2030 Higher Household Growth in Region Scenario: Travel Model Results and Accessibility Analysis

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2030 Higher Household Growth in Region
ScenarioTravel Model Results and Accessibility
Analysis
Item 3

• Presentation to the Joint Technical Working Group
  of the
• Regional Mobility and Accessibility Study
• April 15, 2005
• Mark Moran
• Metropolitan Washington Council of Governments

2005-04-15_hhg_scenar_modelres.ppt
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2030 Higher Household Growth in Region (HHG)
ScenarioWhat is it?

• Land use 2030 Higher Household Growth
• Network 2030 Transit-oriented development (TOD)

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2030 Higher Household Growth in Region Land
UseColor Map

• Based on Round 6.4 land activity forecasts
• Assumes 215,000 more households in the Washington
  region than in Round 6.4 forecast for 2030. Most
  are located in regional activity centers /
  clusters
• 100,000 come from beyond the external cordon
• 115,000 come from jurisdictions beyond the
  Washington region, but within the external cordon
• Reduces growth in commuter and other vehicle
  trips from areas outside the region
• Assumed additional 215,000 households represents
  60 of 2010-to-2030 growth, but only 9.0 of the
  total 2030 HHs)

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2030 Higher Household Growth in Region Land
UseBlack White Map

• Based on Round 6.4 land activity forecasts
• Assumes 215,000 more households in the Washington
  region than in Round 6.4 forecast for 2030. Most
  are located in regional activity centers /
  clusters
• 100,000 come from beyond the external cordon
• 115,000 come from jurisdictions beyond the
  Washington region, but within the external cordon
• Reduces growth in commuter and other vehicle
  trips from areas outside the region
• Assumed additional 215,000 households represents
  60 of 2010-to-2030 growth, but only 9.0 of the
  total 2030 HHs)

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Transit-oriented development network
2030 CLRP Based on 2003 CLRP and FY 2004-2009 TIP
2030 CLRP 2030 CLRP with enhanced transit service, especially elimination of the 2005 transit constraint through DC core
2030 TOD 2030 CLRP with new transit projects Metrorail, Commuter rail, Transitways (BRT/LRT)
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Transportation network Fixed guideway
improvements

• Heavy rail (Metrorail, commuter rail)
• Light rail (LRT)
• Bus rapid transit (BRT)
• Transitway (can be BRT or LRT)

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Transportation network 2005
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Transportation network 2030 CLRP
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Transportation network 2030 CLRP

• CLRP assumes no capital improvements, only
  enhancements to transit service
• Most significant service enhancement the
  lifting of the 2005 transit constraint through
  the DC core
• CLRP is the baseline network

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Transportation network TOD, Map 1 of 2
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Transportation network TOD, Map 2 of 2
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Map legend Fixed guideway extensions in TOD
network
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2030 CLRP vs. TOD networkFixed guideway miles
in the HHG scenario

• Fixed guideway miles have increased from 412 to
  691 miles (67), but most of the increase is in
  the transitway category, which includes BRT/LRT
  lines that may operate in mixed traffic, separate
  right-of-way, or a combination of the two.
• Metrorail miles increase by 23 and commuter rail
  miles by 12.

Source rail_link_miles2.xls, staprotp.rpt
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HHG scenario Model results

• Compared to the 2030 CLRP, the 2030 HHG scenario
  results in 219,000 more transit trips per day
  (16 increase).
• 165,000 (75) due to land use effect
• 54,000 (25) due to network (TOD) effect
• (TOD scenario was 8 increase or 109,000 more
  transit trips)
• Regional transit mode share goes from 5.7 to
  6.2
• This is the 2nd largest regional mode share for
  transit of the scenarios tested (the largest was
  6.3 for the 2030 TOD scenario)
• Home-based work (HBW) transit mode share goes
  from 20.5 to 22.2
• This is the highest transit mode share for work
  trips out of all the scenarios tested (2030 TOD
  was close with 22.1 transit)

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HHG scenario Model results, 2

• VMT drops by about 1.3 (from 149.8 million to
  147.8 million vehicle miles of travel). This is
  impressive, given the fact that motorized person
  trips went up by 6.8 (Generally, when motorized
  person trips go up, VMT goes up).
• HBW walk and bike trips increased 17.8, from
  253,000 to 298,000
• Carpool commuters There is increase of 37,000
  daily trips (5.8)
• AM congestion the number of AM lane miles with a
  volume-to-capacity ratio gt 1.0 drops by 6.4
  (from 2,560 to 2,400 miles)

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HHG scenario Model results, 2
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Accessibility Analysis

• 2030 Higher Household Growth in Region Scenario

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Accessibility analysis overview

• Accessibility to
• Jobs or households
• Via
• AM transit, walk-access to transit
• AM transit, best of walk-access or drive-access
  to transit
• AM highway network
• Threshold
• 45 minutes travel time
• Base scenario
• 2030 CLRP network with Round 6.4 land use
• Alternative scenario
• 2030 Transit-Oriented Development network with
  Higher Household Growth in Region land use

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Change in Accessibility to Jobsvia AM Transit,
Walk AccessLegend scale Large

• Compared to the CLRP, the HHG scenario results
  in moderate gains in accessibility to jobs.
• In the HHG scenario, only HHs are moved (not
  jobs). Thus, this map shows increases in
  accessibility due only to the TOD network
  improvements. Gains are clustered around the
  transit improvements.

