Parallelized extraction of traffic state estimation rules based on bootstrapping rough set

Xia, Yingjie; Ye, Zhoumin; Fang, Yiwen; Zhang, Ting

doi:10.1109/fskd.2012.6233736

Cited by 5 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analysis of computational complexity leads to various postprocessing models, such as parallel computing, probability calculation, and decision tree. For example, the evaluated complexity of rules extraction from massive traffic data is used to parallelize the attribute significance calculation in bootstrapping rough set algorithm to estimate traffic state more accurately and efficiently [23]. The work in [23] focuses on the evaluation of algorithmic complexity for the algorithmic parallelization of computationintensive problems.…”

Section: Related Workmentioning

confidence: 99%

“…For example, the evaluated complexity of rules extraction from massive traffic data is used to parallelize the attribute significance calculation in bootstrapping rough set algorithm to estimate traffic state more accurately and efficiently [23]. The work in [23] focuses on the evaluation of algorithmic complexity for the algorithmic parallelization of computationintensive problems. However, massive traffic data analysis urgently requires the evaluation of data-centric computational complexity to parallelize the data-intensive problems.…”

Section: Related Workmentioning

confidence: 99%

“…Mða; bÞ ¼ 1 indicates the existence of a road segment from intersection a to b, while Mða; bÞ ¼ 0 indicates the inexistence of that road segment. Both speed and flux can be estimated from SCATS data and GPS data by the corresponding algorithms [23]. The tuple format is flexible to be adjusted by adding or removing attributes.…”

Section: Its Computational Domain Theorymentioning

confidence: 99%

“…By number of cells, based on the threshold definition in Eq. (23), the computational domain is decomposed into 64 sub-domains, which correspond to a three-level full octree. The amount of SCATS and GPS data records on 393 road segments during 15 min is 7914, and based on the threshold definition the amount of data records in the sub-domain by even domain decomposition is 124.…”

Section: Efficiency Testmentioning

confidence: 99%

See 3 more Smart Citations

Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

2015

Self Cite

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Its Computational Domain Theorymentioning

confidence: 99%

Section: Efficiency Testmentioning

confidence: 99%

See 2 more Smart Citations

Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

2015

Self Cite

View full text Add to dashboard Cite

“…These kinds of work always run into challenges of computational efficiency when dealing with large data sets. Luckily, this problem can be effectively solved by algorithmic parallelization executed on CI resources [14]. …”

Section: Related Workmentioning

confidence: 99%

A Cyber‐ITS Framework for Massive Traffic Data Analysis Using Cyber Infrastructure

Xia

Fontaine

2013

The Scientific World Journal

Self Cite

View full text Add to dashboard Cite

Traffic data is commonly collected from widely deployed sensors in urban areas. This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. This research, taking into consideration the significant increase in the amount of traffic data and the complexity of data analysis, focuses mainly on the challenge of solving data-intensive and computation-intensive problems. As a solution to the problems, this paper proposes a Cyber-ITS framework to perform data analysis on Cyber Infrastructure (CI), by nature parallel-computing hardware and software systems, in the context of ITS. The techniques of the framework include data representation, domain decomposition, resource allocation, and parallel processing. All these techniques are based on data-driven and application-oriented models and are organized as a component-and-workflow-based model in order to achieve technical interoperability and data reusability. A case study of the Cyber-ITS framework is presented later based on a traffic state estimation application that uses the fusion of massive Sydney Coordinated Adaptive Traffic System (SCATS) data and GPS data. The results prove that the Cyber-ITS-based implementation can achieve a high accuracy rate of traffic state estimation and provide a significant computational speedup for the data fusion by parallel computing.

show abstract

Investigating macro-level hotzone identification and variable importance using big data: A random forest models approach

Jiang

Abdel-Aty

et al. 2016

Neurocomputing

View full text Add to dashboard Cite

Parallelized extraction of traffic state estimation rules based on bootstrapping rough set

Cited by 5 publications

References 12 publications

Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

A Cyber‐ITS Framework for Massive Traffic Data Analysis Using Cyber Infrastructure

Investigating macro-level hotzone identification and variable importance using big data: A random forest models approach

Contact Info

Product

Resources

About