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Panelists Lisa Amini, IBM Ashok Srivastava, NASA Ames

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Presentation on theme: "Panelists Lisa Amini, IBM Ashok Srivastava, NASA Ames"— Presentation transcript:

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? 


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