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Fine-grained analysis on fuel-consumption and emission from vehicles trace

文献类型：期刊论文

作者Kan, Zihan2; Tang, Luliang2; Kwan, Mei-Po1,3; Ren, Chang2; Liu, Dong3; Pei, Tao4; Liu, Yu5; Deng, Min6; Li, Qingquan7
刊名JOURNAL OF CLEANER PRODUCTION
出版日期2018-12-01
卷号203页码:340-352
关键词Fuel consumption Emissions Big data Activity analysis GPS trace CMEM
ISSN号0959-6526
DOI10.1016/j.jclepro.2018.08.222
英文摘要Traffic-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.
WOS关键词GEOGRAPHICAL INFORMATION-SYSTEMS ; SPACE-TIME PRISM ; TRAVEL PATTERNS ; BIG DATA ; CHINA ; SHANGHAI ; MODELS
资助项目National 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
WOS研究方向Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000447568700026
出版者ELSEVIER SCI LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/52789]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Univ Utrecht, Dept Human Geog & Spatial Planning, NL-3508 TC Utrecht, Netherlands
2.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China
3.Univ Illinois, Dept Geog & Geog Informat Sci, Urbana, IL 61801 USA
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
推荐引用方式
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.

入库方式： OAI收割

来源：地理科学与资源研究所

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