Page 145 - Big Data Analytics for Connected Vehicles and Smart Cities

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

analytics, and social media data might be retrieved from the citizen. This will
of course be summarized and anonymized before use. In the other direction,
services and information can be pushed to the citizen to enhance smart city
living and provide the equivalent of a user manual for smart city transporta-
tion services. This will be particularly important as the concept of MaaS is
implemented, when citizens will be offered a range of mobility choices across
multiple modes and from both the public and private sector.
This two-way dialogue will be enabled by the use of wireless and wire line
communications networks that will link citizens to back offices and ultimately
to the IoT, which connects a wide range of devices and appliances, along with
connected vehicles, to a single network of networks.
The dialogue will support the use of citizen awareness analytics that can
provide continual measurement regarding the perception of citizens with re-
spect to services and the awareness of citizens with respect to available services.
These can include trip modal choices and route and timing choices. A citizen
satisfaction survey can also be conducted on a rolling basis making use of the
two-way communications to determine how citizens feel about the quality of
transportation service within the city and at given locations at any given time.
This could be particularly useful for the evaluation of maintenance of traffic
through major reconstruction zones, for example.

Integrated Electronic Payment
Citywide integrated electronic payment that supports payment of tolls, pur-
chase of transit tickets, and payment of parking fees as well as payment for
government services could support a number of financial analytics that measure
the effectiveness and efficiency of the payment system. These could include
the total volume of transit revenue per passenger, transit seat utilization, toll
revenue per vehicle and per trip, and the identification of premium customers
for all modes of travel. This latter would be achieved through a combination
of electronic payment system and origin and destination data that would reveal
the most valuable routes and customers.
Other analytics could be used to identify total citywide payment system
revenue achieved compared to forecast and compared to the size of the address-
able market. Due to the volume of data regarding prevailing transportation sup-
ply and demand, it is likely that this service will also support analytics for trans-
portation performance management and travel information, at a minimum.

Intelligent Sensor–Based Infrastructure
Intelligent sensor–based infrastructure will allow multiple readings to be taken
of the same data element and enable what is known as orthogonal sensing. This
would support the definition of a data quality index that provides detail on the