Page 39 - Big Data Analytics for Connected Vehicles and Smart Cities

20 Big Data Analytics for Connected Vehicles and Smart Cities Questions to Be Addressed 21

These four simple, yet powerful questions go a long way to framing the
current state of transportation and the actions and investments required to im-
prove service delivery and to manage transportation in a much more effective
manner.
Again, the 20 big questions are summarized in Table 2.1. As discussed
earlier, the questions are categorized as safety-related, efficiency-related, and
user–experience related (with the assumption that environmental effects are in-
cluded under the efficiency umbrella). While Table 2.1 identifies the intended
readership groups for the questions, readers are encouraged to explore all of
the questions to gain a complete overview of the subject. Please note that the
definition of these high-level questions does not represent a deep dive into each
of the subjects. The intention is to waterski across the subject matter to provide
more complete coverage at the expense of detail. Sections 2.6–2.8 discuss the
questions further.

2.6 Safety-Related Questions

The safety-related questions focus on ways in which safety can be improved
through crash reduction and incident management, while considering the cost
of improvement.

How Do We Maximize the Safety of the Transportation System?
The safety of a transportation system can be measured in multiple dimensions.
For a start, there are different modes of travel such as private car, transit, bicy-
cle, pedestrian, and freight. There are also different dimensions to travel safety.
These include crashes, incidents, the deployment of emergency resources, inci-
dent response, and, of course, human behavior. Having an accurate picture of
the total number of crashes and the type of crashes is just a starting point. Other
questions that can be addressed by big data and analytics include those relating
to causal factors such as street lighting, road width, traffic speeds, weather con-
ditions, the presence of a sidewalks, and geometric parameters. Using analytics,
we can determine the relationship between these causal factors and support the
sort of data discovery that will lead to other questions. In the retail business, for
example, large companies use big data and analytics to establish the probability
that customers who buy product A will also purchase product B. This informa-
tion can be used to locate products in close proximity to each other and predict
demand for one product based on sales of the other. Of course, analytics can
only be achieved if a suitable central repository of data (or data lake) has been
created, combining all the data regarding the causal factors described above. If
data is then added regarding investment programs or work programs for safety