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

In: AI and Analytics for Smart Cities and Service Systems

Author

Listed:
  • Baoxian Liu

    (Tsinghua University
    Beijing Environmental Monitoring Center)

  • Gang Zhou

    (Insights Value Technology)

  • Yanyan Yang

    (Tsinghua University)

  • Zilong Huang

    (Insights Value Technology)

  • Qiongqiong Gong

    (Insights Value Technology)

  • Kexu Zou

    (Insights Value Technology)

  • Wenjun Yin

    (Insights Value Technology)

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.

Suggested Citation

  • Baoxian Liu & Gang Zhou & Yanyan Yang & Zilong Huang & Qiongqiong Gong & Kexu Zou & Wenjun Yin, 2021. "Intelligent Identification of High Emission Road Segment Based on Large-Scale Traffic Datasets," Lecture Notes in Operations Research, in: Robin Qiu & Kelly Lyons & Weiwei Chen (ed.), AI and Analytics for Smart Cities and Service Systems, pages 103-113, Springer.
    • Handle: RePEc:spr:lnopch:978-3-030-90275-9_9
    DOI: 10.1007/978-3-030-90275-9_9
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