  In current study, a mechanism to extract traffic related information such as
congestion and incidents from textual data from the internet is proposed. The
current source of data is Twitter. As the data being considered is extremely
large in size automated models are developed to stream, download, and mine the
data in real-time. Furthermore, if any tweet has traffic related information
then the models should be able to infer and extract this data.
  Currently, the data is collected only for United States and a total of
120,000 geo-tagged traffic related tweets are extracted, while six million
geo-tagged non-traffic related tweets are retrieved and classification models
are trained. Furthermore, this data is used for various kinds of spatial and
temporal analysis. A mechanism to calculate level of traffic congestion,
safety, and traffic perception for cities in U.S. is proposed. Traffic
congestion and safety rankings for the various urban areas are obtained and
then they are statistically validated with existing widely adopted rankings.
Traffic perception depicts the attitude and perception of people towards the
traffic.
  It is also seen that traffic related data when visualized spatially and
temporally provides the same pattern as the actual traffic flows for various
urban areas. When visualized at the city level, it is clearly visible that the
flow of tweets is similar to flow of vehicles and that the traffic related
tweets are representative of traffic within the cities. With all the findings
in current study, it is shown that significant amount of traffic related
information can be extracted from Twitter and other sources on internet.
Furthermore, Twitter and these data sources are freely available and are not
bound by spatial and temporal limitations. That is, wherever there is a user
there is a potential for data.
