A learning-based comprehensive evaluation model for traffic data quality in intelligent transportation systems

详细信息    查看全文

• 作者：Yidong Li ; Dewang Chen
• 关键词：Intelligent transportation systems ; Fuzzy inference system ; Traffic data quality
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：75
• 期：19
• 页码：11683-11698
• 全文大小：1,852 KB
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721
• 卷排序：75

文摘

Human motion modelling has attracted more and more attentions in various industrial fields with the event of information technology. Previous studies focus on capturing, animating, understanding and modelling human gestures or physical activities. However, in many applications such as Intelligent Transportation Systems (ITS), the traffic data quality (TDQ) is becoming a critical issue which can has great influence on the efficiency of the modelling. In this paper, we focus on evaluating the traffic data quality (TDQ) from the large amount of detectors and traffic flow data in the modelling of Intelligent Transportation Systems (ITS). We first introduce four error indices of an occupancy speed model and an occupancy flow model as model evaluation indices, and two indices from experts as non-model evaluation indices. Then, we propose a comprehensive evaluation model (CEM) for TDQ. Furthermore, we develop two algorithms for training the parameters in CEM based on the least square method (LSM) and the adaptive network based fuzzy inference system (ANFIS). We compare the proposed algorithms with the real-world traffic flow data which has been collected on Beijing ring-roads and connected lines. The experimental results show that the ANFIS-based learning method outperforms in most scenarios and ensures the evaluation error less than 10 %, which can significantly improve the efficiency of identifying traffic flow detectors with low data quality.