Page 150 - Big Data Analytics for Connected Vehicles and Smart Cities

130 Big Data Analytics for Connected Vehicles and Smart Cities What Are Analytics? 131

in safety. Defining another analytic that compares such reductions with invest-
ment in safety-related systems would also yield insight into the performance of
such investments.
With respect to efficiency, an overall improvement in trip times and trip
time reliability including wait times and modal transfer times would provide
insight into efficiency improvements. Enhanced user experience could be char-
acterized by the use of a user perception index supported by smart phone apps
or the in-vehicle unit within the automated vehicle. Other analytics that would
characterize the progress being made toward full automation of the city would
be the percentage of automated vehicles within the entire citywide fleet, the
percentage of automated vehicles in use by city agencies and private fleets, the
proportion of deliveries made by automated vehicles, and the proportion of
passengers carried by automated transit vehicles. These would all take account
of the resources invested in the services and the availability of the services over
time, space, and quality levels within the city.

Urban Delivery and Logistics

Analytics to characterize urban delivery and logistics would address cost, time,
and reliability of delivery. For example, an analytic that characterizes the average
cost of urban delivery in comparison to the number of deliveries would shed
light on the efficiency gain related to automated deliveries. Another analytic
that characterizes the average time for end-to-end delivery, taking account of
the volume of deliveries, would also provide insight into efficiency gains. Im-
provement in user experience could be measured by a freight and logistics user
satisfaction index and a freight management satisfaction index. These would
measure the increased levels of satisfaction from the end user and from the
freight operator, respectively. It is likely that such analytics will be closely re-
lated to and used in combination with transportation management analytics
that characterize trip time and trip time reliability across the city. Ultimately
this could support a more sophisticated approach to money-back guarantees
for failure to deliver on time. This might even be extended to address mobility
and transit services.

User-Focused Mobility
User-focused mobility services will make use of many of the analytics previously
defined for the other 15 services. This would include a citywide mobility index
to measure the increase in mobility caused by the service, a user satisfaction
index to measure user perception of mobility services, and a reliability index for
transportation services within the smart city.
An ultimate analytics for user-focused mobility services would compare
the level of mobility afforded compared to the proportion of the population
serviced and the resources invested in capital and operations.