Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

doi:10.1007/s11633-020-1271-y

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	Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories
	Zhen-Yi Zhao 4
刊名	International Journal of Automation and Computing
	2021
卷号	18 期号:2 页码:219-232
关键词	Vehicle emission prediction spatiotemporal gragh convolution GPS trajectories motor vehicle emission simulator (MOVES) model feature sharing
ISSN号	1476-8186
DOI	10.1007/s11633-020-1271-y
英文摘要	With the rapid increase of the amount of vehicles in urban areas, the pollution of vehicle emissions is becoming more and more serious. Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making. Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning. Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions, however, they are not effective and efficient enough in the combination and utilization of different inputs. To address this issue, we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator (MOVES) model. Specifically, we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms. These matrices can reflect the attributes related to the traffic status of road networks such as volume, speed and acceleration. Then, our multi-channel spatiotemporal network is used to efficiently combine three key attributes (volume, speed and acceleration) through the feature sharing mechanism and generate a precise prediction of them in the future period. Finally, we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states, road networks and the statistical information of urban vehicles. We evaluate our model on the Xi′an taxi GPS trajectories dataset. Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44018]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China 2.Institute of Advanced Technology, University of Science and Technology of China, Hefei 230088, China 3.Department of Automation and State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China 4.Department of Automation, University of Science and Technology of China, Hefei 230026, China
推荐引用方式 GB/T 7714	Zhen-Yi Zhao. Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories[J]. International Journal of Automation and Computing,2021,18(2):219-232.
APA	Zhen-Yi Zhao.(2021).Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories.International Journal of Automation and Computing,18(2),219-232.
MLA	Zhen-Yi Zhao."Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories".International Journal of Automation and Computing 18.2(2021):219-232.