Development and sensitivity testing of alternative mobility metrics in a regulatory context

Development and sensitivity testing of alternative mobility metrics in a regulatory context

7 May 2013

Prepared for:TRB Planning Applications Conference

John Gliebe, RSG, Inc.James Strathman, Portland State UniversitySteven Tuttle, RSG, Inc.Myra Sperley, Oregon DOT Research Section

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Acknowledgments

• This work was funded by an Oregon DOT Research (SPR 716)

• The authors are grateful for the contributions of the following individuals:• Amanda Pietz,ODOT Research• Sam Ayash, ODOT TPAU• Terry Cole, ODOT Region 2• Kathryn McGovern, PSU• David Ruelas, PSU• David Boyd, TAC• Jazmin Casas , TAC• Brian Gregor, TAC• Douglas Norval, TAC• Lidwien Rahman, TAC• Michael Rock, TAC• Mark Vandehey, TAC

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Background

• Oregon Highway Plan’s (OHP) mobility policies guide planning and programming by Oregon Department of Transportation (ODOT).

• ODOT has land use change review responsibilities under the Transportation Planning Rule, as adopted by the state’s Land Conservation and Development Commission.

• A single volume-to-capacity (v/c) metric currently supports OHP mobility policies and may be the basis for requiring mitigation. Sometimes this stops the project.

• Critics of the single facility-based v/c measure charge that it is focused too narrowly on operational objectives.

• In many cases, adherence to this standard has undermined community economic development, compact growth, and non-auto mode share objectives.

• Numerous alternative performance measures have been suggested that would better capture these concerns; however, many of them are difficult to predict as an outcome of a particular land use change proposal.

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Objectives

• Demonstrate the potential use of alternative mobility metrics for evaluation of large-scale land use change proposals– Related to goals found in the Oregon Highway Plan

promoting non-SOV travel and efficient land use patterns• Explore how these metrics co-vary with each

other and V/C– Variation across inputs– Variation across spatial dimensions

• Provide information for consideration of metrics by policy boards or as part of transportation system planning (TSP) process

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Case Study Methodology

• Chose a representative land use scenario for model based analysis– Previously analyzed by ODOT without pending decisions

• Northgate Lifestyle Center proposal – Medford– Centrally located– Served by transit– Near highway interchanges– Semi-mixed use

• Analyzed “build” and “no build” scenarios– 2010 Opening Year – 2025 Future Year

• Sensitivity tests on alternative futures– Fringe growth– Scaled up development– Conserved growth

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Criteria for Selection of Metrics

• Provides evidence of a change in travel activity that related to an OHP policy (e.g., promoting non-motorized travel modes)

• May be theoretically or empirically linked to land use, socio-economic, or transportation system inputs

• Robust over a range of inputs values• Can be forecast using established methods and data• Set of metrics should be complementary, avoid

redundancy, offer a range of perspectives• Set of metrics should represent all travel modes and

markets• Set of metrics should include both facility-specific and

area-wide measurements• Should not include direct measurement of non-travel

activity– “Second-order effects” that results from travel-activity– E.g., economic impacts, safety impacts, environmental impacts

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Metrics Selected

• Network wide V/C• Total vehicle hours of travel time• Person hours of travel time• Average person trip travel time• Trip length distributions• Mode shares• Regional accessibility to employment/shopping

– By Auto, Transit and Walk

• Local accessibility to employment/shopping (20-min. neighborhood)– By Auto, Transit and Walk

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Other Metrics Considered

The study team’s review of literature revealed a long list metrics to consider. Some of the more noteworthy metrics that we rejected for this study, included…

– Land use variables related to urban form, street connectivity, lane miles of bike and pedestrian facilities

Why? Existence value not easily quantified in terms of travel behavior. Focus should be on the traveler response.

– Reliability indices – planning time index, buffer time index, 95th percentile travel time

Why? Difficult to forecast and attribute to a facility (area-wide measures). Ambiguous implications—very high congestion—reliably congested.

– Congestion duration, queuing, recurring delayWhy? Impossible to forecast with static network

assignment models. Need DTA.

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Study Area

• 219,300 square foot office park– professional services

and light industrial uses

• 417,500 square feet retail shopping space

• 167,000 square foot business park

• Intra-development TrolleyTrolley following Central Ave

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Study Area Cities and TAZ System

Northgate Site

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Network Model

Rogue Valley MPO (RVMPO)

Model Version 2, using JEMnR platform

– Supplied by ODOT- TPAU

– Converted from EMME/2 to EMME/3

– 759 TAZs, 8671 links, 3016 nodes

– 3 TAZs comprise the Northgate developmentNorthgate

Site

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Study Districts Used for Analysis of Spatial Focus

Concentric Study Districts

– Site TAZs– Approx. 1 mile out– Approx. 4 miles out– Entire region

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Classifications of Trips by District

1. If either the origin or the destination of a trip belonged to one of the TAZs on the map shown as District 1, then the trip was considered to belong to District 1.

