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124 Big Data Analytics for Connected Vehicles and Smart Cities What Are Analytics? 125

gence of discovery tools that can be used on a big data set to uncover trends
and patterns within the data provides us with a flexible approach to analytics.
Predefinition of analytics is like having a hypothesis that will be proven by the
data analysis. Another way to approach this is through a discovery process that
enables the data to talk, revealing further insights. This type of approach might
yield suggested analytics that could be used in addition to the ones that have
been predefined. The process of discovery for transportation holds the promise
of a fascinating role for a transportation data analyst. Using a big data set, com-
bined with a powerful discovery tool, should make it possible to build a deeper
understanding with respect to transportation within a city or region. This will
require a skill set that combines an understanding of data science with an un-
derstanding of transportation, a blend that seems to be rare at the moment.
One way to address this would be to ensure that suitable data analytics expertise
is built into a smart city planning and deployment teams. While in the short
term executive-level staff and managers would not be expected to have a deep
understanding of data science, they need to be supported by someone that does.
Over a period of time, data science knowledge and awareness should grow at
the executive level as the tools become more intuitive, enabling executive-level
and management staff to conduct their own discovery and analytics. Perhaps,
in the early days at least, this is an ideal role for a consultant.
Again, Table 6.1 shows a series of analytics for each of the 16 services
identified in Chapter 5. The focus is placed on services since the concept of
smart cities is to take full advantage of technology to provide better services to
city residents and visitors. It is intended to act as a starting point by providing
a sample of analytics in order to illustrate the nature and characteristics of ana-
lytics. It is expected that a smart city team would build on these and develop a
set of customized services and analytics specifically for the city in question. It
is also likely that analytics for one service will be combined with analytics for
another service to create hybrid analytics that will address either one service, or
the other, or both. For example, trip time reliability from the intelligent sensor–
based infrastructure service could also be used to measure the performance and
effectiveness of other services. It is also likely that analytics derived for use in
integrated payment, connected and autonomous vehicles, and travel informa-
tion will find application in transportation performance management. This is
another reason for taking a structured, coordinated approach to the definition
of analytics for each service rather than an ad hoc one.

Asset and Maintenance Management
Analytic applied to asset and maintenance management associated with smart
city transportation services will have the potential to improve the efficiency of
service delivery. This will, in turn, improve the effectiveness and efficiency of
the other services that are supported by asset and maintenance management.