Page 182 - Big Data Analytics for Connected Vehicles and Smart Cities

162 Big Data Analytics for Connected Vehicles and Smart Cities Transportation Use Cases 163

areas; integrated use of probe vehicle and sensor-based data; and maxi-
mizing the value of the data delivered and minimizing the cost of data
collection.
• Success criteria: Use of vehicle probe data for the full spectrum of
transportation activities; effective integration of probe- and sensor
based data; and optimization of data collection and acquisition invest-
ments.
• Source data examples: Connected vehicle data including vehicle location,
instantaneous vehicle speed, vehicle ID, and vehicle dynamics and en-
gine management data.
• Business benefits: More comprehensive and higher resolution picture of
transportation supply conditions and transportation demand.
• Challenges: Agreeing on access to connected vehicle data and improving
market penetration of connected vehicles.
• Analytics that can be applied: Connected vehicle data accessibilty, con-
nected vehicle market penetration.

Use Case Example 3: Connected, Involved Citizens
Smart City Service: Connected, Involved citizens

• Objectives: To support a two-way dialogue between data sources and citi-
zens and to enable citizens to provide crowdsource data and feedback
concerning perception of quality and satisfaction levels.
• Expected outcome of analyses: Better informed citizens and enhanced abil-
ities for citizens to provide data and opinions on transportation service
delivery.
• Success criteria: Higher levels of citizen satisfaction and an increased
awareness of citizen perception of traveler information service quality.
• Source data examples: Movement analytics data; citizen perception data;
and quality of transportation service data.
• Business benefits: Enhanced user experience; increased understanding
of user perception; and lower cost of data collection by incorporating
crowdsourcing.
• Challenges: Developing a suitable data collection that can also enable
user perception feedback and integrating user perception and crowd-
sourcing data with other data.