When adding capacity is not the option due to financial constraints or due to various other reasons, operating transportation systems efficiently is the only available option, in combating congestion. More and more transportation systems are concentrating on improving efficiency of these; and Traffic Archive Modelling and ITS –use of computer and communication technology is at fore-front to achieve the above said objective. For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems. A significant change in ITS in recent years is that much more data are collected from a variety of sources. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system. In this paper, we provide a critical performance based survey on the development of intelligent transportation systems, discussing the functionality of its key components and some deployment issues associated with it. Future research directions and a roadmap to future is also presented.

Data Mining, data-driven intelligent transportation systems machine learning, Hierarchical clustering, GPS, mobility, Traffic Density

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