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addressed and that the value proposition of conducting the analyses has been
thought through and documented, at least in outline. The use case sets the
scene for the conduct of the analytics work, acting as a major guiding force to
ensure that the analytics are firmly focused on objectives and business value. An
initial overview of data requirements also ensures that use cases are developed
with full recognition of available data. Use cases are used to link analytics to
objectives. They also serve as a communication tool in two directions. In the
first direction, objectives are linked to analytics ensuring that end users can see
how their objectives are being matched. This could be thought of as feedback.
Use cases also support communications in the other direction, which could be
considered to be feed-forward. The use case communicates the problem and
details of the proposed data and analytics to the data scientists or analyst.

8.5 Smart City Transportation Use Case Examples

Appendix A identifies and describes a collection of 17 smart city transportation
use case examples. Table 8.1 provides an overview of the use cases and the smart
city transportation services to which they relate.
Each use case example in Appendix A follows the same format, with the
following elements:

• Smart city service: A description of the smart city service that is addressed
by the use case. The use cases are directly connected to the 17 smart city
services defined in Chapter 5.
• Use case name: A short label for the use case that reflects the subject area.
The label is intended to make it easy to refer to the use case in a short-
hand manner during the application.
• Objective and problem statement: A concise definition of the business
challenge to be addressed by the use case. This is to ensure that user
needs and objectives have been understood and captured.
• Expected outcome of analysis: A description of the outcome of the analysis
that will deliver benefits. This ensures that the desired outcome of the
analysis has been clearly defined at the outset.
• Success criteria: Critical success factors in the delivery of the use case.
The support for and objectives-driven approach to analytics, reinforcing
the focus on objectives and user needs, avoiding a disconnect between
the analytics and the objectives.
• Source data: A high-level description of data content, data latency, data
detail, and any further information regarding the nature of the data nec-