2. If either the origin or the destination of a trip belonged to one of the TAZs on the map shown as District 2, inclusive of District 1, then the trip was considered to belong to District 2.

3. If either the origin or the destination of a trip belonged to one of the TAZs on the map shown as District 3, inclusive of Districts 1 and 2, then the trip was considered to belong to District 3.

4. All trips were considered to be part of District 4. For example, a trip with a trip end in District 1 will also be included in the tabulations for Districts 2, 3 and 4.

Used to establish spatial focus

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Network Wide V/C Change Analysis

Example: 2025 Baseline vs. Northgate Scenario

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Travel Time Metrics

Total Network Travel Time

Baseline Northgate % Change Baseline Northgate % Change

Auto/Truck Vehicle Miles (VMT) 1,742,599 1,750,526 0% 2,109,860 2,118,955 0%

Auto/Truck Vehicle Hours (VHT) 67,232 67,552 0% 80,681 81,061 0%

Transit Trip Miles 3,629 3,520 -3% 4,049 3,945 -3%

Transit Trip Hours 3,152 2,992 -5% 3,600 3,450 -4%

2010 2025

2025Mode 1 2 3 4 1 2 3 4 1 2 3 4

Walk 50 1,272 7,329 11,134 404 1,491 7,167 10,854 708% 17% -2% -3%

Bike 5 120 742 1,067 43 147 744 1,064 703% 23% 0% 0%

Walk to Bus 11 269 1,615 2,433 90 316 1,578 2,377 726% 18% -2% -2%

PnR Bus 0 17 145 184 0 16 139 177 0% -6% -4% -4%

Drive Alone 251 3,823 23,851 32,397 1,915 5,159 24,278 32,666 662% 35% 2% 1%

Drive w Pasg. 204 3,581 19,826 26,762 2,096 5,052 20,212 26,945 929% 41% 2% 1%

Passenger 225 3,999 21,136 28,682 2,393 5,641 21,470 28,751 962% 41% 2% 0%

All 747 13,081 74,645 102,660 6,941 17,823 75,588 102,834 830% 36% 1% 0%

Baseline by Study District Northgate by Study District Percent Change

Person Hours of Travel Time

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Trip Length Distributions

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0 to 2 2 to 4 4 to 6 6 to 8 8 to 10 10 to 12 12 to 14 14 to 16 16 to 18 18 to 20

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Miles

2025 Base

2025 NG

District 4 Trip Length Distribution: 2025 Base vs 2025 Northgate

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2025 Base

2025 NG


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2025 Base

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Example: 2025 Baseline vs. Build by Study District

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Modes

Mode Shares

Trips by Mode2025

Mode 1 2 3 4 1 2 3 4 1 2 3 4

Walk 107 3,526 32,773 58,103 911 3,902 31,147 55,538 749% 11% -5% -4%

Bike 29 676 4,599 7,455 282 861 4,598 7,403 881% 27% 0% -1%

Walk to Bus 40 625 4,320 7,053 333 843 4,266 6,919 742% 35% -1% -2%

PnR Bus 0 47 416 491 0 44 396 470 0% -7% -5% -4%

Drive Alone 2,622 41,628 243,231 351,213 21,708 56,383 246,798 352,448 728% 35% 1% 0%

Drive w Pasg. 2,044 36,158 191,229 270,611 22,377 51,725 194,983 271,714 995% 43% 2% 0%

Passenger 2,384 42,185 213,363 301,944 27,020 60,773 217,293 302,377 1033% 44% 2% 0%

All 7,225 124,845 689,931 996,869 72,633 174,530 699,481 996,869 905% 40% 1% 0%

Northgate by Study District Percent ChangeBaseline by Study District

2025Mode 1 2 3 4 1 2 3 4 1 2 3 4

Walk 1% 3% 5% 6% 1% 2% 4% 6% -16% -21% -6% -4%

Bike 0% 1% 1% 1% 0% 0% 1% 1% -2% -9% -1% -1%

Walk to Bus 1% 1% 1% 1% 0% 0% 1% 1% -16% -4% -3% -2%

PnR Bus 0% 0% 0% 0% 0% 0% 0% 0% 0% -33% -6% -4%

Drive Alone 36% 33% 35% 35% 30% 32% 35% 35% -18% -3% 0% 0%

Drive w Pasg. 28% 29% 28% 27% 31% 30% 28% 27% 9% 2% 1% 0%

Passenger 33% 34% 31% 30% 37% 35% 31% 30% 13% 3% 0% 0%

All 100% 100% 100% 100% 100% 100% 100% 100% 0% 0% 0% 0%

Northgate by Study District Percent ChangeBaseline by Study District

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Regional Accessibility Baseline Build Scenario

Auto / Highway

WalkTransit

Total Households

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Local Accessibility

For example: if your spatial focus is limited to District 3, then the Northgate scenario would result in a 7% increase in access to retail shopping opportunities (employment) in 2010, using the 20-minute neighborhood concept.

