Context the governor s climate roadmap
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Context: the Governor’s Climate Roadmap. Overall goals quantify the relationship between land use and transportation sector greenhouse gas (GHG) emissions evaluate a range of strategies including incentives, regulations and other forms of land use control to reduce GHG

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Context: the Governor’s Climate Roadmap

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Context the governor s climate roadmap

Context: the Governor’s Climate Roadmap

Overall goals

  • quantify the relationship between land use and transportation sector greenhouse gas (GHG) emissions

  • evaluate a range of strategies including incentives, regulations and other forms of land use control to reduce GHG

    Purpose of this meeting

  • introduce new datasets combining population, land use and business locations and using grid approach

  • review MassGIS model for trips with residential origin

  • discuss a cooperative approach - can MassGIS work provide input into existing tranportation models in modeling GHG ?


Automated approach using gis

Automated approach using GIS

  • Create statewide grid of 250 meter cells

  • Allocate block level Census household and population data to grid cells based on residential land use

  • Map locations of all common destinations such as schools, grocery stores, libraries, restaurants etc. and assign to grid cells

  • Calculate distance from every cell to nearest (or several nearest) destination

  • Using a set of trip weights from National Household Travel Survey, estimate VMT for household origin trips

  • Calibrate model using published VMT figures


Context the governor s climate roadmap

First question:

Where do people live?


Basemap se mass

Basemap – SE Mass.


Census geography no households

Census geography – no. households


Census geography plus land use

Census geography plus land use


Allocation of population to res areas

Allocation of population to res. areas


Why use a grid cell approach

Why use a grid cell approach?

  • Many operations can be performed on grids with little computational expense including addition, multiplication, logical tests, spatial means, distance to nearest feature etc.

  • Small grid cell size supports modeling at a very local scale

  • Model can be easilty calibrated to published results and can be run iteratively with different scenarios


Grid cell representation

Grid cell representation


Zoom out

Zoom out ...


Household density no 250 m cell

Household density (no. / 250 m cell)


Context the governor s climate roadmap

Second question:

Where do people go?


Residential areas plus business locations

Residential areas plus business locations


Highlight a single kind of destination

Highlight a single kind of destination


Context the governor s climate roadmap

grocerystore

grocerystore

Euclidean distance grid

4 cells

4

2

6 cells

For each type of destination,

create a grid that records the distance

to the nearest location in every cell –

this is called a “euclidean distance grid”


Context the governor s climate roadmap

Focalsum grid

4 cells

2

For each type of destination, count the number of destinations at various distances from each cell - this is called a “focal sum grid”


Trip weights partial listing

Trip Weights (partial listing)

need a surrogate for this item

don’t use closest destination, use

focal sum distance instead for 3-5 destinations


Grid cell computations

Grid cell computations

Summing up grid cell values for each household, using just euclidian distance examples:

distance to groceries x % of trips = total distance traveled to groceries / total trips

distance to hardware x % of trips = total distance traveled to hardware / total trips

distance to church x % of trips = total distance traveled to church / total trips

etc. for 30 different destinations

SUM of grid cell values x total number of trips = total distance traveled

For each grid cell, we estimate the total distance travelled per household and we know the number of households, so we can estimate the total VMT for all households


Use of grid to estimate household trips

Use of grid to estimate household trips

 (Ti * Di) Ttotal

Wi = Ti / Ttotal Wi = 1

weight = trips to destination i divided by total trips

Vtotal = (Wi * Di) =

cell value = sum of weights multiplied by distances for all destinations = total travel over total number of trips for each household

VMThouseholds = H * (Ti * Di) = H * Vtotal * Ttotal

total vehicle miles = number of households times total travel for each household = number of households times cell value times total number of trips per household


All businesses with edg for grocery stores

All businesses with EDG for grocery stores


Edg for restaurants on the south shore

EDG for restaurants on the South Shore


Weighted average household vmt trip all non work destinations

Weighted average household VMT / trip (all non-work destinations)


Aggregate non work vmt

Aggregate non-work VMT


Aggregate commuter miles gross estimate

Aggregate commuter miles (gross estimate)


Aggregate all res origin miles

Aggregate all res. origin miles


Grid model results

Grid model results

Assuming....

  • 30% correction for “as the crow flies” based on limited sampling

  • 4.2 trips / household / day

  • 2000 Census and 2007 DNB

    model computes 145 million residential origin DVMT

    Point of the model is NOT to estimate VMT, however, but to understand variation in VMT based on geographic location

    Major gaps and problems:

  • JTW was based on census plus gross assumptions on average speed

  • social trips – not sure how to measure

  • weightings – is there a better survey

    Next steps

    – correlate with land use, population density, business density


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