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A big-data processing framework for uncertainties in transportation data

Conference Paper


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Abstract


  • Transportation infrastructure takes a primary role in urban development planning. To better facilitate or understand the infrastructure status and demands, a huge amount of transportation data such as traffic flow counts has been collected from numerous transportation monitoring systems. Making full use of harvested data samples to discover important patterns has become an increasingly appealing research topic, in which a sophisticated and uncertainty processing framework is required. In this paper, a big-data processing framework is introduced to analyse the transportation data, particularly taking the classification problem of the parking occupation status as an illustrative example. Three modules are implemented to crawl the raw records, generate high-level features, and apply the machine learning algorithm for classification. In addition, the fuzzification algorithm is also introduced to quantify the key attributes of the data, which helps in removing the data redundancy and inconsistency. The proposed framework then is evaluated using a real-world dataset collected from twelve car parks in a university. Simulation results show that the proposed framework performs well with a convincing classification

    accuracy.

Publication Date


  • 2015

Citation


  • Yang, J. & Ma, J. (2015). A big-data processing framework for uncertainties in transportation data. In A. Yazici, N. R. Pal, U. Kaymak, T. Martin, H. Ishibuchi, C. Lin, J. M. C. Sousa & B. Tutmez (Eds.), IEEE International Conference on Fuzzy Systems (FuzzIEEEE 2015) (pp. 1-6). United States: IEEE.

Scopus Eid


  • 2-s2.0-84975721651

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6348&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5320

Start Page


  • 1

End Page


  • 6

Abstract


  • Transportation infrastructure takes a primary role in urban development planning. To better facilitate or understand the infrastructure status and demands, a huge amount of transportation data such as traffic flow counts has been collected from numerous transportation monitoring systems. Making full use of harvested data samples to discover important patterns has become an increasingly appealing research topic, in which a sophisticated and uncertainty processing framework is required. In this paper, a big-data processing framework is introduced to analyse the transportation data, particularly taking the classification problem of the parking occupation status as an illustrative example. Three modules are implemented to crawl the raw records, generate high-level features, and apply the machine learning algorithm for classification. In addition, the fuzzification algorithm is also introduced to quantify the key attributes of the data, which helps in removing the data redundancy and inconsistency. The proposed framework then is evaluated using a real-world dataset collected from twelve car parks in a university. Simulation results show that the proposed framework performs well with a convincing classification

    accuracy.

Publication Date


  • 2015

Citation


  • Yang, J. & Ma, J. (2015). A big-data processing framework for uncertainties in transportation data. In A. Yazici, N. R. Pal, U. Kaymak, T. Martin, H. Ishibuchi, C. Lin, J. M. C. Sousa & B. Tutmez (Eds.), IEEE International Conference on Fuzzy Systems (FuzzIEEEE 2015) (pp. 1-6). United States: IEEE.

Scopus Eid


  • 2-s2.0-84975721651

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6348&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5320

Start Page


  • 1

End Page


  • 6