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A method for delineating traffic low emission control zone based on deep learning and multi-objective optimization.

Shuqi Xue1,2, Hong Zou3, Qiang Feng4

  • 1Engineering Research Center of Road Transportation Decarbonization, Ministry of Education, Chang'an University, Xi'an, 710018, People's Republic of China.

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Summary
This summary is machine-generated.

This study introduces an advanced deep learning model to analyze PM2.5 pollution for optimizing traffic low emission control zones (TLEZ). The findings offer a data-driven framework for creating effective Low, Ultra-Low, and Zero Emission Zones.

Keywords:
Environmental managementHuman healthPollution controlTraffic congestionTraffic pollution

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Area of Science:

  • Environmental Science
  • Urban Planning
  • Data Science

Background:

  • Current traffic low emission control zones (TLEZ) face implementation challenges.
  • Accurate analysis of PM2.5 concentration is crucial for effective zone delineation.
  • Existing methods lack comprehensive spatial-temporal analysis and socio-environmental consideration.

Purpose of the Study:

  • To develop a sophisticated model for analyzing PM2.5 concentration within TLEZ.
  • To integrate diverse data sources including taxi-fleet PM2.5 data, road network features, and time series data.
  • To establish a multi-objective optimization framework for defining optimal TLEZ boundaries considering environmental and social impacts.

Main Methods:

  • Utilized PM2.5 data from taxi fleets combined with static road network and dynamic time series features.
  • Employed a deep learning model integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Attention Mechanism (AM) for spatial-temporal analysis.
  • Developed a multi-objective optimization model solved with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify Pareto-optimal solutions for zone delineation.

Main Results:

  • The deep learning model accurately captured complex spatial and temporal PM2.5 distribution patterns.
  • The multi-objective optimization identified optimal boundaries for Low, Ultra-Low, and Zero Emission Zones.
  • The framework provides a balanced approach considering both environmental quality and residents' well-being.

Conclusions:

  • This study presents a novel, data-driven approach for optimizing TLEZ using advanced AI techniques.
  • The proposed framework offers actionable insights for policymakers and urban planners to design more effective and equitable emission control zones.
  • The integration of real-world data and sophisticated modeling enhances the precision and responsiveness of urban pollution management strategies.