Abstract
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.
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Acknowledgments
This work was supported by National Key R&D Program of China under Grant (Nos. 2018AAA0100800, 2018YFE0106800), National Natural Science Foundation of China (Nos. 61725304, 61673361 and 62033012), Major Special Science and Technology Project of Anhui, China (No. 912198698036).
Data source: Didi Chuxing GAIA Initiative (https://gaia.didichuxing.com).
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Zhen-Yi Zhao received the B. Sc. degree in automation from University of Science and Technology of China, China in 2017. She is currently a Ph. D. degree candidate of control science and engineering in Department of Automation, University of Science and Technology of China, China.
Her research interests include deep learning, urban computing, intelligent transportation, machine learning and data mining.
Yang Cao received the B. Sc. and the Ph. D. degrees in information engineering from Northeastern University, China in 1999 and 2004, respectively. Since 2004, he has been with Department of Automation, University of Science and Technology of China, where he is currently an associate professor. He is a member of the IEEE Signal Processing Society.
His research interests include machine learning and computer vision.
Yu Kang received the Ph. D. degree in control theory and control engineering from University of Science and Technology of China, China in 2005. From 2005 to 2007, he was a post-doctoral fellow with Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China. He is currently a professor with Department of Automation, University of Science and Technology of China, China.
His research interests include adaptive/robust control, variable structure control, mobile manipulator, and Markovian jump systems.
Zhen-Yi Xu received the B. Sc. degree in automation from the Nanjing Institute of Technology, China in 2015, and the Ph. D. degree of control science and engineering from Department of Automation, University of Science and Technology of China, China in 2020. He is currently a post-doctoral fellow with School of Information Science, University of Science and Technology of China, China.
His research interests include deep learning, urban computing, intelligent transportation, machine learning and data mining.
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Zhao, ZY., Cao, Y., Kang, Y. et al. Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories. Int. J. Autom. Comput. 18, 219–232 (2021). https://doi.org/10.1007/s11633-020-1271-y
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DOI: https://doi.org/10.1007/s11633-020-1271-y