Enhancing MOVES Transportation and Air Quality Analysis by Integrating with Simulation-Based

Dynamic Traffic Assignment

Yi-Chang Chiu, University of ArizonaJane Lin, University of Illinois ChicagoSuriya Vallamsundar, University of Illinois ChicagoSong Bai, Sonoma Technology, Inc.

TRB Planning Application Conference, Reno, NVMay 9, 2011

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

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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

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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

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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

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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

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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

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Integrated Framework Component I: (Dynamic urban systems for

Transportation)

• 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)

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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

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

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MOVES – Total Emission Estimation

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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

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Travel models Link characteristicsDriving PatternVehicle Operating ModesVehicle Fleet Characteristics

Local sourceMeteorological infoFuel supplyInspection/ Maintenance Program

Data sources for MOVES project-level application

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

Integration: Data Flow from DynusT to MOVES

Data Item Description Possible Source

Link Roadway link characteristics(Length, grade, average speed)

User Defined

Link Drive Schedule Speed/ time trace second by second

DTA models

Operating Mode Distribution

Operating mode distribution defined jointly by speed, VSP (a)roadway links – optional (b) off-network link - required

DTA models

Link Source Type Hour Vehicle fleet composition/ link DTA models

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Implementation of Integration (I)

• Two stages are involved in integrating the two components for project level analysis

First StageModifying 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

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Proposed Integrated Framework

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Simulation based Dynamic Traffic Assignment Model

MOVES

Built-in Converter to Link by Link Operating Mode Distribution

Modification to DynusT Traffic Activity Output: Built in Converter to Link by Link Operating Mode Distribution

at Converged Iteration

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moves_input.dat

At time t, for each vehicle n with prevailing speed Vt andprevious speed Vt-1Compute acceleration/deceleration = (Vt-Vt-1)/SimInterval Operating mode bin count ++1 Total Count ++1

Not = t + 1

End of Sim?

MovesOut_Links_Hour_1MovesOut_LinkSourceTypes_Hour_1.csv

MovesOut_opmodedistribution_Hour_1.csvMovesOut_offNetwork_Hour_1.csv

Yes

Move-switch on and outputinterval in

Parameter.dat

MOVES Excel Input FileLinks

opmodedistributionLinkSourceType

OffNetwork

MovesOut_Links_Hour_2MovesOut_LinkSourceTypes_Hour_2.csv

MovesOut_opmodedistribution_Hour_2.csvMovesOut_offNetwork_Hour_2.csv

MovesOut_Links_Hour_nMovesOut_LinkSourceTypes_Hour_n.csv

MovesOut_opmodedistribution_Hour_n.csvMovesOut_offNetwork_Hour_n.csv

Yes

…..

MOVES Excel Input FileLinks

opmodedistributionLinkSourceType

OffNetwork

MOVES Excel Input FileLinks

opmodedistributionLinkSourceType

OffNetwork

Implementation of Integration (II)

Second StageIdentifying sources for and preparing local data

Data Item Description Possible Sources

Source Type Age Distribution

Vehicle age distribution • Local vehicle registration • Converted from MOBILE • MOVES default data

Off- Network Off-network represents TAZs to model start emissions

• DTA models/activity based models

Meteorology Local specific temperature and humidity information

• Local specific• Converted from MOBILE • MOVES default data

Fuel Supply Fuel supply parameters • Local specific• MOVES default data

Inspection/ Maintenance Program

I/M program parameters • Local specific• MOVES default data

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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

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6. Sacramento Case Study (Parts 1 and 2)

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• Part 1: improvement vs. baseline• Part 2: local data vs. MOVES default

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

– 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

23Source: Google Map

Source: DynusT simulation

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

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Source: Google Map

Improvement vs. Baseline : Traffic Activities

Baseline Improvement % Change

VHT (hrs) 3,569 3,130 12.3%

VMT (miles) 148,076 141,775 4.3%

Total Stop Time (hrs) 550 338 38.5%

• 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)

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Speed improvement on Business Loop I-80 main lanes

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Baseline Improvement

Improvement vs. Baseline : Traffic Activities

Improvement vs. Baseline : Operating Mode

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Baseline

19%

23%58%

Low-speed

Medium-speed

High-speed

Improvement

19%

19%

62%

Hour by Hour Comparison

6:00 - 6:59 AM Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) VMT CO2eLDV 45,309 13,936 44,558 12,983 -1.7% -6.8%LDT 4,553 1,909 4,596 1,877 0.9% -1.7%HDT 445 730 428 621 -3.8% -14.9%Total 50,307 16,575 49,581 15,481 -1.4% -6.6%

7:00 - 7:59 AM Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) VMT CO2eLDV 86,849 26,031 84,392 23,644 -2.8% -9.2%LDT 8,954 3,657 9,056 3,593 1.1% -1.7%HDT 726 1,199 851 1,309 17.2% 9.2%Total 96,528 30,887 94,299 28,545 -2.3% -7.6%

8:00 - 8:59 AM Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) VMT CO2eLDV 125,784 36,263 121,689 33,532 -3.3% -7.5%LDT 12,825 5,077 13,378 5,098 4.3% 0.4%HDT 1,120 1,719 1,180 1,649 5.4% -4.1%Total 139,730 43,058 136,247 40,279 -2.5% -6.5%

Baseline Improvement % change:Impr. v.s. base

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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)

9:00 - 9:59 Am Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) VMT CO2eLDV 194,152 108,055 190,550 92,848 -1.9% -14.1%LDT 20,453 15,190 20,346 13,368 -0.5% -12.0%HDT 1,802 5,025 1,945 4,758 7.9% -5.3%Total 216,407 128,270 212,842 110,974 -2% -13%

Baseline Improvement % change: Imp vs. Base

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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

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Comparison Scenarios Setup

• Using the same baseline scenario as presented previously for the Sacramento case study

• Running MOVES in separate runs with1. Link average speeds, i.e., using MOVES default drive

schedules, to replace user supplied operating mode distribution

2. User-supplied operating mode distribution, i.e., the baseline scenario in the previous case study

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Comparison Results

% change:(Op. Mode Distribution) Default vs. Op Mode

6:00 - 6:59 Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) CO2eLDV 45,309 13,936 45,309 16,359 17.4%LDT 4,553 1,909 4,553 2,401 25.8%HDT 445 730 445 941 28.9%Total 50,307 16,575 50,307 19,701 18.9%

7:00 - 7:59 Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) CO2eLDV 86,849 26,031 86,849 30,821 18.4%LDT 8,954 3,657 8,954 4,649 27.1%HDT 726 1,199 726 1,543 28.7%Total 96,528 30,887 96,528 37,013 19.8%

8:00 - 8:59 Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) CO2eLDV 125,784 36,263 125,784 43,816 20.8%LDT 12,825 5,077 12,825 6,566 29.3%HDT 1,120 1,719 1,120 2,353 36.9%Total 139,730 43,058 139,730 52,736 22.5%

Baseline Baseline(MOVES default Drive Schedule)

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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.

% change:Default vs. Op Mode

9:00 - 9:59 Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) CO2eLDV 194,152 108,055 194,152 101,146 -6.4%LDT 20,453 15,190 20,453 15,086 -0.7%HDT 1,802 5,025 1,802 3,994 -20.5%Total 216,407 128,270 216,407 120,226 -6%

Baseline Baseline(Op. Mode Distribution) (MOVES default Drive Schedule)

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Using MOVES Default Drive Schedules

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Source: User Guide for MOVES2010a (EPA, 2010), pp 66.

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.

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Overall Summary and Next Steps

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• 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 highly congested traffic; further investigation is needed.

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

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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

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