A big-data processing framework for uncertainties in transportation data

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.