Earticle

초록

영어

Accurate assessment of carbon emission of urban regional transportation is the core of urban low-carbon traffic construction. Traditional carbon emission evaluation methods need a large number of samples and sample data of carbon emission of urban regional transportation is smaller, so the precision will be lower if traditional methods are adopted. This paper proposes particle swarm optimization to optimize support vector machine carbon emission early warning system of urban regional transportation (PSO－SVM) and takes the advantage of small sample data modeling of support vector machine to improve the carbon emission evaluation accuracy of urban regional transportation. Furthermore, this paper takes carbon emission evaluation accuracy of urban regional transportation as modeling target, selects reasonable evaluation index, confirms carbon emission evaluation model structure of urban regional transportation and then optimizes support vector machine (SVM) by adopting particle swarm optimization (PSO) to establish evaluation model and conduct system simulation. Results show that PSO-SVM actually increases the assessment accuracy, having practical application value in urban traffic carbon emission management.

목차

Abstract
 1. Introduction
 2. Carbon Emission Evaluation Theory of Urban Regional Transportation
 3. Carbon Emission Evaluation Model of Urban Regional Transportation
  3.1. Evaluation Index Selection
  3.2 Evaluation Index Standardization
  3.3 SVM Algorithm
  3.4 Particle Swarm Optimization Algorithm
  3.5 Carbon Emission Evaluation Model Workflow of Urban Regional Transportation
  3.6 Carbon Emission Model Performance Evaluation Standards of Urban Regional Transportation
 4. Simulation Study
  4.1 Simulation Data
  4.2 Model Realization
  4.3 Simulation Results and Analysis
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Hao Wu [ College of Economics and Management, Nanjing University of Aeronautics and Astronautics ]
• Xianglian Zhao [ College of Economics and Management, Nanjing University of Aeronautics and Astronautics ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605