Prediction method of urban traffic carbon emission reduction rate based on grey relational analysis

Jiagu Liao

doi:10.1117/12.2614663

10 November 2021 Prediction method of urban traffic carbon emission reduction rate based on grey relational analysis

Jiagu Liao

Proceedings Volume 12050, International Conference on Smart Transportation and City Engineering 2021; 120502G (2021) https://doi.org/10.1117/12.2614663
Event: 2021 International Conference on Smart Transportation and City Engineering, 2021, Chongqing, China

Abstract

In order to more accurately predict the carbon emissions of urban transportation in my country, this paper proposes a method for predicting the reduction rate of urban transportation carbon emissions based on gray correlation analysis. Based on the principle of grey relational analysis, the influencing factors of China's carbon emissions were screened. On this basis, this paper constructed the prediction model of urban traffic carbon emission reduction rate, established the evaluation index data column, determined the reference index set, and standardized the data column. Experiments show that the root mean square error of the method in this paper is less than that of the comparison method, and the average absolute percentage error is better than that of the LSSVR and ELM methods.

Citation Download Citation

Jiagu Liao "Prediction method of urban traffic carbon emission reduction rate based on grey relational analysis", Proc. SPIE 12050, International Conference on Smart Transportation and City Engineering 2021, 120502G (10 November 2021); https://doi.org/10.1117/12.2614663

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