  The rapid growth of population and the permanent increase in the number of
vehicles engender several issues in transportation systems, which in turn call
for an intelligent and cost-effective approach to resolve the problems in an
efficient manner. Smart transportation is a framework that leverages the power
of Information and Communication Technology for acquisition, management, and
mining of traffic-related data sources, which, in this study, are categorized
into: 1) traffic flow sensors, 2) video image processors, 3) probe people and
vehicles based on Global Positioning Systems (GPS), mobile phone cellular
networks, and Bluetooth, 4) location-based social networks, 5) transit data
with the focus on smart cards, and 6) environmental data. For each data source,
first, the operational mechanism of the technology for capturing the data is
succinctly demonstrated. Secondly, as the most salient feature of this study,
the transport-domain applications of each data source that have been conducted
by the previous studies are reviewed and classified into the main groups.
Thirdly, a number of possible future research directions are provided for all
types of data sources. Moreover, in order to alleviate the shortcomings
pertaining to each single data source and acquire a better understanding of
mobility behavior in transportation systems, the data fusion architectures are
introduced to fuse the knowledge learned from a set of heterogeneous but
complementary data sources. Finally, we briefly mention the current challenges
and their corresponding solutions in the smart transportation.
