1. The Next Generation of Transportation Systems, Greenhouse Emissions, and Data Mining
Panelists
Lisa Amini, IBM Ashok Srivastava, NASA Ames
Eugene Tierney, US Environmental Protection Agency
Ramasamy Uthurusamy, General Motors (Retd.)
Moderator Hillol Kargupta, University of Maryland Baltimore County and AGNIK.

2. Roadmap Brief introduction to the topic (10 mins)
Transportation, Emissions, and Data Mining
Individual presentations:
Lisa Amini (10 mins)
Ashok Srivastava (10 mins)
Eugene Tierney (10 mins)
Ramasamy Uthurusamy (10 mins)
Open Discussions (30 mins)

3. Transportation Systems
Specific Modes
Cars/Trucks
Aviation
Support Infrastructure
Marine and others

4. Transportation and Emissions Poorly maintained vehicles pollute
20 million commercial vehicles in US alone; more than 200 million of cars.
Vehicles have serious effects on the environment
Transportation activities are responsible for approximately 25% to 30% of total U.S. GHG emissions
On-highway commercial truck market accounting for over 45% of transportation GHG
Poorly maintained vehicles pollute

5. Aviation and Emissions
A Boeing 747 uses approximately 1 gallon of fuel every second.
A flight from Washington DC to Los Angeles emits about 726 pounds of CO2.
Aircrafts generate large volume of data even from short flights (e.g. 10MB from an hour long flight depending upon the type of aircraft)

6. Emissions Data Mining: Data Sources
Vehicle performance data (speed, acceleration, O2 sensor, load, etc.)
Vehicle diagnostic data
Emissions data (CO2, CO, NOx, HC, PM, etc.)
Contextual data
Geo-spatial characteristics (e.g. roadways information)
Weather
Traffic information
Vehicle user data
CO Emissions from a light duty vehicle
Experimental MEMS Sensor for low-cost emission measurement. Courtsey: Makel

7. ROI: Motivation for Improved Maintenance
SmartWay
Greenhouse (GHG) emissions scoring
Air Pollution emissions scoring
Emissions Trading
Cap and trade!
Current trading with and without the support from legislation

8. The SmartWay® Program

9. The Business of Carbon Trading

10. Wireless Emissions Monitoring
Vehicle Emissions check
Periodic check
Continuous emissions monitoring over wide-area wireless networks
Example: State of California CTP

11. Transportation Infrastructure, Energy Storage and Power Distribution
Develop smart materials with better storage and embedded sensing capabilities.
Develop ways to interface the smart materials with the systems-level properties in a distributed power grid where multiple storage devices at different user locations are used to store electric power.
Device Level Issues: Development of smart materials that offer improved storage capability along with built-in sensing capability for improved measurement of internal states.
System Level Issues: Use distributed data analysis algorithms to detect patterns from performance data of the distributed electric power generation and storage network.
Connecting the System Level with the Device Level: Link the measured patterns in the device level data with the macroscopic data and control mechanisms at the System level for improved storage and management.

12. Additional ROI: Driver Safety & Predictive Health Maintenance
Indirect benefits of emissions reductions
Better vehicle health
Driver safety

13. Some Transportation Infrastructure Issues Related to Emissions Management
Traffic management
Energy storage and distribution
Infrastructure safety

14. What Data Mining may Offer: Some Examples
Detect anomalous events for diagnostic applications
Build predictive models of emissions and fuel economy as a function of vehicle and driving parameters for optimizing the performance onboard the vehicle
Effect of speeding on fuel economy/emissions
Effect of acceleration /braking on emissions
Effect of idling on emissions
Benchmark the behavior of the exhaust sub-system of vehicles with respect to a standard vehicle
Quantify the effect of the oxygen sensor or the air-intake subsystem behavior on fuel economy and emission
How the emission patterns are correlated with environmental and vehicle performance parameters
CO2 emissions from a light duty vehicle

15. Some Questions
What are the key information processing challenges in the next generation of intelligent transportation systems? 
What are the challenges in making transportation greener and how data mining can help? 
Why predictive vehicle health monitoring is important and why should data miners care? 
What are the emerging business models for Green IT that data miners can benefit from? 
How can data miners help vehicle manufacturers in building better and cleaner vehicles? 
How can data miners help maintaining and monitoring vehicles after market? 
What is the current status of the technology and what are the achievable return of investments for this market? 
What are the projections for the next five years and what can data miners do to help? 
What are the challenges against large-scale adoption of data mining-based decision support tools for clean vehicles and transportation systems? 
How can policy makers and funding organizations help?