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Atmosphere
Volume 16
Issue 10
10.3390/atmos16101213
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

High-Resolution Traffic Flow Prediction and Vehicle Emission Inventory Estimation for Chinese Cities Using Geo-Spatial Data of Jinan City, China

by
Xuejun Yan
1,
Qi Yang
2,
Jingyang Fan
2,
Ziyuan Cai
2,
Pan Wang
2,
Xiuli Zhang
3,
Hengzhi Wang
2,
Chenxi Zhu
2,
Dongquan He
2 and
Chunxiao Hao
4,5,*
1
Jinan Ecological and Environment Monitoring Center of Shandong Province, Jinan 250000, China
2
Beijing Smart Green Transport Research Centre, Beijing 100022, China
3
Energy Innovation: Policy and Technology, LLC., Energy Innovation 98 Battery Street, Suite 202, San Francisco, CA 94111, USA
4
Key Laboratory for Vehicle Emission Control and Simulation of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
5
National Laboratory of Automotive Performance & Emission Test, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(10), 1213; https://doi.org/10.3390/atmos16101213
Submission received: 15 August 2025 / Revised: 10 September 2025 / Accepted: 17 October 2025 / Published: 20 October 2025
(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))
Versions Notes

Abstract

Motor vehicle emissions are a major air quality concern in Chinese cities. However, traditional population-based emission inventory methods fail to capture the spatial and temporal variations in emissions for effective policy design. This study proposes a high-resolution approach for traffic flow prediction and vehicle emission inventory estimation, using Jinan City, China, as a case study. We leverage multi-source geospatial data and employ a two-fold random forest model to predict hourly traffic flow at a road-segment level. Speed-aligned emission factors were then combined with these data to calculate hourly and road-level vehicle emission estimates. Compared to traditional methods, our approach offers substantial improvements: (1) improved spatiotemporal resolution; (2) enhanced accuracy of traffic flow prediction; and (3) support for more effective vehicle emission control strategies. Results show that heavy-duty vehicles, particularly freight trucks operating on inter-regional corridors through Jinan, contribute 78% more to NOX emissions than local light-duty vehicles. These transient emissions are typically overlooked in static inventories but constitute a significant source of urban pollution. This study offers valuable insights for combining geospatial data and machine learning to improve the accuracy and resolution of vehicle emission inventories, supporting urban air quality policy and planning.
Keywords: vehicle emission inventory; traffic flow prediction; geo-spatial data; spatial analysis; random forest vehicle emission inventory; traffic flow prediction; geo-spatial data; spatial analysis; random forest

Share and Cite

MDPI and ACS Style

Yan, X.; Yang, Q.; Fan, J.; Cai, Z.; Wang, P.; Zhang, X.; Wang, H.; Zhu, C.; He, D.; Hao, C. High-Resolution Traffic Flow Prediction and Vehicle Emission Inventory Estimation for Chinese Cities Using Geo-Spatial Data of Jinan City, China. Atmosphere 2025, 16, 1213. https://doi.org/10.3390/atmos16101213

AMA Style

Yan X, Yang Q, Fan J, Cai Z, Wang P, Zhang X, Wang H, Zhu C, He D, Hao C. High-Resolution Traffic Flow Prediction and Vehicle Emission Inventory Estimation for Chinese Cities Using Geo-Spatial Data of Jinan City, China. Atmosphere. 2025; 16(10):1213. https://doi.org/10.3390/atmos16101213

Chicago/Turabian Style

Yan, Xuejun, Qi Yang, Jingyang Fan, Ziyuan Cai, Pan Wang, Xiuli Zhang, Hengzhi Wang, Chenxi Zhu, Dongquan He, and Chunxiao Hao. 2025. "High-Resolution Traffic Flow Prediction and Vehicle Emission Inventory Estimation for Chinese Cities Using Geo-Spatial Data of Jinan City, China" Atmosphere 16, no. 10: 1213. https://doi.org/10.3390/atmos16101213

APA Style

Yan, X., Yang, Q., Fan, J., Cai, Z., Wang, P., Zhang, X., Wang, H., Zhu, C., He, D., & Hao, C. (2025). High-Resolution Traffic Flow Prediction and Vehicle Emission Inventory Estimation for Chinese Cities Using Geo-Spatial Data of Jinan City, China. Atmosphere, 16(10), 1213. https://doi.org/10.3390/atmos16101213

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