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
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Enhancing MOVES Transportation and Air Quality Analysis by Integrating with Simulation-Based Dynamic Traffic Assignment Yi-Chang Chiu, University of Arizona.
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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.
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
• 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
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
• 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
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. [email protected]