November 28, 2006 CCOS On-Road Allocation Factors Page 1 Spatial & Temporal Allocation of On-Road Emissions CCOS Technical Committee November 28, 2006

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


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


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


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


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


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


Prior CCOS Work


Prior CCOS Work


CCOS: NOx, TOG, HDV 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


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



Freeway Link TOG

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Speed post processing has a significant effect on congested emissions



• 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


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

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Heavy Duty Vehicles

SJV Goods Movement Study Phase II (2004)


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


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


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


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

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Low

Note: cost assumptions in speaking notes window

Ver

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igh


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


Impact of Spline Function

Source: Atm. Env. V.38, issue 2, 305-319 (2004)



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.


Count Locations from Phase II truck Model



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.



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



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


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.

Documents

November 28, 2006 CCOS On-Road Allocation Factors Page 1 Spatial & Temporal Allocation of On-Road Emissions CCOS Technical Committee November 28, 2006