November 28, 2006 CCOS On-Road Allocation Factors Page 1 Spatial & Temporal Allocation of On-Road Emissions CCOS Technical Committee November 28, 2006 Prepared by: Tom Kear, Ph.D., P.E. Dowling Associates Debbie Niemeier, Ph.D., P.E. UC Davis
November 28, 2006 CCOS On-Road Allocation Factors Page 1
Spatial & Temporal Allocation of On-Road Emissions
CCOS Technical CommitteeNovember 28, 2006
Prepared by:
Tom Kear, Ph.D., P.E.Dowling Associates
Debbie Niemeier, Ph.D., P.E. UC Davis
November 28, 2006 CCOS On-Road Allocation Factors Page 2
Presentation Overview
• Preview of key issues
• On-road proportion & Prior CCOS work
• Major trends identified in the literature & heavy duty modeling practice
• Critical assumptions
• Findings
• Phase II priority projects
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Preview Of Key Issues
• The ITN used to develop the base-year (2000) inventory is not applicable to future years
• Heavy-duty vehicle activity, in general, is not being modeled, but is assigned to roads as a percentage of light duty vehicle activity
• Speed post-processing has been to shown dramatically affect emission estimates under certain conditions
• Current modeling techniques are not capturing the spatial distribution of weekend travel
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On-Road Proportion Of Emissions
• On-Road contributes about 1/3 of the ROG inventory
• Diesel vehicles are not an important source of ROG
SJV ROG (Tons/day)
On-Road, 105
Point, Area, &
Non-Road, 323
Calif. ROG (Tons/day)
Point, Area, &
Non-Road, 1,868
On-Road, 960
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On-Road Proportion Of Emissions
• On-Road contributes about 50% of the NOx inventory
• Trucks account for about 3% of VMT but 30% of on-road NOx
Calif . NOx (Tons/day)
On-Road, 1,752
Point, Area, &
Non-Road, 1,804
SJV NOx (Tons/day)
Point, Area, &
Non-Road, 280
On-Road, 222
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Prior CCOS Work
• BURDEN 2002 emissions allocated to grid cells using DTIM4
• Integrated Transportation Network (ITN) from individual county (loaded) travel demand model networks
• Temporal allocations assigned per BURDEN and available traffic counts
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Prior CCOS Work
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Prior CCOS Work
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CCOS: NOx, TOG, HDV NOx
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7/27
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7/28
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Day & Hour
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TOG NOX Heavy-Duty NOx
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Critical assumptions
• CCOS assumes uniform growth of vehicle activity across regions
• Note the variation in growth forecasts, ranging from none to more than 10x (e.g., 1,000%)
• ITN needs to be rebuilt using loaded networks for each analysis year (interpolated trip tables) prior to DTIM runs
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Prominent Trends in Literature
• Light/heavy vehicle ratio differ by day of week
• Less truck activity on weekends, but the ratio of LDV/HDT increases
• Ratios vary by geographic location
• Weekdays (Mon-Thurs) have similar temporal allocation
• Saturday and Sunday are often very different from each other
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Prominent Trends in Literature
Freeway Link TOG
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Freeway Link NOx
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LOS: A-C LOS: D-E LOS: F
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Speed post processing has a significant effect on congested emissions
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Prominent Trends in Literature
• Statewide HVMT accounts for only about 1% of the annual total. The low HVMT suggests that changes in harvest hauling traffic patterns will not dramatically affect emissions for a typical day.
• Current activity factor for nonagricultural unpaved roads underestimated vehicle activity for Forest and Woodland and Urban Residential areas, but overestimated vehicle activity in Grasslands, Sand dunes and Scrubland and Urban Interface areas.
Table 10. Annual unpaved road VMT in California
Harvest VMTNonharvest VMT Total Statewide VMT
4,945,329 468,023,838 472,969,167
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Heavy Duty Vehicles• Not modeled but captured during
calibration by increasing non-home-based-trips to match counts
• True freight models aggregate trip tables from inter county commodity flow data and regional gravity models.
• Trucks not well captured by SJV phase II truck model, or any of the 8 RTPA models.
