept Surveying & Geoinformat, Changsha 410084, Hunan, Peoples R China
7.Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Kan, Zihan,Tang, Luliang,Kwan, Mei-Po,et al. Fine-grained analysis on fuel-consumption and emission from vehicles trace[J]. JOURNAL OF CLEANER PRODUCTION,2018,203:340-352.
APA Kan, Zihan.,Tang, Luliang.,Kwan, Mei-Po.,Ren, Chang.,Liu, Dong.,...&Li, Qingquan.(2018).Fine-grained analysis on fuel-consumption and emission from vehicles trace.JOURNAL OF CLEANER PRODUCTION,203,340-352.
MLA Kan, Zihan,et al."Fine-grained analysis on fuel-consumption and emission from vehicles trace".JOURNAL OF CLEANER PRODUCTION 203(2018):340-352.
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