Yi-Chang Chiu , University of Arizona Jane Lin , University of Illinois Chicago

1. Enhancing MOVES Transportation and Air Quality Analysis by Integrating with Simulation-Based Dynamic Traffic Assignment Yi-Chang Chiu, University of Arizona Jane Lin, University of Illinois Chicago Suriya Vallamsundar, University of Illinois Chicago Song Bai, Sonoma Technology, Inc. TRB Planning Application Conference, Reno, NV May 9, 2011

2. Objectives • To present, through a case study, an integrated modeling framework of MOVES and simulation-based dynamic traffic assignment (SBDTA) model, i.e., DynusT, especially for project level emission analyses • To share our experience specifically in • How to integrate a SBDTA model and MOVES • How to properly run and extract traffic activity outputs from a SBDTA model • Project level emission estimation in MOVES • Differences in using MOVES default drive schedule (i.e., specifying only link average speed) versus local specific operating mode distribution input 2

3. Motivations of Our Study MOVES is the new EPA regulatory mobile emissions models for transportation conformity analyses. MOVES is capable of much finer spatial and temporal emission modeling than its predecessor MOBILE6 Few research efforts exist in integrating MOVES with transportation models 3

4. Literature Review • Most popular integration of traffic simulation and emission models in the U.S is between the VISSIM and CMEM (Comprehensive Modal Emissions Model) • Nam, E.K., C.A. Gierczak and J.W. Butler. 2003; Stathopoulos, F.G. and Noland, R.B. 2003; Noland, R.B. and Quddus, M.A. 2006; Chen, K. and L. Yu., 2007. • Integrations between CMEM and other traffic simulation models • Barth, M. C. Malcolm, 2001; Malcolm, C., Score, G and Barth, M. 2001; Tate, J. E., Bell, M. C and Liu, R. 2005 • Integration between MOVES and traffic simulation models is very limited due to the fact that MOVES is new • Integration between TRANSIMS and MOVES by FHWA 4

5. Simulation-Based Dynamic Traffic Assignment • Iterations between • Mesoscopic traffic simulation • Dynamic user equilibrium (vehicles departing at the same time between same OD pair has the same experienced travel time) • SBDTA retains advantages of: • Macro models – large-scale assignment (but with more realistic congestion patterns) • Micro models – high-fidelity traffic flow dynamics (but 1000+ times faster simulation) • Improved temporal and spatial resolutions at low computational cost 5

6. Why Using Dynamic Traffic Assignment to Support MOVES? • Assignment is the linchpin between travel demand model and Mobile6/EMFAC • Capture travelers’ route choice learning network changes. • This linkage remains crucial when linking MOVES with traffic simulation models • Without which, vehicles may be at wrong locations at wrong time – misleading VMT and VHT. • One-shot micro simulation (no assignment) is not consistent with assignment/learning and likely to produce inaccurate and/or counterintuitive results. • Micro models extracted from TDM sub-area cut may gridlock – OD in TDM not roadway capacity constrained 6

7. Modeling Demand/Supply Interactions in Simulation-Based DTA • Four fundamental transportation system elements • Infrastructure • Geometries • Traffic flows • Speed, density, flow, shockwaves, queue • Control systems • Signals, ramp meters • Information • Traveler information, message sings 7

8. Integrated Framework Component I: (DynamicurbansystemsforTransportation) • Mesoscopic Dynamic Traffic Assignment (DTA) • Developed since 2002, supported by FHWA, used in 20+ regions since 2005 (Univ. of Arizona) • SCAG, PAG, MAG, DRCOG, PSRC, SFCTA, HGAC, Las Vegas, ELP, NC Triangle, Guam, Florida, SEMCOG, Toronto, SACOG, Mississippi, North Virginia, I-95, US36, New York, Bay Area) • 50+ agency/firm/university users internationally • Open Source in 2011 (http://www.dynust.net) 8

9. Integrated Framework Component II: MOVES • EPA’s Next Generation Emission Model • “Modal based approach” for emission factor estimation • Four major functions - Total activity generator, Source bin distribution generator, Operating mode distribution generator and Emission calculator • Data driven model • Data are stored and managed in MySQL database • Outputs total emission inventories and composite emission rates • Three scales of analysis • National • County • Project 9

10. MOVES Modal Approach • Associates emission rates with vehicle specific power (VSP) and speed • VSP – power placed on vehicle under various driving modes • Distributes activities using several temporal resolutions (e.g., hours of day, weekday vs. weekend) • Classifies vehicles consistent with HPMS data 10

11. MOVES – Total Emission Estimation 11

12. Data sources for MOVES project-level application Local source Meteorological info Fuel supply Inspection/ Maintenance Program Travel models Link characteristics Driving Pattern Vehicle Operating Modes Vehicle Fleet Characteristics MOVES Input Data • National • National default database and use of allocation factors • County • Use of default data and regional user specific data • Project level • Detailed local specific data 14

13. MOVES Activity Data from Transportation Models • Key travel model outputs for emissions modeling • Volume (or VMT) • Speed (average for each roadway link) • Fleet mix (cars vs. trucks) • MOVES requires data at higher resolution than that is provided by traditional travel demand models • Literature shows using processed traditional travel modeling data introduces noticeable discrepancies in vehicle emissions estimates • Activity based travel demand models and simulation based DTA – suited to bridge travel activities and MOVES