Assumptions: walk speed 3 mph, bike speed 9 mph

Measuring the 20-minute neighborhood

Mode 1 2 3 4 1 2 3 4

Auto 3% 3% 3% 3% 3% 3% 3% 3%

Transit 19% 19% 1% 1% 17% 16% 1% 1%

Walk 122% 24% 2% 2% 109% 22% 2% 1%

Auto 12% 12% 12% 11% 10% 10% 10% 9%

Transit 40% 64% 9% 7% 37% 58% 7% 6%

Walk 210% 44% 7% 6% 183% 40% 6% 5%

2025 Study District2010 Study District

Ret

ail

Wor

k

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Sensitivity Tests

• Relocating the Development to a Fringe Area

• Scaling Up the Development – (2X and 5x)

• Conserved Growth– no net gain in

total employment– Subtracted

Northgate employment from elsewhere

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Lessons Learned

• Fringe Growth– Lower impact on surrounding transportation facilities– Fewer total trips attracted, but nearly all auto– Net V/C, PHT, Average Person minutes, Regional accessibility,

number of trips by mode and study district capture differences• Scaled Up Development

– More dramatic positive and negative changes – Many more local trips, and many more regional trips—offsetting

impacts– Net V/C, PHT, Average Person minutes, Regional accessibility,

number of trips by mode and study district capture differences• Conserved Growth

– Shows how a new regional center will draw trips away from other neighborhood locations

– Net impacts may be negative or positive (negative mostly in this case)

– Net V/C and regional accessibility capture differences best

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Regional Accessibility Conserved Growth Scenario

Auto / Highway

WalkTransit

Total Households

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Lessons Learned

• The geographic distance at which one measures land use change impacts is important—affects attenuate further from the source of change. Not surprising, but important for regulatory usage.

• At the regional level, all modeled scenarios led to slight increases in auto travel and slight net reductions in non-auto travel.

• The concentration of a large amount of commercial development in a single location has non-linear increasing effects on trip attractions.

• Because the model system is production constrained and because the build scenarios assumed only an increase in employment, without increases in households and workers, scenarios involving an increase, decrease or change in location of employment due to the Northgate development all produced the same number of total trips for the region.

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Assessment of Metrics

• Network-wide V/C Changes– Best for showing direct impacts and can show offsetting

effects if evaluated network wide– Does not explain why changes occur where they do

• Total Network Travel Time and Distance– Theoretically nice for portraying total network impacts– Not sensitive enough to local changes---too aggregate– Potentially misleading—hides problems– Lacking in insights

• Total Person Hours of Travel Time– Captures both increased trip lengths and mode shifts

together– Potentially misleading (e.g., walk time increase may be

beneficial)– Misses out on external markets and trucks

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Assessment of Metrics

• Average Person Trip Lengths & Trip Length Distributions– Nice to show changes in average trip lengths– Does not provide enough insight on underlying behavior– Potentially misleading—regression to the mean

• Mode Shares– Percentage shares can be misleading due to small magnitudes of

some modes– Number of trips by mode and total trips are useful as diagnostics,

but difficult to use in a standardized way• Regional Accessibility

– Good for showing benefits of travel differentiated by mode– Needs to be put into context of households (or whoever benefits)

• Local Accessibility (20-minute neighborhood)– Very little regional variation for small areas (need to resize

buffer)– Arbitrary buffer, misleading treatment of trips within buffer

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Recommendations for Further Consideration

• Network-wide V/C Budget– Familiar measurement concepts– May be extended to include V/C “budget”– Improved V/C on some facilities would offset worsened

V/C on others in mitigation negotiations– Requires precise measurements of V/C using network

models that can portray pluses and minuses• Regional Accessibility

– Closely related to economic benefits calculations– May be derived precisely from econometric formulations– Should be weighted by households or other beneficiaries– Could be simplified and standardized– TBD: form of impedance functions, spatial units

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Methodological Recommendations

• Limitations of trip-based modeling and static network assignment are “exposed” in this type of analysis.

• Activity-based models would respond more appropriately because discretionary, secondary stop making would vary based on accessibility (not production constrained). Tour-based travel paradigms might respond differently, as well.

• Dynamic Traffic Assignment (DTA) would enable us to consider additional mobility metrics related to reliability, e.g., recurring delay, duration of congestion, and queuing.

Questions and Answers

For more information:

John Gliebe, RSG 802-295-4999

Documents

Development and sensitivity testing of alternative mobility metrics in a regulatory context