• 2025 SJV Phase III truck model forecast is being extrapolated from 1978 commodity flow surveys
0
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100 to 249miles
250 to 499miles
500 to 749miles
750 to 999miles
1,000 to1,499 miles
1,500 to1,999 miles
2,000miles or
miles
% o
f T
rip
s
LA Area Bay Area Rural
~ 60% to 70% of trips are < 100 miles
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Heavy Duty Vehicles
SJV Goods Movement Study Phase II (2004)
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Critical assumptions• The current approach assumes weekend and
weekday trip distribution is identical, only the number of trips generated changes– Just matching base year creates a forecasting problem
because behavioral component is lacking
• Heavy duty vehicle activity is assumed to be distributed similarly to the light duty vehicle activity on all RTPA networks.
• Assumes that trip based emission factors are applicable to links
• Existing and future activity is assumed to follow the same spatial / temporal distributions
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Findings from Phase 1
• Areas of uncertainty – Spatial changes between weekday-weekend
activity– Where are the trucks?– Spatial mismatch between activity data &
emissions rates– Impact of better transportation data (refinement of
spatial network, speed post processing, and the treatment of trip ends)
– Impact of seasonality on agricultural goods movement
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Findings from Phase 1
• Best way to group daily hours of travel?
• Importance of speed post processing
• Trucks are not well represented
• Weekend activity is not well represented
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Phase II priority projectsTask Description Cost
1. 2010-2020 forecasts
Statewide Model, DTIM, spline smoothing.
$80 K
2. Improve truck data
Model truck activity on highways and arterials, integrate w/ task (1)
$75 K ($115 K if counts needed)
3. Speed post-processing
Identify best method and implement
$45 K
4. Improve weekend data (LDV)
Create weekend trip tables, validate/calibrate relative distributions
$75 K
5. Link-level EFs
Trucks from Lit or E55/E98 data, MOBILE6 for LDVs
$ 50 - $75 K
Hig
hM
oder
ate
Low
Note: cost assumptions in speaking notes window
Ver
y H
igh
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Phase II priority projects
1) 2010, 2015, 2020 on-road forecasts.– BURDEN 2007 control totals– Statewide model (rather than ITN) w/DTIM for
spatial allocation– Interpolate trip tables for intermediate year
assignments.– “Disperse” (via spline interpolation) the on-road
allocation to approximate the impact of network elements not explicitly modeled in the Statewide network
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Impact of Spline Function
Source: Atm. Env. V.38, issue 2, 305-319 (2004)
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Phase II priority projects
2) Improve truck activity estimates:– Reverse fit an OD table to observed truck
counts, use SJV Phase II goods movement model as an initial condition
– Base projections on TAZ employment growth
Rational: Heavy-duty truck activity is poorly understood.
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Count Locations from Phase II truck Model
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Phase II priority projects
3) Speed post processing link data– Post process speed data to represent hourly
conditions– Research into the sensitivity / appropriateness of
different formulations– SAS code to implement– Impacts highly congested links
Rational: As shown in the literature review, the impact of speed post processing on estimated emissions can be dramatic for links operating near and over capacity conditions.
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Phase II priority projects
4) Improve weekend spatial allocation – Incorporating behavioral characteristics into the
method (e.g., ratio OD tables by trip type and ITE data).
– Reverse fit OD tables to observed light duty counts
Rational: Trip making patterns change along with trip generation rates for weekend activity. Currently only trip rates are taken into account
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Phase II priority projects
5) Link level emission rates– Use emission rates and activity data with similar
spatial specificity – HDV emission rates from models in the literature– Option: use E55/E59 data to construct new rates
based on Kear & Niemeier 2006– Use light duty rates from MOBILE6– BURDEN 2007 still sets control totals
Rational: Link-based emissions rates are based on road segment level activity. BURDEN trip based rates include operation over all facility types
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Q & A
• What effect will time and resource constraints have on CCOS priorities?
• How does the on-road inventory uncertainty compare to that in the rest of the inventory?
• Different projects have different uncertainties and commensurate impacts
• Extrapolations from an inappropriate set of year 2000 assumptions would have little value. Internal consistency and a scientific/behavioral bases for on-road activity is critical.