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Intelligent Identification of High Emission Road Segment Based on Large-Scale Traffic Datasets

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AI and Analytics for Smart Cities and Service Systems (ICSS 2021)

Part of the book series: Lecture Notes in Operations Research ((LNOR))

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Abstract

The primary data, such as traffic flow, vehicle type composition, and road network speed in Beijing, is acquired through model simulation and investigation statistics. A dynamic calculation method of high-resolution vehicle emission inventory was constructed with ArcGIS as the platform based on the road network traffic information and vehicle emission factors. Meanwhile, the intelligent supervision and identification of high-emission road sections were carried out based on the spatiotemporal emission distribution characteristics of the road network. The results show that the spatial distribution of pollutant emission intensity decreases from the main urban area to the suburbs. The emission on ring roads and expressways presents a linear and radiant distribution pattern because of the large traffic flow. Besides, the emission intensity of pollutants shows a noticeable diurnal trend as high during the day and low at night, and mainly concentrates in the morning and evening peak hours. The dynamic road network emission can intelligently identify the high emission road segments in real-time, track the high-emission road segments, and provide critical technical means for traffic environment management.

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Acknowledgements

This work was supported by the Project of Beijing Science and Technology Commission (Z181100005418003, Z191100009119014).

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Authors and Affiliations

  1. School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China

    Baoxian Liu & Yanyan Yang

  2. Insights Value Technology, Beijing, 100071, China

    Gang Zhou, Zilong Huang, Qiongqiong Gong, Kexu Zou & Wenjun Yin

  3. Beijing Key Laboratory of Airborne Particulate Matter Monitoring Technology, Beijing Environmental Monitoring Center, Beijing, 100048, China

    Baoxian Liu

Authors
  1. Baoxian Liu
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  2. Gang Zhou
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  3. Yanyan Yang
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  4. Zilong Huang
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  5. Qiongqiong Gong
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  6. Kexu Zou
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  7. Wenjun Yin
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Corresponding author

Correspondence to Zilong Huang .

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Editors and Affiliations

  1. Division of Engineering and Information Science, Pennsylvania State University, Malvern, PA, USA

    Robin Qiu

  2. Faculty of Information (Cross-appointed to Computer Science), Faculty Affiliate, Schwartz Reisman Institute, University of Toronto, Toronto, ON, Canada

    Kelly Lyons

  3. Department of Supply Chain Management, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

    Weiwei Chen

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Liu, B. et al. (2021). Intelligent Identification of High Emission Road Segment Based on Large-Scale Traffic Datasets. In: Qiu, R., Lyons, K., Chen, W. (eds) AI and Analytics for Smart Cities and Service Systems. ICSS 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-030-90275-9_9

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