Page 148 - Big Data Analytics for Connected Vehicles and Smart Cities

128 Big Data Analytics for Connected Vehicles and Smart Cities What Are Analytics? 129

could be represented by a supply and demand matching index that shows the
difference between supply and demand for transportation in the smart city at
any given time. Transportation governance should also include responsibility
for coordination between the activities of various agencies that deliver transpor-
tation the city. This could be addressed by a transportation agency coordination
index that defines the effectiveness of coordinated plans and activities between
agencies. With respect to public-private partnerships a partnership cost-saving
index could be used to show the financial advantage of public-private partner-
ships. With respect to big data, the cost of data storage and manipulation could
be compared to services provided in an analytic that measures how effectively
the entire city is collaborating on the use of big data. This is another crucial
aspect of success in transportation delivery for a smart city as the technologies
and services involved are best utilized in a sharing and coordination environ-
ment. Transportation has historically been operated by a number of highly fo-
cused agencies that derive great efficiency from specialization. Coordination
and partnership must be added to this specialism if smart city transportation
services are to be delivered in the most effective manner. This may require a
reorganization and redefinition of arrangements for governance and manage-
ment to accommodate the new challenges. Existing transportation agencies also
require a certain degree of autonomy to implement policies and procedures
without reference to other organizations. A balanced approach to autonomy
and coordination will be required.

Transportation Management
It is expected that within a smart city, transportation management will be con-
ducted on a multimodal basis across all transportation services delivered within
the city. This will require the use of analytics that characterize the effectiveness
and efficiency of all services. One such analytics could be a mobility index that
measures the real mobility to and from each zone within the smart city in addi-
tion to an overall mobility measure for the entire city. A citywide job accessibil-
ity index analytic could also be used to characterize the ease or difficulty associ-
ated with trips to and from work. These analytics could be used in combination
with a transportation efficiency index, a travel time reliability index, and a total
travel time index to develop a complete picture of transportation management
efficiency within a smart city. At the highest level analytics could be defined
that compare the volume of public and private sector investment to the results
obtained. This would include the application of scientific investment planning
techniques discussed earlier in Section 6.5.
Efficient transportation management would also address parking manage-
ment as part of the analytics approach. Analytics could be applied to revenue
management, parking space utilization, user satisfaction with parking informa-
tion, and parking supply planning topics, at a minimum.