Page 163 - Big Data Analytics for Connected Vehicles and Smart Cities

144 Big Data Analytics for Connected Vehicles and Smart Cities The Practical Application of Analytics to Transportation 145

7.8 Why Does MaaS Make a Good Departure Point for a Smart
City?

MaaS is a relatively new term that encapsulates considerable potential for im-
proving transportation service delivery within a smart city. Over the past few
years, significant privately financed and operated transportation service alterna-
tives have emerged in the form of Uber and Lyft. These are overlaid on publicly
funded transit services to offer the possibility of creating a portfolio of options
for the traveler. The development and communication of this portfolio has the
possibility of influencing the traveler away from the private vehicle and perhaps
ultimately influencing decisions on whether to acquire a vehicle or simply ac-
quire transportation as a service.
Convenient ways to request transportation and to pay for it provide sig-
nificant influence in traveler decision-making. As most cities around the world
are battling with congestion and the surplus of demand for private car transpor-
tation compared to public transit, MaaS could be viewed as one of the solutions
to this issue. Cities also struggle with a lack of available land and the amount of
land that car parking requires. In fact, there is a significant investment in private
cars that at any given time is sitting in a parking lot and not returning value.
MaaS offers the possibility for flexible and dynamic matching of supply and
demand as it fluctuates over time and space within a smart city.

7.9 MaaS Analytics and Their Practical Application

MaaS represents a combination of both public and private transportation ser-
vices that can be offered to the traveler as a portfolio. Table 7.2 lists some can-
didate analytics for MaaS and provides notes on the practical application of the
analytics.

7.10 Traffic Management—What Is It?

The traffic management element will encompass the management of freeways,
arterials, and city streets. It is often the case that these elements are managed
independently, but we will assume that in a smart city there will be coordinated
management across them. Freeway management includes the use of infrastruc-
ture-based and probe vehicle data collection, the use of in-vehicle systems, and
roadside dynamic message signs to communicate with drivers and decentralize
dispatching of incident clearance and recovery resources. In most cases, closed-
circuit TV cameras are also used to understand the nature of the incident to
provide input to the selection of resources to be dispatched.