14. Integration: Data Flow from DynusT to MOVES 16

15. Implementation of Integration (I) • Two stages are involved in integrating the two components for project level analysis First Stage Modifying DynusT to output traffic data as required by MOVES • Network Parameters • Fleet Characteristics • Driving Pattern – Operating Mode versus Drive Schedule Link • Operating modes - “modes” of vehicle activity with distinct emission rates. • Running activity has modes distinguished by their VSP and instantaneous speed • Start activity has modes distinguished by soak time 17

16. Proposed Integrated Framework Simulation based Dynamic Traffic Assignment Model Built-in Converter to Link by Link Operating Mode Distribution MOVES 18

17. Modification to DynusT Traffic Activity Output: Built in Converter to Link by Link Operating Mode Distributionat Converged Iteration 19

18. Implementation of Integration (II) Second Stage Identifying sources for and preparing local data 20

19. Summary Features of the Integrated Framework • Integrated framework: DynusT (DTA) + MOVES – advantages of DTA over static traffic assignment and one-shot simulation • Run Time integration with built in converters of traffic activity output from traffic simulation model to MOVES required operating mode distribution format 21

20. 6. Sacramento Case Study (Parts 1 and 2) • Part 1: improvement vs. baseline • Part 2: local data vs. MOVES default 22

21. 437 nodes, 768 links, 66,150 vehicles (hourly demand variation: 23/22/18/37%) Fleet mix: 90% passenger vehicles and 10% heavy-duty vehicles Westbound congestion significant Case Study Setup: Baseline • Emission analyses focus on CO2 from on-road traffic • Time period: 6-10 AM in a weekday, February 2009 • Downtown Sacramento area Source: Google Map 23 Source: DynusT simulation

22. Case Study Part 1: Improvement Scenario • Improving freeway interchange to relieve congestion • Increase off-ramp and downstream interchange capacity • Signal re-timing for higher off-ramp traffic throughput Source: Google Map 24

23. Improvement vs. Baseline : Traffic Activities Both VHT and VMT were reduced (12.3% and 4.3%) due to interchange improvement Total stop time was reduced by 38.5% (directly related to changes in operating mode distributions) 25

24. Improvement vs. Baseline : Traffic Activities Speed improvement on Business Loop I-80 main lanes Baseline Improvement 26

25. Improvement vs. Baseline : Operating Mode 27

26. Hour by Hour Comparison 28

27. Case Study Part 1: Conclusion • Variation in VMT and CO2 emissions (total and by source type) are consistent over the four-hour period • CO2 emissions benefit in the improvement scenario is related to: • VMT reductions • shift in operating mode distributions (reduced stop time and improved travel speed) 29

28. Case Study Part 2: Local vs. Default Data • MOVES default drive schedule vs. user-supplied operating mode distribution • How much difference in emissions estimates? • Use of MOVES default drive schedule • Easy to implement in practice • Potential limitations • Use of project-level operating mode distribution • Requires data preparation and conversion • Presumably more appropriate for emissions modeling 30

29. Comparison Scenarios Setup • Using the same baseline scenario as presented previously for the Sacramento case study • Running MOVES in separate runs with • Link average speeds, i.e., using MOVES default drive schedules, to replace user supplied operating mode distribution • User-supplied operating mode distribution, i.e., the baseline scenario in the previous case study 31

30. Comparison Results 32

31. Comparison Results (cont’d) • Q/A check: VMT by source type remains the same; • Results for the first 3 hours: using MOVES default drive schedules yields much higher CO2 emissions; • Results for hour 4: pattern is opposite. 33

32. Using MOVES Default Drive Schedules Source: User Guide for MOVES2010a (EPA, 2010), pp 66. 34

33. Part 2: Conclusion (Local vs. Default Data) • In this case (especially hour 4 results), for links with speed below 5.8 mph, MOVES does not provide HDV emissions if default drive schedules were used. • Similar situation for LDV emissions (speed < 2.5 mph) • The missed emissions associated with low-speed links contributed to underestimation in MOVES when using default drive schedules. • Using local-specific data under a highly congested condition seems important to produce more consistent results than using default drive schedules. 35

34. Overall Summary and Next Steps • An integrated modeling framework of DynusT and MOVES - connecting and automating the modeling process from DTA to MOVES project-scale applications • Advantages of the integrated model in policy analysis • Using local-specific traffic activity inputs and operating mode distributions is important • MOVES default drive schedules are convenient to use but may become questionable when modeling highlycongested traffic; further investigation is needed. 36

35. Future Research • Use DynusT project-specific drive schedules in MOVES modeling • Compare static traffic assignment with dynamic traffic assignment for emissions modeling • Conduct a series of sensitivity analyses with selected traffic and MOVES parameters 37

36. Acknowledgments • This research is part of the TRB SHRP C10 project led by Cambridge Systematics, Inc. • This study is a joint effort among: Dr. Song Bai, Sonoma Technology, Inc. sbai@sonomatech.com Dr. Yi-Chang Chiu, University of Arizona chiu@email.arizona.edu Dr. Jane Lin, University of Illinois at Chicago janelin@uic.edu Ms. Suriya Vallamsundar, University of Illinois at Chicago svalla2@uic.edu 38