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Change in Accessibility to Jobsvia AM Transit,
Walk AccessLegend scale Small

• Same as previous map, but legend scale is small
  to show more detail concerning accessibility
  changes.
• As was the case on the previous map, this map
  shows increases in accessibility due only to the
  TOD network improvements. There is no land use
  effect, since the measure is acc. to jobs.

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Change in Accessibility to Householdsvia AM
Transit, Walk AccessLegend scale Large

• Compared to the CLRP, the HHG scenario results
  in significant gains in accessibility to HHs.
• This map shows both the land use effect (moving
  HHs in) and the network effect (TOD network).
  The increases in accessibility are concentrated
  in the areas where either HHs were added, TOD
  network improvements were made, or both.

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Change in Accessibility to Householdsvia AM
Transit, Walk AccessLegend scale Small

• Same as previous map, but legend scale is small
  to show more detail concerning accessibility
  changes.
• The increases in accessibility are concentrated
  in the areas where either HHs were added, TOD
  network improvements were made, or both.

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Change in Accessibility to Jobsvia AM Transit,
Best of Walk or Drive AccessLegend scale Large

• Compared to the CLRP, the HHG scenario results
  in moderate gains in accessibility to jobs.
• In the HHG scenario, only HHs are moved (not
  jobs). Thus, this map shows increases in
  accessibility due only to the TOD network
  improvements. Gains are clustered around the
  transit improvements.
• One area shows a moderate loss in accessibility.
  This is an area where the drive-access transit
  path is dominant and it is likely that the added
  development around transit has slowed auto travel
  slightly (including drive-access to transit).

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Change in Accessibility to Householdsvia AM
Transit, Best of Walk or Drive AccessLegend
scale Large

• Compared to the CLRP, the HHG scenario results
  in significant gains in accessibility to jobs.
• This map shows both the land use effect (moving
  HHs in) and the network effect (TOD network).
  The increases in accessibility are concentrated
  in the areas where either HHs were added, TOD
  network improvements were made, or both.

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Change in Accessibility to Jobsvia AM Highway
SpeedLegend scale Large

• Since there was no job movement as part of this
  scenario, this map shows the effects of the land
  use change only.
• Compared to the CLRP, the HHG scenario results
  in both moderate gains and moderate losses in
  accessibility to jobs, via the AM highway
  network.
• Both the gains and the losses are due to changes
  in congestion, which affects travel times.
• There were moderate gains in accessibility in
  Montgomery and Prince Georges counties, due to
  fewer long-distance commuting trips from Howard
  Anne Arundel counties.
• There were moderate losses in accessibility in
  Prince Georges County (inside the Beltway) and
  S.E. DC, due to the added households in these
  areas, which resulted in increased congestion and
  lower travel speeds.
• There were moderate losses in accessibility via
  the AM highway network in other scattered areas
  for the same reason cited above.

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Change in Accessibility to Householdsvia AM
Highway SpeedLegend scale Large

• This map shows the effects of both the land use
  change and the network change, since the HHG
  scenario has changes in both these entities.
• Compared to the CLRP, the HHG scenario results
  in moderate gains in accessibility to HHs, via
  the AM highway network, throughout the region.
• The gains are predominantly found in the
  jurisdictions that were the recipients of the
  added HHs
• No areas showed losses in accessibility to HHs

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Findings/Summary

• HHG scenario
• TOD network
• Added 215,000 HHs (9 of total 2030 HHs)
• Reduced external vehicle trips by corresponding
  amount
• Results in 16 increase in transit trips
  (219,000 trips)
• 2030 TOD scenario 8 increase (109,000 trips)
• Regional transit mode share increased to 6.2
  (only the 2030 TOD scenario produced a higher
  number, 6.3)
• Home-based work (HBW) transit mode share
  increased to 22.2 (the highest of all scenarios
  tested to date)
• Despite a 6.8 increase in motorized person
  trips, VMT dropped by 1.3

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Findings/Summary

• Changes in accessibility were logical and
  consistent
• Accessibility to jobs via AM transit
• Increases were concentrated around transit
  improvements
• Accessibility to HHs via AM transit
• Increases were concentrated around areas with
  transit improvements, added HHs, or both
• Accessibility to jobs via AM highway
• Increases in areas where road congestion improved
  (Montgomery PG counties, due to fewer
  long-distance commuting trips from Howard Anne
  Arundel counties)
• Decreases in areas where road congestion worsened
  (S.E. DC and PG Co. inside the Beltway, which had
  large increases in HHs)
• Accessibility to HHs via AM highway
• Moderate gains throughout much of the COG
  planning area.

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Acknowledgments

• Program Manager Bob Griffiths
• Travel model development and overview Ron Milone
  and Jim Hogan
• Network development Bob Snead, John Bethea,
  Wanda Hamlin, Joe Davis, Bill Bacon
• Mapping and technical support Meseret Seifu, Don
  McAuslan
• Travel modeling Mark Moran

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Thank you

• Questions?

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TPB modeled area
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Four-step model Overview

• Trip generation
• Predict the no. of trip ends generated in each
  zone
• Trip distribution
• Predict where trips are going, i.e., connecting
  trip ends into trips
• Mode choice
• Predict the share of trips made by each travel
  mode
• Trip assignment
• Assign trips to the network. Equilibration of
  supply and demand

Graphic from Urban Transportation Planning,
Meyer Miller, 1984.