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  1. Home
  3. Models For Predicting Vehicle Emissions: A Comprehensive Review.
  1. Home
  3. Models For Predicting Vehicle Emissions: A Comprehensive Review.

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Models for predicting vehicle emissions: A comprehensive review.

Hui Zhong1, Kehua Chen2, Chenxi Liu3

  • 1Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511455, China.

The Science of the Total Environment
|March 2, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This review compares traditional and data-driven vehicle emission models. Data-driven models show promise for accurate traffic emission prediction, especially with advanced techniques like GCN for spatial-temporal analysis.

Keywords:
Data-drivenSpatial-temporalSurveyTime-seriesVehicle emission

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Area of Science:

  • Environmental Science
  • Transportation Engineering
  • Data Science

Background:

  • Air pollution causes millions of deaths annually, with traffic contributing significantly to emissions.
  • Existing vehicle emission models are often outdated or narrowly focused, lacking micro-level detail for targeted policies.

Purpose of the Study:

  • To systematically review and compare traditional and data-driven vehicle emission prediction models.
  • To identify progress, gaps, and future research directions in vehicle emission modeling.

Main Methods:

  • Systematic literature review of traditional (average-speed, modal) and data-driven (ANN, LSTM, GRU, RNN, GCN) emission models.
  • Analysis of model performance based on dynamometer and on-road test data.
  • Discussion of model applicability across various scenarios and influencing factors.

Main Results:

  • Traditional models lack micro-level detail, while data-driven models offer improved accuracy.
  • Artificial Neural Networks (ANN) show high performance for specific engine types; integrated models (LSTM, GRU, RNN) outperform individual ones.
  • Graph Convolutional Networks (GCN) show potential for capturing spatial-temporal emission patterns.

Conclusions:

  • Data-driven models, particularly integrated and GCN approaches, are crucial for accurate, spatially-resolved vehicle emission prediction.
  • Further research is needed on forecasting particulate matter emissions using data-driven methods.