Premium
Parallelized Fusion on Multisensor Transportation Data: A Case Study in CyberITS
Author(s) -
Xia Yingjie,
Li Xiumei,
Shan Zhenyu
Publication year - 2013
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21592
Subject(s) - computer science , sensor fusion , intelligent transportation system , data mining , fusion , computation , dempster–shafer theory , rough set , artificial intelligence , algorithm , engineering , linguistics , philosophy , civil engineering
With increasing requirements on traffic efficiency, environmental quality, energy efficiency, and economic productivity, intelligent transportation systems (ITS) research, which is centered on traffic state evaluation, meets two grand challenges. One is to process heterogeneous transportation data collected from different types of traffic sensors and the other is to reduce the high computation intensity for processing massive transportation data. To overcome both challenges, this paper proposes an approach using parallelized fusion on multisensor transportation data. Parallelized fusion is an embodied case of CyberITS framework, which is developed for the synthesis of cyberinfrastructure and ITS. The fusion functionality is shaped by a rough evidential fusion model (REFM) based on rough set and Dempster–Shafer evidence theories. The REFM consists of four components, which are sensor data input, bootstrapping rough conversion, hierarchical evidential fusion, and traffic state output. Their computation intensity is centered on conversion and fusion components, which can be optimized by the algorithm‐ and data‐centric parallelization, respectively. Computational experiments for accuracy and efficiency demonstrate that parallelized fusion achieves a distributed, high‐performance, and collaborative CyberITS implementation to provide accurate and real‐time traffic state evaluation.