基于车辆身份检测数据的车辆出行排放知识图谱研究

Abstract
Figure/Table
References
Related Citation (1)

Download: PDF (4850 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract To realize the refined representation and mining of individual vehicle travel and emission behaviour, a vehicle travel emission knowledge graph was established based on multi-dimensional traffic big data such as trajectories, technical parameters, and emission trajectories of all individual vehicles in the central urban area of Xuancheng City. Spatiotemporal correlations among vehicle, road, travel and emission information were intuitively represented by knowledge graph, achieving the fine mining of multi-scale travel characteristics of individual vehicles on different days, different time periods, and different road sections. A private passenger car was cited as an example, its hourly nodes connected to travel nodes on Monday and Wednesday were 7:00, 8:00 and 17:00, and hourly nodes connected to travel on Friday and non-working days were obviously random. The number of road nodes connected to travel on Monday and Wednesday were few and basically same, and the proportions of mileage on Xuanshui Road, Zhaoting North Road and Zhaoting South Road were 63%~68%, while the road nodes connected to travel on Fridays and non-working days were more dispersed. Through the associated retrieval of travel information and emission information nodes, fine identification and traceability analysis of the spatiotemporal characteristics of individual vehicle travel emissions could be realized. The retrieval results of the example vehicle showed that:the daily CO emission of the vehicle on Monday was 1.2g, which was 2.5times that of Saturday. During the morning peak hour (7:00), when the vehicle travelled on the busy road section, with the low level of vehicle speed, its emission intensity was relatively high.

Key words： individual travel vehicle emission knowledge graph fine traceability

Received: 24 May 2022

PACS:	X51
	X734.2

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHAO Yong-ming
	DING Hui
	LIU Yong-hong
	WANG Qing-gang

Cite this article:

ZHAO Yong-ming,DING Hui,LIU Yong-hong等. Research on vehicle travel emission knowledge graph based on vehicle identification data[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(12): 5598-5609.

URL:

http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I12/5598

[1]	Deng F Y, Lv Z F, Qi L J, et al. A big data approach to improving the vehicle emission inventory in China[J]. Nature Communications, 2020,11(1):2801.
[2]	Maes A D S, Hoinaski L, Meirelles T B, et al. A methodology for high resolution vehicular emissions inventories in metropolitan areas:Evaluating the effect of automotive technologies improvement[J]. Transportation Research Part D:Transport and Environment, 2019, 77:303-319.
[3]	中华人民共和国环境保护部.中国移动源环境管理年报(2020年)[R]. 北京:2020. Ministry of Ecology and Environment of the People's Republic of China. China mobile source environmental management annual report(2020)[R]. Beijing, 2020.
[4]	Yu Z, Li W C, Liu Y H, et al. Quantification and management of urban traffic emissions based on individual vehicle data[J]. Journal of Cleaner Production, 2021,328:129386.
[5]	Ding H, Cai M, Lin X F, et al. RTVEMVS:Real-time modeling and visualization system for vehicle emissions on an urban road network[J]. Journal of Cleaner Produc-tion, 2021,309:127166.
[6]	Zhan X Y, Li R M, Ukkusuri S V. Link-based traffic state estimation and prediction for arterial networks using license-plate recognition data[J]. Transportation Research Part C:Emerging Technologies, 2020,117:102660.
[7]	An C, Wu C. Traffic big data assisted V2X communications toward smart transportation[J]. Wireless Networks, 2020,26(3):1601-1610.
[8]	冯慧芳,柏凤山,徐有基.基于轨迹大数据的城市交通感知和路网关键节点识别[J]. 交通运输系统工程与信息, 2018,3(18):42-47. Feng H F, Bai F S, Xu Y J. Urban traffic perception and critical node identification of road network based on trajectory big data[J]. Journal of Transportation Systems Engineering and Information Technology, 2018,3(18):42-47.
[9]	Hu C H, Fan W C, Zeng E, et al. Digital twin-assisted Real-time traffic data prediction method for 5G-enabled internet of vehicles[J]. IEEE Transactions on Industrial Informatics, 2022,18(4):2811-2819.
[10]	Celes C, Boukerche A, Loureiro A A F. Mobility trace analysis for intelligent vehicular networks[J]. ACM Computing Surveys, 2021, 54(3):1-38.
[11]	Yao W B, Zhang M L, Jin S, et al. Understanding vehicles commuting pattern based on license plate recognition data[J]. Transportation Research Part C:Emerging Tech-nologies, 2021,128:103142.
[12]	Najafabadi M M, Villanustre F, Khoshgoftaar T M, et al. Deep learning applications and challenges in big data an-alytics[J]. Journal of Big Data, 2015,2(1):1-21.
[13]	王瑛琦.面向关系数据库的关键字查询技术研究[D]. 哈尔滨:哈尔滨工程大学, 2018. Wang Y Q. Research on keyword search in relational database[D]. Harbin:Harbin Engineering University, 2018.
[14]	张帜.Neo4j权威指南[M]. 北京:清华大学出版社, 2017:10-12. Zhang Z. The definitive guide to Neo4j[M]. Beijing:Tsinghua University Press, 2017.
[15]	徐增林,盛泳潘,贺丽荣,等.知识图谱技术综述[J]. 电子科技大学学报, 2016,45(4):589-606. Xu Z L, Sheng Y P, He L R, et al. Review on knowledge graph techniques[J]. Journal of University of Electronic Science and Technology of China, 2016,45(4):589-606.
[16]	刘峤,李杨,段宏,等.知识图谱构建技术综述[J]. 计算机研究与发展, 2016,3(53):582-600. Liu Q, Li Y, Duan H, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016,3(53):582-600.
[17]	Zhou G, Chen F. Urban congestion areas prediction by combining knowledge graph and deep spatio temporal convolutional neural network[C]. Guilin:International Conference on Electromechanical Control Technology and Transportation, 2019:105-108.
[18]	Zhang N Y, Deng S M, Chen H J, et al. Structured knowledge base as prior knowledge to improve urban data analysis[J]. ISPRS International Journal of Geo-Information, 2018,7(7):264.
[19]	邹易呈.基于多源数据融合的交通知识图谱构建及应用[D]. 大连:大连理工大学, 2020. Zhou Y C. Construction and application of traffic knowledge graph based on multi-source data Fusion[D]. Dalian:Dalian University of Technology, 2020.
[20]	邹兵,余志,何兆成,等.基于车辆身份检测数据的城市道路使用特征[J]. 中山大学学报(自然科学版), 2022,61(3):132-139. Zou B, Yu Z, He Z C, et al. The characteristics of urban road usage using automatic vehicle identification data[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2022,61(3):132-139.
[21]	Zhu Y T, He Z C, Sun W W. Network-wide link travel time inference using trip-based data from automatic vehicle identification detectors[J]. IEEE Transactions on Intelligent Transportation Systems, 2019,21(6):2485-2495.
[22]	林颖,丁卉,刘永红,等.基于车辆身份检测数据的单车排放轨迹研究[J]. 中国环境科学, 2019,39(12):4929-4940. Lin Y, Ding H, Liu Y H, et al. Research on vehicle emission trajectory based on vehicle identification data[J]. China Environmental Science, 2019,39(12):4929-4940.
[23]	孙世达,金嘉欣,吕建华,等.基于精细化年均行驶里程建立机动车排放清单[J]. 中国环境科学, 2020,40(5):2018-2029. Sun S D, Jin J X, Lv J H, et al. Developing vehicle emission inventory based on refined annual average vehicle kilometers travelled.[J]. China Environmental Science, 2020,40(5):2018-2029.
[24]	Yang W, Yu C Y, Yuan W, et al. High-resolution vehicle emission inventory and emission control policy scenario analysis, a case in the Beijing-Tianjin-Hebei (BTH) Region, China[J]. Journal of Cleaner Production, 2018,203:530-539.
[25]	Zhou Z H, Tan Q W, Liu H F, et al. Emission characteristics and high-resolution spatial and temporal distribution of pollutants from motor vehicles in Chengdu, China[J]. Atmospheric Pollution Research, 2018,10:749-758.
[26]	Zhang S J, Niu T L, Wu Y, et al. Fine-grained vehicle emission management using intelligent transportation system data[J]. Environmental Pollution, 2018,241:1027-1037.
[27]	Li B, Yu Z, Sun W W, et al. Relieving the congestion around a school via automatic vehicle identification technology-based traffic measures[J]. Journal of advanced transportation, 2021,2021:5525580.
[28]	GB/T 51328-2018城市综合交通体系规划标准[S]. GB/T 51328-2018 Standard for urban comprehensive transport system planning[S].
[29]	GB/T 33171-2016城市交通运行状况评价规范[S]. GB/T 33171-2016 Specification for urban traffic performance evaluation[S].
[30]	王一峰.基于聚类算法的城市车辆出行数据挖掘与特征分析[D]. 成都:西华大学, 2021. Wang Y F. Data mining and characteristics analysis of city vehicles based on clustering algorithm[D]. Chengdu:Xihua University, 2021.
[31]	毋帆.基于车牌识别数据的通勤路径选择行为与协同控制优化研究[D]. 济南:山东大学, 2020. Wu F. Research of commuter route choice Behavior and coordinative control optimization based license plate data[D]. Jinan:Shandong University, 2020.