Analyzing Large-Scale Smart Card Data to Investigate Public Transport Travel Behaviour Using Big Data Analytics

In urban public transport, Smart card data have been used more and more in order to collect fare automatically. They allowed passengers to access almost all type of public transportation system modes (bus, train, tram, funiculars, LRT, metro, and ferryboats) with a single card that is valid for the complete journey. Although Smart card major concentration is in revenue collection, they also generate massive amounts of passive data from the technological devices installed to control the operation of them. Generated data could be beneficial to transit planners which could rise the better understanding of passengers’ behavioral patterns for short and long term service planning. However, one of the major challenges is the fact that traditional infrastructures and methods are inefficient when processing or analyzing a large volume of data. Thus, as an alternative, big data technology could be employed to enhance collecting, storing, processing, and analyzing the data. Moreover, the main motivation would be cost-efficiency of this methodology as the cost of processing and analyzing large-scale data is huge. This experience demonstrates that a combination of planning knowledge, big data, and data mining tool allows to produce travel behaviors indicators, public transport policies, operational performance, and fare policies.