1. “Green” PORTAL: Adding Sustainability Performance Measures to a Transportation Data Archive Emissions Modeling

2. Outline Objectives Freeway emissions factors Emissions models MOBILE 6 model PORTAL and model inputs Emissions measures Conclusions

3. Green PORTAL Project Objectives: Investigate Sustainability Performance Indicators Address environmental, economic, and social sustainability Establish Modeling and Calculation ProceduresIncorporate measures into PORTAL For performance reporting, planning, and operations

4. Motivations? Internationally, road transport is the largest anthropogenic source of urban air pollution. Beyond emissions, transportation is a heavy user of society’s time and energy resources.

5. Sustainability Performance Measures Using Archived ITS Data: 1.Emissions Estimates 2.Fuel Consumption 3.Cost of Delay 4.Person Mobility (PMT, PHT, PHD) (this presentation)

6. Emissions Modeling and Estimation

7. Factors Affecting Emissions Primary determinant VMT sometimes used as surrogate measure for emissions Travel Congestion and speed variability are important factors in urban emissions High speeds lead to fuel enrichment and heavy emissions Low speeds generate high emissions rates Traffic Modifying individual driving behaviors can affect emissions by 5 to 25% Fluidity and steadiness of speed yield the least emissions Aggressive accelerations and high speeds generate the most emissions Driver Behavior Steep highway grades cause increased emissions, especially at high speeds Surface roughness can increase emissions by increasing rolling resistance Roadway Affects mostly evaporative (non-exhaust) emissions Measure temperature, humidity, and sun exposure Weather Vehicles determine how above factors are translated into emissions Assess vehicle fleet distribution and usage over facility, vehicle class, and age Vehicle

8. Emissions Estimation Regional Fuel Sales Average Speed Models Modal Models Method Carbon balance with fuel sales Emissions rates tied to roadway average speed Emissions based on individual vehicle modes of operation Ideal Scope Macro (regional, state, and national GHG inventories) Meso to Macro Micro (link and segment estimates) Advantages Minimal data needs Only needs speed and travel data Can be improved with other inputs (speed distribution) Captures more influences (Roadway and driver) Disadvantages GHG emissions only Low time and space resolution Does not capture driver and roadway effects Approximates traffic effects Require extensive, detailed data (instantaneous speed and acceleration, vehicle fleet)

9. Average Speed Emissions Models Model Development Process: Record Drive Cycles Probe vehicles on complete trips Representative set of conditions Key to accuracy of model Test Vehicles Run vehicles through drive cycles on a dynamometer Representative set of vehicles from roadway fleet Important to capture range of conditions, size, age, etc. Avg. Speed Emission Rates Link emissions to vehicle classes at average drive cycle speeds Facility-specific drive cycles can capture congestion effects Calculate Emissions with rates and travel Uses VMT and emissions rates Emissions rates can be modified by other inputs (weather, fuel programs, etc.)

10. Some Average Speed Model Considerations Does not fully capture speed dynamics (though facility-specific drive cycles can approximate) Using speed distributions (as opposed to simply mean speed) can increase estimates by up to 9% Accuracy increased with other inputs: hourly and roadway vehicle fleet, weather, facility type, fuel programs, etc. Accuracy relies on relevance of drive cycles and tested vehicles All emissions models have a significant level of uncertainty

11. MOBILE 6 1.Created by U.S. Environmental Protection Agency 2.This version (6) released January 2001; MOBILE models date back to 1978 3.Standard usage in North America for regulatory compliance (Clean Air Act) 4.Available free at: http://www.epa.gov/otaq/m6.htmhttp://www.epa.gov/otaq/m6.htm 5.Soon to be replaced by MOVES model from EPA (a robust average speed emissions model)

12. MOBILE 6.2 1.New facility-specific drive cycles recorded in modern American cities 2.Updated vehicles, emissions rates, regulatory programs, and driver behaviors 3.Fuel consumption and CO 2 estimates not speed- dependent (only based on fuel and fleet data) 4.Non-specified parameters default to national averages (many county-specific data available from the EPA) Improvements and caveats

13. PORTAL portal.its.pdx.edu Regional transportation data archive at PSU

14. Raw Data from PORTAL 20-second count, occupancy, and speed from ~600 inductive loop detectors on the Portland metropolitan freeway system -Hourly weather data also available -Auto/truck split estimates calculated from 20-second occupancy and speed

15. Performance Measure Methodology MOBILE inputs generated from PORTAL and gathered local dataMOBILE model run for locations and time periods of interestMOBILE output database processed to establish emissions rates Emissions rates combined with PORTAL travel data (VMT) to determine freeway segment emissions

16. MOBILE Model Input Parameter Summary Input Parameter Data Source(s) Difficulty to Obtain General Sensitivity of Performance Measures Hourly VMT PORTAL Low High Hourly Speed Distributions PORTAL Low High Vehicle Fleet PORTAL and Averages Med Medium – High Inspection Programs OR DEQ and Averages Med-High Low – Medium Fuel Programs US EPA and Averages Med-High Low – Medium Weather PORTAL Low

17. Hourly CO 2 Estimate I-5 MP 302.5 (1.4 mile section)

18. Volatile Organic Compounds I-5 MP 302.5 (1.4 mile section)

19. VOC Emissions From Congestions I-5 MP 302.5 (1.4 mile section)

20. CO Emissions From Congestions I-5 MP 302.5 (1.4 mile section)

21. A Quick Comparison... Note: There are many other factors (temperature) and sources (non-mobile) for CO in Portland. This was simply a sample visual comparison, not a correlation analysis.

22. Future Improvements More local data More high-resolution local fleet, fuel, and inspection data More robust CO2 model Measure congestion effects with speed-based estimates Forthcoming MOVES model will improve Sensitivity of measures to input parameters Assess benefits of using local data over national averages Improve truck counting methodology Current implementation has low accuracy Important to other sustainability measures as well

23. Conclusion These “green” performance measures offer key transportation system sustainability indicators that can readily be calculated from existing PORTAL data. While these measures can offer new insights, they rely on the accuracy of the archived data as well as the models. The next step in this project will be online, automated implementation of these measures based on the methods described here.

24. THANK YOU! Questions? - Comments? Funding and support for this project is provided by the National Science Foundation, Oregon Department of Transportation, Federal Highway Administration, City of Portland, TriMet and Metro. Special thanks to the PORTAL development team, PORTAL users and the TransPort ITS committee for their feedback and support. Thank you to Dr. Robert Bertini, Portland State University Acknowledgments: Photo credits: Julie Verdini and PORTAL