Page 190 - Big Data Analytics for Connected Vehicles and Smart Cities

170 Big Data Analytics for Connected Vehicles and Smart Cities Transportation Use Cases 171

Use Case Example 15: Urban Automation Analysis

Smart City Service: Urban Automation

• Objectives: Analysis of progress toward the application of urban automa-
tion, including the movement of people and goods.
• Expected outcome of analyses: Accelerating the deployment of automation
within the urban environment.
• Success criteria: Accelerated progress in implementing urban automa-
tion.
• Source data examples: Automated vehicle use data and transportation
demand data.
• Business benefits: Transportation service cost reduction; improved trans-
portation service reliability; and better transportation service response.
• Challenges: Access to data on ownership and use of automated vehicles.
• Analytics that can be applied: Vehicle ownership data, automated vehicle
use data.

Use Case Example 16: Freight Performance Management
Smart City Service: Urban Delivery and Logistics

• Objectives: Detailed assessment of the cost of urban delivery for goods,
average time for entering delivery and quality of delivery service
• Expected outcome of analyses: More effective urban delivery for goods;
better value for money for goods customers; and an increase in service
quality.
• Success criteria: Lower-cost urban goods delivery; minimized cost of
goods delivery; and maximized goods delivery service quality.
• Source data examples: Urban delivery cost data; urban delivery trip time
data; user satisfaction data; and operator satisfaction data.
• Business benefits: Reduced freight cost; enhanced freight delivery time
reliability; and enhanced user experience.
• Challenges: Access to freight delivery costs and access to delivery times.
• Analytics that can be applied: Freight delivery costs, delivery times, deliv-
ery time reliability.