Page 186 - Big Data Analytics for Connected Vehicles and Smart Cities

166 Big Data Analytics for Connected Vehicles and Smart Cities Transportation Use Cases 167

• Success criteria: Availability of electric vehicle charging points; optimized
energy consumption related to electric vehicles; and increased use of
electric vehicles and the urban environment.
• Source data examples: Number of electric vehicles; energy and demand
related to electric vehicles; population distribution; electric vehicle dis-
tribution; energy consumption data for electric vehicles; and electric ve-
hicle use data.
• Business benefits: Energy efficiency; etter matching of energy supply and
demand; reduced omissions; and reduced dependency on fossil-based
fuels.
• Challenges: Determining demand for electric vehicle charging; collecting
electric vehicle use data; and collecting electric vehicle ownership data.
• Analytics that can be applied: Demand for charging electric vehicle use,
electric vehicle ownership, market penetration of electric vehicles, miles
traveled for electric vehicles as compared to other vehicles.

Use Case Example 9: Mobility Hub
Smart City Service: Smart Land Use

• Objectives: The use of observed data to provide a detailed understanding
of the relationship between land use and transportation demand.
• Expected outcome of analyses: More accurate and detailed assessment of
the effects of land use on transportation demand.
• Success criteria: The development of better strategies for relating land
use, transportation demand, and transportation supply.
• Source data examples: Origin and destination data; movement analytics
data; smart manufacturing data; smart retail data; and smart healthcare
data.

• Business benefits: Better decision-making data and deeper understanding
of the relationship between land use and transportation demand.
• Challenges: Access to observe data; access to mobility analytics; charac-
terizing existing land use; and developing a catalog of land use transpor-
tation impacts based on observe data.
• Analytics that can be applied: Mobility hub efficiency, mobility hub
throughput, mobility hub cost-benefit index.