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RPS Modeling Wrap Up

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Reviewed by two peer review panels of national and international experts ... Region-wide VMT, travel time, and freeway travel vary very little: 2-3 ... – PowerPoint PPT presentation

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Title: RPS Modeling Wrap Up


1
RPS ModelingWrap Up
1
  • Presentation to OMUG
  • Brian Gregor
  • Transportation Planning Analysis Unit
  • March 21, 2008

2
Modeling Process Review
3
3
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

4
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

5
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

6
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
7
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?

8
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.

9
1st Stage Modeling
  • Completed land use modeling.
  • Completed transportation modeling on 30 land use
    scenarios using the RTP transportation network.

10
Household Growth
11
Employment Growth
12
Effects of Simulation Starting Year on Growth
Trends
13
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.
14
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

15
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.
16
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.
17
2nd Stage Modeling
5
  • 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.

18
11
19
Congested Travel
28
  • 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.

20
29
21
36
22
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

23
No Policy Change Land Use
24
Nodal Development Land Use
25
Regional Attractor Land Use
26
Road Network Scenarios
Enhanced
High Capacity
RTP
27
Transit Scenario Networks
Low Transit
High Transit
28
Congestion Comparison Base Road Network vs. High
Capacity Road Network
29
Road Network Congestion
No Policy Change Land Use RTP Roads and Transit
Nodal Land Use High Capacity Roads and Transit
30
Group 1 Eagle Point Area
31
Group 1 Eagle Point Area
32
Group 3 Northeast Medford Area
33
Group 3 Northeast Medford Area
34
Average Peak Hour Trip Length
No Policy Change and Regional Attractor Scenarios
have the same trip lengths
35
Average Peak Hour Trip Length
Nodal Development Scenario trip lengths are 5-7
shorter
36
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
37
Annual Peak Hour Congestion Delay Per Capita
Regional Attractor Land Use Scenario produces the
highest amounts of travel delay
38
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
39
Annual Peak Hour Congestion Delay Per Capita
High Transit Scenarios produce 78 lower travel
delay than the corresponding Low Transit
Scenarios
40
Annual Peak Hour Congestion Delay Per Capita
Improving the road network from the RTP Network
to the Enhanced Network reduces delay by about
20
41
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
42
Annual Peak Hour Congestion Delay Per Capita
Best performing scenario has about 40 less delay
than the worst performing scenarios.
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