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214 Big Data Analytics for Connected Vehicles and Smart Cities Practical Applications and Concepts for Transportation Data Analytics 215

lic perception; therefore, the toll agency would have to develop specific
values for the agency. As an example, if user perception is measured on a
scale from 0 to 100% for both the road in question and parallel alterna-
tives, then a positive difference in perception of 1% could be valued at
$10,000. This is an arbitrary number intended to provide an example of
the calculation. In practice, the value could be calculated as a percentage
of the total expenditure of the toll agency in marketing and outreach.
For example, if the toll agency spends $5 million per year on marketing
and outreach, then a proportion of this figure could be used to represent
the value of a 1% improvement in user perception.

Another approach to establishing the value of a 1% change in user per-
ception would be to conduct direct surveys on the user population. The survey
would ask users to place a dollar value on the improved experience delivered
by the toll road. Smart phone apps could be used to make this an efficient and
continuous process.

10.6 Smart City Accessibility Index

Many of the objectives related to smart city initiatives center on improving ac-
cessibility to jobs, education, health, and retail opportunities. This involves the
measurement of the ease or difficulty of travel between residential zones within
the smart city region and zones that contain such opportunities. In close coop-
eration with movement analytics data providers, smart city analysts and smart
city practitioners, the following concept was developed to address the needs of
accessibility analysis within a smart city. Typically, transportation accessibility
has been defined in urban areas by making use of synthetic data from trans-
portation land-use models. These take relatively small samples of real trans-
portation conditions and apply modeling techniques to develop a big picture
for prevailing and future conditions. With the advent of movement analytics
from smart phone apps, it is possible to revisit the approach and define a new
approach based on observed data. Movement analytics involves the capture of
GPS data from smart phones in an aggregated and anonymized manner that
enables patterns of travel to and from zones to be determined at a relatively high
sample rate. In addition to providing an assessment of overall demand between
zones in the smart city region, movement analytics can also provide a strong
indication of the modes and routes that are chosen to make the trip. Through
the definition of an accessibility index, which is comprised of travel time, travel
time reliability, and cost of travel between major zones in the smart city region,
it is possible to evaluate accessibility. Note that the movement analytics data
also enables the identification of residential zones and those that contain jobs,