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

• To present, through a case study, an integrated modeling framework of MOVES and simulationbased 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

• 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

• 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

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

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

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

Integrated Framework Component I :
( Dyn amic u rban s ystems for
T ransportation)
• 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

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

• 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

• 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
Data sources for MOVES project-level application
Travel models
Link characteristics
Driving Pattern
Vehicle Operating Modes
Vehicle Fleet Characteristics
Local source
Meteorological info
Fuel supply
Inspection/ Maintenance
Program
14

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
Link Roadway link characteristics
(Length, grade, average speed)
Link Drive Schedule
Operating Mode
Distribution
Speed/ time trace second by second
Operating mode distribution defined jointly by speed, VSP
(a)roadway links – optional
(b) off-network link - required
Link Source Type Hour Vehicle fleet composition/ link
Possible Source
User Defined
DTA models
DTA models
DTA models
16

• 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

Simulation based
Dynamic Traffic
Assignment Model
Built-in Converter to Link by Link
Operating Mode
Distribution
MOVES
18

Modification to DynusT Traffic Activity Output: Built in
Converter to Link by Link Operating Mode Distribution at Converged Iteration
Move-switch on and output interval in
Parameter.dat
moves_input.dat
At time t , for each vehicle n with prevailing speed V t and previous speed V t-1
Compute
 acceleration/deceleration = ( V t -V t-1 )/SimInterval


Operating mode bin count ++1

Total Count ++1
No t = t + 1
End of Sim?
MovesOut_Links_Hour_1
MovesOut_LinkSourceTypes_Hour_1.csv
MovesOut_opmodedistribution_Hour_1.csv
MovesOut_offNetwork_Hour_1.csv
Yes
MovesOut_Links_Hour_2
MovesOut_LinkSourceTypes_Hour_2.csv
MovesOut_opmodedistribution_Hour_2.csv
MovesOut_offNetwork_Hour_2.csv
Yes
…..
MovesOut_Links_Hour_n
MovesOut_LinkSourceTypes_Hour_n.csv
MovesOut_opmodedistribution_Hour_n.csv
MovesOut_offNetwork_Hour_n.csv
MOVES Excel Input File
Links opmodedistribution
LinkSourceType
OffNetwork
MOVES Excel Input File
Links opmodedistribution
LinkSourceType
OffNetwork
MOVES Excel Input File
Links opmodedistribution
LinkSourceType
OffNetwork
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Data Item
Second Stage
Identifying sources for and preparing local data
Description Possible Sources
Source Type Age
Distribution
Off- Network
Meteorology
Fuel Supply
Inspection/
Maintenance Program
Vehicle age distribution
Off-network represents TAZs to model start emissions
• Local vehicle registration
• Converted from MOBILE
• MOVES default data
• DTA models/activity based models
Local specific temperature and humidity information
Fuel supply parameters
I/M program parameters
• Local specific
• Converted from MOBILE
• MOVES default data
• Local specific
• MOVES default data
• Local specific
• MOVES default data
20

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

• Part 1: improvement vs. baseline
• Part 2: local data vs. MOVES default
22

• Emission analyses focus on CO
2 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
Source: Google Map
Source: DynusT simulation 23

• 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

VHT (hrs)
VMT (miles)
Total Stop Time (hrs)
Baseline
3,569
148,076
550
Improvement
3,130
141,775
338
% Change
12.3%
4.3%
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
Baseline Improvement
26

58%
Baseline
19%
23%
Low-speed
Medium-speed
High-speed
62%
Improvement
19%
Low-speed
19%
Medium-speed
High-speed
27

6:00 - 6:59 AM Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) VMT
LDV 45,309 13,936 44,558 12,983 -1.7%
LDT
HDT
4,553
445
1,909
730
4,596
428
1,877
621
0.9%
-3.8%
Total
Baseline
50,307 16,575
Improvement
49,581 15,481
% change:
Impr. v.s. base
-1.4%
CO2e
-6.8%
-1.7%
-14.9%
-6.6%
7:00 - 7:59 AM
Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) VMT
LDV
LDT
HDT
86,849
8,954
726
26,031
3,657
1,199
84,392
9,056
851
23,644
3,593
1,309
-2.8%
1.1%
17.2%
Total 96,528 30,887 94,299 28,545 -2.3%
8:00 - 8:59 AM Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) VMT
LDV 125,784 36,263 121,689 33,532 -3.3%
LDT
HDT
12,825
1,120
5,077
1,719
13,378
1,180
5,098
1,649
4.3%
5.4%
Total 139,730 43,058 136,247 40,279 -2.5%
CO2e
-9.2%
-1.7%
9.2%
-7.6%
CO2e
-7.5%
0.4%
-4.1%
-6.5%
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9:00 - 9:59 Am
Baseline Improvement % change:
Imp vs. Base
Source Type VMT (mile) CO2e (kg) VMT (mile) CO2e (kg) VMT
LDV 194,152 108,055 190,550 92,848 -1.9%
CO2e
-14.1%
LDT
HDT
20,453
1,802
15,190
5,025
20,346
1,945
13,368
4,758
-0.5%
7.9%
-12.0%
-5.3%
Total 216,407 128,270 212,842 110,974 -2% -13%
• Variation in VMT and CO
2 emissions (total and by source type) are consistent over the fourhour period
• CO
2 emissions benefit in the improvement scenario is related to:
– VMT reductions
– shift in operating mode distributions (reduced stop time and improved travel speed)
29

• 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

• Using the same baseline scenario as presented previously for the Sacramento case study
• Running MOVES in separate runs with
1. 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
31

6:00 - 6:59
Baseline
(Op. Mode Distribution)
Baseline
(MOVES default Drive Schedule)
Source Type VMT (mile) CO2e (kg) VMT (mile)
LDV 45,309 13,936 45,309
LDT
HDT
4,553
445
1,909
730
4,553
445
Total 50,307 16,575 50,307
CO2e (kg)
16,359
2,401
941
19,701
% change:
Default vs. Op Mode
CO2e
17.4%
25.8%
28.9%
18.9%
7:00 - 7:59
Source Type VMT (mile) CO2e (kg) VMT (mile)
LDV
LDT
HDT
86,849
8,954
726
26,031
3,657
1,199
86,849
8,954
726
Total 96,528 30,887 96,528
CO2e (kg)
30,821
4,649
1,543
37,013
8:00 - 8:59 Source Type VMT (mile) CO2e (kg) VMT (mile)
LDV 125,784 36,263 125,784
LDT
HDT
12,825
1,120
5,077
1,719
12,825
1,120
Total 139,730 43,058 139,730
CO2e (kg)
43,816
6,566
2,353
52,736
CO2e
18.4%
27.1%
28.7%
19.8%
CO2e
20.8%
29.3%
36.9%
22.5%
32

9:00 - 9:59
Baseline
(Op. Mode Distribution)
Baseline
(MOVES default Drive Schedule)
Source Type VMT (mile) CO2e (kg) VMT (mile)
LDV 194,152 108,055 194,152
LDT
HDT
20,453
1,802
15,190
5,025
20,453
1,802
Total 216,407 128,270 216,407
CO2e (kg)
101,146
15,086
3,994
120,226
% change:
Default vs. Op Mode
CO2e
-6.4%
-0.7%
-20.5%
-6%
• Q/A check: VMT by source type remains the same;
• Results for the first 3 hours: using MOVES default drive schedules yields much higher CO
2 emissions;
• Results for hour 4: pattern is opposite.
33

Source: User Guide for MOVES2010a (EPA, 2010), pp 66.
34

• 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

• 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.
36

• 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

• 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