Title :
A big-data processing framework for uncertainties in transportation data
Author :
Jie Yang; Jun Ma
Author_Institution :
SMART Infrastructure Facility, Faculty of Engineering and Information Sciences, University of Wollongong, Northfields Avenue, New South Wales 2522, Australia
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.
Keywords :
"Support vector machines","Transportation","Crawlers","Machine learning algorithms","Real-time systems","Biological system modeling","Feature extraction"
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on
DOI :
10.1109/FUZZ-IEEE.2015.7337843