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I have decided that perhaps the solution lies in the application of transportation
data analytics. Organizational change would take the form of changes in job
objectives and descriptions and the adoption of a new culture. To address the
idea of a new culture, consider the following football analogy.
World-class reporting will enable staff within an organization to be ex-
tremely well-informed spectators at the football game. Great transportation
analytics will empower the same staff to be coaches and exert influence on the
performance of the organization, our team. It is expected that transportation
data analytics will significantly impact the planning, design, and operations of
transportation while providing guidance for future transportation investment
programs. As an example, an interesting analytic might be dollars per percent-
age modal shift toward public transportation. This would be a measure of the
effectiveness of investments designed to influence the modal shift in the region
in favor of public transit.
So how exactly would transportation data analytics shape the organiza-
tional arrangements within a transportation enterprise? Analysis of big data sets
using suitable discovery tools will reveal the trends, patterns, and underlying
mechanisms of transportation. Transportation data analytics will be defined
and can be used to manage planning, design, implementation, operations, and
maintenance of transportation. These transportation data analytics would then
be incorporated into job descriptions and objectives for the staff involved in
transportation service delivery.
A simple example would be the role of the traffic signal engineer. At the
moment, the job of the traffic signal engineer is to run the traffic signal sys-
tem. Perhaps in the future, the job objectives of the traffic signal engineer will
be stated as the minimization of stops and delays across the corridor or the
network. There may also be other advanced analytics yet to be discovered that
could be suitable for use as job objectives. The job description for a traffic signal
engineer would then be written around the attainment of the job objectives.
For example, a narrow view of this would suggest that the change in em-
phasis could be unfair to the traffic signal engineer, as some factors affecting
stops and delays are not within his or her control. Perhaps the job description
could be written to include the need to cooperate and collaborate with others
whose actions affect the primary objectives. This also leads to the thought that
perhaps ITS user services could have analytics associated with them for the pur-
poses of measuring the effectiveness of the delivery of the services. This concept
holds out the possibility of building a bridge between the various layers of an
ITS architecture, while also setting the scene for a laser focus on results rather
than activity.
The emergence of big data and the connected vehicle and growing under-
standing of the data science possibilities from outside of transportation means