RPS Modeling Wrap Up

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RPS ModelingWrap Up
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• Presentation to OMUG
• Brian Gregor
• Transportation Planning Analysis Unit
• March 21, 2008

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Modeling Process Review
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Regional Problem Solving (RPS)

• Regional planning process to identify urban
  reserves
• 8 jurisdictions in Rogue Valley metropolitan area
  located in southwestern Oregon
• Assumed doubling of regional population
• LUSDR developed to help evaluate alternative land
  use policies and impacts to transportation

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Modeling Challenges

• New transportation model needed to cover entire
  study area
• Transportation model requires detailed land use
  inputs
• Uncertainty about land use
• Long-range forecast
• General growth proposals
• Transportation and land use models were not
  available when the study was started

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Meeting the Challenges

• New transportation model Rogue Valley MPO model
• Covers entire area
• More advanced than most MPO models in the nation
• New land use model (LUSDR)
• Required in order to produce land use inputs for
  transportation model
• New model which recognizes forecast uncertainty
• Reviewed by two peer review panels of national
  and international experts

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We cant be certain about how land will develop
Or I might be the realization
I might become the realization of a shopping
center
But shopping center employment wont be spread
out over all zones
meeting shopping center requirements
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Uncertainty Is Informative
A
B

• B is more likely than A to need widening.
• What are the characteristics of scenarios
  requiring widening, and not requiring widening?

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Modeling Stages

• Stage 1 - Get the LUSDR model running and produce
  30 plausible land use futures. Evaluate with
  transportation model using RTP network.
• Stage 2 - Model transportation with an expanded
  road network. Compare with earlier results.
• Stage 3 - Evaluate effects of 15 combined
  transportation and land use policy scenarios.

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1st Stage Modeling

• Completed land use modeling.
• Completed transportation modeling on 30 land use
  scenarios using the RTP transportation network.

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Household Growth
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Employment Growth
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Effects of Simulation Starting Year on Growth
Trends
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Dot Plot of Households by District Ordered by
Absolute Variation
Tolo has higher variation for its size because
of alternative growth plans.
Ashland and West Medford have less variation for
their sizes. Result of land constraints.
Variation shows up in the traffic results.
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General Transportation Results

• Region-wide VMT, travel time, and freeway travel
  vary very little 2-3
• Region-wide total delay, employment accessibility
  and transit accessibility vary significantly
• Delay 35
• Jobs accessible within 10 minute drive 9
• Jobs served by public transit 7

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Traffic Monitor Sites
NOTE Although these locations are shown as
points, they represent all sections of roads that
have the same number of lanes and similar amounts
of traffic.
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Dot Plot of Congestion at Traffic Monitor Sites
NOTE Although these locations are shown as
points, other portions of roadways with the
similar traffic and the same number of lanes can
be expected to have similar congestion. These
results do not show congestion at interchanges or
intersections.
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2nd Stage Modeling
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• An enhanced road network was developed by the
  technical advisory committee.
• Transportation modeling was done on the enhanced
  road network for the same land use growth
  scenarios modeled previously.
• Results were compared with previous
  transportation model runs.

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Congested Travel
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• Large increases in the amount of travel on
  freeways and arterials that will experience high
  levels of congestion.
• The Enhanced Network reduces this growth
  primarily on principal arterials.
• Freeway ramp congestion is sensitive to land use
  patterns.

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29
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36
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3rd Stage Policy Scenario Modeling

• Land Use
• No Policy Change
• Nodal Development
• Regional Attractor
• Transportation
• RTP Road Transit Networks
• Enhanced Roads RTP Transit
• High Capacity Roads RTP Transit
• Enhanced Roads High Cap. Transit
• High Cap. Roads High Cap. Transit

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No Policy Change Land Use
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Nodal Development Land Use
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Regional Attractor Land Use
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Road Network Scenarios
Enhanced
High Capacity
RTP
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Transit Scenario Networks
Low Transit
High Transit
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Congestion Comparison Base Road Network vs. High
Capacity Road Network
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Road Network Congestion
No Policy Change Land Use RTP Roads and Transit
Nodal Land Use High Capacity Roads and Transit
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Group 1 Eagle Point Area
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Group 1 Eagle Point Area
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Group 3 Northeast Medford Area
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Group 3 Northeast Medford Area
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Average Peak Hour Trip Length
No Policy Change and Regional Attractor Scenarios
have the same trip lengths
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Average Peak Hour Trip Length
Nodal Development Scenario trip lengths are 5-7
shorter
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Average Peak Hour Trip Length
High Capacity Road Network increases trip lengths
for No Policy Change and Regional Attractor
Scenarios -- -- but not for Nodal Development
Scenario
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Annual Peak Hour Congestion Delay Per Capita
Regional Attractor Land Use Scenario produces the
highest amounts of travel delay
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Annual Peak Hour Congestion Delay Per Capita
Nodal Development Scenario produces the lowest
amounts of delay 8-11 lower than the No Policy
Change Scenario
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Annual Peak Hour Congestion Delay Per Capita
High Transit Scenarios produce 78 lower travel
delay than the corresponding Low Transit
Scenarios
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Annual Peak Hour Congestion Delay Per Capita
Improving the road network from the RTP Network
to the Enhanced Network reduces delay by about
20
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Annual Peak Hour Congestion Delay Per Capita
Improving the road network from the Enhanced
Network to the High Capacity Network reduces
delay by an additional 8-10
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Annual Peak Hour Congestion Delay Per Capita
Best performing scenario has about 40 less delay
than the worst performing scenarios.