层次分析法
支持向量机
北京
城市轨道交通
公共交通
计算机科学
智能交通系统
运输工程
模糊逻辑
背景(考古学)
轨道交通
评价方法
工程类
运筹学
可靠性工程
机器学习
人工智能
中国
法学
古生物学
生物
政治学
出处
期刊:SAE International journal of sustainable transportation, energy, environment & policy
日期:2023-04-26
卷期号:5 (1)
标识
DOI:10.4271/13-05-01-0001
摘要
<div>With the rapid development of the Internet and intelligent control technology, intelligent transportation has become a research hotspot in building a smart city. Under the background of intelligent transportation, it is particularly important to effectively evaluate the rail transit as the framework of urban public transport in this study, and fuzzy mechanism is introduced to optimize the support vector machine (SVM), and on this basis, analytic hierarchy process (AHP) and SVM are combined to improve the classification accuracy and improve the rail transit operation safety evaluation index system. The experimental results show that the classification accuracy of the fuzzy SVM combined with AHP is above 85% on all the datasets, and it can effectively eliminate the less-relevant indicators. In the actual evaluation of Shanghai Rail Transit safety, the prediction accuracy exceeded 80% and the highest reached 94.51%. Among them, the accuracy of management level and infrastructure were increased by 24.1% and 18.34%, respectively, indicating that this method can effectively screen the evaluation indicators. In the evaluation of Beijing Rail Transit, the accuracy rate of the combined algorithm reaches 95.67%, with high classification accuracy, which provides a reference direction for the establishment of the rail transit operation safety evaluation system.</div>
科研通智能强力驱动
Strongly Powered by AbleSci AI