Page 146 - Big Data Analytics for Connected Vehicles and Smart Cities

126 Big Data Analytics for Connected Vehicles and Smart Cities What Are Analytics? 127

quality of the data being collected in terms of accuracy and completeness. A
transportation conditions index could also be created from data that emanates
from sensor-based infrastructure that shows the ebb and flow in the quantity
and quality of transportation services being provided within the smart city. This
could be supplemented by time and trip time variability indexes. Sensor data
could also be combined with probe data from movement analytics and con-
nected and autonomous vehicles to provide hybrid analytics.

Low-Cost, Efficient, Secure, and Resilient ICT
Assuming that low-cost, efficient, secure, and resilient ICT also includes man-
agement capabilities to measure the volume and use of each datalink, then ana-
lytics can be created to compare the total network capacity to the load on each
link any given time. Network latency on a total network and individual link
basis along with the cost of data transfer and a network security index could also
be determined to measure the performance and gain insight and understanding
into the delivery of information and communication technologies. This will
support the application of network management techniques to transportation
communication networks. It is also interesting to note that such techniques
are likely to be applied to transportation services within the smart city in the
future. The concept of a network manager is long established for computer and
energy networks. The availability of data and analytics should make it possible
to identify and support a “transportation network manager” role for the future
smart city. It is now feasible, with the help of big data and analytics, to manage
transportation service on a network and citywide basis.

Smart Grid, Roadway Electrification, and Electric Vehicles
The smart grid, roadway electrification, and electric vehicles service involves the
use of electricity as an energy source for vehicles in a smart city. Analytics that
can be used to characterize this service include those that relate to the availabil-
ity of electric vehicle charging points and those that relate to the performance
of the electric vehicles. For example, an analytic that defines the number of
electric vehicle charging points per mile could be used to define the viability of
electric vehicle operation in a smart city. Another analytics measure—electric
vehicle charging points per head of population—could also be used to define
the progress being made toward making electric vehicles ubiquitous in a smart
city. The number of electric vehicles as a percentage of the total fleet, electric
vehicle miles per day, electric vehicle miles per trip, and electric vehicle miles
between charges could also be used as analytics to define the performance of
the electric vehicle. An overarching analytic for the entire electric vehicle system
would be the amount of energy being consumed for all vehicles over the entire
city, compared to the degree of mobility made available by the service.