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38 Big Data Analytics for Connected Vehicles and Smart Cities What Is Big Data? 39

This leads to the ability to operationalize the knowledge by applying the in-
sights that have been gained regarding transportation. This in turn leads to
the ability to automatically activate events that are based on triggers that have
been defined as a result of the analytics. This incremental growth is enabled by
a parallel migration in which ad hoc analysis increases and analytical modeling
grows, followed by the ability to continuously update and answer time sensitive
queries. Ultimately event-based triggering becomes a reality. In transportation
terms event-based triggering could mean automatically generating messages to
be displayed on dynamic message signs or the automatic retiming of traffic sig-
nals based on fluctuations in demand and the occurrence of events. It could also
include automated response to incidents including the dispatching of resources
and the use of traffic control devices to manage traffic.

Variety
Another dimension of big data is an increase in the variety of data that can be
collected and processed. For example, in the world of transportation, it is pos-
sible to collect a wide range of data including the following:

• Traffic speed;
• Traffic volume;
• Travel times;
• Transit passenger counts;
• Vehicle location.

The above items can be described as traditional transportation data. In
the era of big data, when we have no need to constrain data sizes, we can also
add the following nontraditional data:

• Electricity consumption;
• Retail transactions;
• Smart phone position data;
• Probe data from connected vehicles.

These are just a few examples of additional data that can be blended with
traditional data to create big data with a wider variety of data. In fact, the surface
has just been scratched in terms of the wide variety of data that can be collected
to gain better insights into the demand for transportation including the vol-
umes of and the reasons for travel. It is to be expected that another dimension
of variability will lie in the expansion of data collection within transportation