Transcript Presentation - 15th TRB National Transportation Planning

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
Objectives
• 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
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
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
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:
(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)
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
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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
MOVES – Total Emission Estimation
11
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
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
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 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
Proposed Integrated Framework
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 Vt and
previous speed Vt-1
Compute
 acceleration/deceleration = (Vt-Vt-1)/SimInterval

 Operating mode bin count ++1
 Total Count ++1
No
t=t+1
End of Sim?
Yes
Yes
MovesOut_Links_Hour_1
MovesOut_Links_Hour_2
MovesOut_LinkSourceTypes_Hour_1.csv
MovesOut_opmodedistribution_Hour_1.csv
MovesOut_offNetwork_Hour_1.csv
MovesOut_LinkSourceTypes_Hour_2.csv
MovesOut_opmodedistribution_Hour_2.csv
MovesOut_offNetwork_Hour_2.csv
MOVES Excel Input File
MOVES Excel Input File
MOVES Excel Input File
Links
opmodedistribution
LinkSourceType
OffNetwork
Links
opmodedistribution
LinkSourceType
OffNetwork
Links
opmodedistribution
LinkSourceType
OffNetwork
…..
MovesOut_Links_Hour_n
MovesOut_LinkSourceTypes_Hour_n.csv
MovesOut_opmodedistribution_Hour_n.csv
MovesOut_offNetwork_Hour_n.csv
19
Implementation of Integration (II)
Second Stage
Identifying sources for and preparing local data
Data Item
Description
Possible Sources
Source Type Age
Distribution
Vehicle age distribution
•
•
•
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
Local specific
MOVES default data
I/M program parameters
Inspection/
Maintenance Program
Local vehicle registration
Converted from MOBILE
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
6. Sacramento Case Study (Parts 1 and 2)
• Part 1: improvement vs. baseline
• Part 2: local data vs. MOVES default
22
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
Source: Google Map
Source: DynusT simulation
23
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
Improvement vs. Baseline : Traffic Activities
VHT (hrs)
VMT (miles)
Total Stop Time (hrs)
Baseline
Improvement
% Change
3,569
3,130
12.3%
148,076
141,775
4.3%
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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Improvement vs. Baseline : Traffic Activities
Speed improvement on Business Loop I-80 main lanes
Baseline
Improvement
26
Improvement vs. Baseline : Operating Mode
Improvement
Baseline
19%
19%
58%
Low-speed
L
Medium-speed
M
19%
23%
High-speed
62%
H
27
Hour by Hour Comparison
Baseline
Improvement
6:00 - 6:59 AM
Source Type
LDV
LDT
HDT
Total
VMT (mile) CO2e (kg) VMT (mile) CO2e (kg)
45,309
13,936
44,558
12,983
4,553
1,909
4,596
1,877
445
730
428
621
50,307
16,575
49,581
15,481
% change:
Impr. v.s. base
VMT
CO2e
-1.7%
-6.8%
0.9%
-1.7%
-3.8%
-14.9%
-1.4%
-6.6%
7:00 - 7:59 AM
Source Type
LDV
LDT
HDT
Total
VMT (mile) CO2e (kg) VMT (mile) CO2e (kg)
86,849
26,031
84,392
23,644
8,954
3,657
9,056
3,593
726
1,199
851
1,309
96,528
30,887
94,299
28,545
VMT
-2.8%
1.1%
17.2%
-2.3%
CO2e
-9.2%
-1.7%
9.2%
-7.6%
8:00 - 8:59 AM
Source Type
LDV
LDT
HDT
Total
VMT (mile) CO2e (kg) VMT (mile) CO2e (kg)
125,784
36,263
121,689
33,532
12,825
5,077
13,378
5,098
1,120
1,719
1,180
1,649
139,730
43,058
136,247
40,279
VMT
-3.3%
4.3%
5.4%
-2.5%
CO2e
-7.5%
0.4%
-4.1%
-6.5%
28
Case Study Part 1: Conclusion
Baseline
9:00 - 9:59 Am
Source Type
LDV
LDT
HDT
Total
VMT (mile)
194,152
20,453
1,802
216,407
CO2e (kg)
108,055
15,190
5,025
128,270
Improvement
VMT (mile)
190,550
20,346
1,945
212,842
CO2e (kg)
92,848
13,368
4,758
110,974
% change:
Imp vs. Base
VMT
CO2e
-1.9%
-14.1%
-0.5%
-12.0%
7.9%
-5.3%
-2%
-13%
• Variation in VMT and CO2 emissions (total and
by source type) are consistent over the fourhour 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
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
Comparison Scenarios Setup
•
•
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
Comparison Results
Baseline
(Op. Mode Distribution)
Baseline
% change:
(MOVES default Drive Schedule) Default vs. Op Mode
6:00 - 6:59
Source Type VMT (mile) CO2e (kg)
LDV
45,309
13,936
LDT
4,553
1,909
HDT
445
730
Total
50,307
16,575
VMT (mile)
45,309
4,553
445
50,307
CO2e (kg)
16,359
2,401
941
19,701
CO2e
17.4%
25.8%
28.9%
18.9%
7:00 - 7:59
Source Type VMT (mile) CO2e (kg)
LDV
86,849
26,031
LDT
8,954
3,657
HDT
726
1,199
Total
96,528
30,887
VMT (mile)
86,849
8,954
726
96,528
CO2e (kg)
30,821
4,649
1,543
37,013
CO2e
18.4%
27.1%
28.7%
19.8%
8:00 - 8:59
Source Type VMT (mile) CO2e (kg)
LDV
125,784
36,263
LDT
12,825
5,077
HDT
1,120
1,719
Total
139,730
43,058
VMT (mile)
125,784
12,825
1,120
139,730
CO2e (kg)
43,816
6,566
2,353
52,736
CO2e
20.8%
29.3%
36.9%
22.5%
32
Comparison Results (cont’d)
Baseline
(Op. Mode Distribution)
9:00 - 9:59 Source Type
LDV
LDT
HDT
Total
VMT (mile)
194,152
20,453
1,802
216,407
CO2e (kg)
108,055
15,190
5,025
128,270
Baseline
% change:
(MOVES default Drive Schedule) Default vs. Op Mode
VMT (mile)
194,152
20,453
1,802
216,407
CO2e (kg)
101,146
15,086
3,994
120,226
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 CO2 emissions;
• Results for hour 4: pattern is opposite.
33
Using MOVES Default Drive Schedules
Source: User Guide for MOVES2010a (EPA, 2010), pp 66.
34
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
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 highly
congested traffic; further investigation is needed.
36
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
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]
Dr. Yi-Chang Chiu, University of Arizona [email protected]
Dr. Jane Lin, University of Illinois at Chicago [email protected]
Ms. Suriya Vallamsundar, University of Illinois at Chicago
[email protected]
38