摘要
为寻找合适的城市交通运输碳排放预测方法,基于STIRPAT模型,选取人口总量、人均GDP、机动车保有量、碳排放强度、城镇化率、旅客周转量和货物周转量等7项指标作为城市交通运输碳排放影响因素,分别建立基于遗传算法优化支持向量机、粒子群优化支持向量机、网格搜索优化支持向量机预测模型,并以1995—2016年交通运输碳排放相关指标作为基础数据做实例分析。结果表明:GA-SVM对比PSO-SVM与GS-SVM所得出训练集的相关系数分别增长了2.74%和1.07%,测试集的相关系数分别增长了1.04%和0.29%,较其它两种预测模型具有良好的学习和推广能力,说明GA-SVM模型更适合对城市交通碳排放进行预测分析。
In order to find a suitable prediction method for carbon emission of urban traffic,7 indexes were selected as the influencing factors of carbon emission of urban traffic based on the STIRPAT model.The 7 indexes included total population,per capita GDP,vehicle ownership,carbon emission intensity,urbanization rate,passenger turnover,cargo turnover and etc..The prediction models of GA-SVM,PSO-SVM and GS-SVM were established respectively.And the relevant indicators of traffic carbon emissions from 1995 to 2016 were taken as the basic data for case study.The results show that:compared with PSO-SVM and GS-SVM,the correlation coefficients of training set of GA-SVM increase by 2.74% and 1.07% respectively,and the correlation coefficients of test set of GA-SVM increase by 1.04% and 0.29% respectively.Compared with the other two prediction models,GA-SVM model has good learning and generalization ability,which indicates that GA-SVM model is more suitable for the prediction and analysis of urban traffic carbon emissions.
作者
高金贺
黄伟玲
蒋浩鹏
GAO Jinhe;HUANG Weiling;JIANG Haopeng(School of Civil Engineering and Architecture,East China University of Technology,Nanchang 330013,Jiangxi,China;Yangtze River College,East China University of Technology,Nanchang 344000,Jiangxi,China;School of Civil Engineering and Transportation,Hebei University of Technology,Tianjin 300401,China)
出处
《重庆交通大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第7期33-39,共7页
Journal of Chongqing Jiaotong University(Natural Science)
基金
江西省科技厅科技计划项目(2016BBG70084)。
关键词
交通运输工程
碳排放预测
GA-SVM模型
影响指标
traffic and transportation engineering
carbon emissions
GA-SVM model
influence index
