santa monica street typologies tumlin cnu
DESCRIPTION
TRANSCRIPT
1
Street Typologies for a Complete Community
A New Approach in Santa Monica CA
CNU 17 Denver COJune 12, 2009Jeffrey Tumlin
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 2
Approach
Transportation is not an end in itself.
It is merely a means by which we support the City’s larger goals.
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 3
Conventional Measures
Auto Level of Service (LOS) Seconds of delay
experienced by vehicles, typically at intersections.
Easy to measure. Says nothing about
average travel speed over a corridor.
Says nothing about person capacity.
Ignores other modes of transportation
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 4 4
Congestion in Santa Monica: Where we are now
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 5
Tools for Measuring
If we can’t quantify it, it won’t be considered important
Need different tools for Judging success of overall transportation
system Balancing the needs of each mode in street
improvement projects Prioritizing funding
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 6
Approach
Start with new street typologies including: Context
– Land use– intensity – transect– ground floor frontage
Mobility– Priority for each mode: cars, transit, bikes
Set Quality of Service indicators for each mode Set minimum, maximum and preferred measures
for each mode and vary according to context
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 7
Street Typologies – Start with Land Use Context
Neighborhood Retail and Downtown streets An important function is to support local retailers Need very high quality pedestrian realm – pedestrians
are more important here than any other mode For cars, LOS A is worse than LOS F. LOS C or D
are optimal, because slower auto traffic supports pedestrian quality and allows motorists to see storefronts
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 8
Street Typologies – Start with Land Use Context
Residential Neighborhood Streets Most important function
is to support a high quality living environment
Need continuous sidewalks and landscape buffer
Slow traffic speeds
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 9
Street Typologies – Start with Land Use Context
Industrial Area Streets Most important function is to accommodate
industrial businesses Must accommodate trucks and truck loading Pedestrian quality less important but cannot
be ignored Higher traffic speeds and volumes OK If you must have congestion, put it here!
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 10
Street Typologies – Start with Land Use Context
Special Streets Ceremonial, recreational
streets have special characteristics and needs– San Vicente– 3rd Street Promenade– Olympic– Oceanfront Walk– Beach Bike Path
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 11
Street Typologies: Priority for Each Mode Convert
arterial, collector, feeder to 1st, 2nd and 3rd priority for cars
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 12
Insert auto map
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 13
Street Typologies: Priority for Each Mode Transit: Use
transit frequency map to note 1st, 2nd and 3rd priority for transit
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 14
Street Typologies: Priority for Each Mode Insert transit map
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 15
Street Typologies: Priority for Each Mode For bicycles, define where
separate facility is needed due to high motor vehicle speeds and volumes
Then, to complete the network, define priority locations where motor vehicle speeds and volumes will be made compatible with bikes
Flickr photo by SeenyRita
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 16
Street Typologies: Priority for Each Mode
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 17
Street Typologies: Priority for Each Mode All streets must work well for pedestrians, but we
need to note which streets are most important, require special considerations and should be prioritized for funding.
For pedestrians, use land use context map to note retail, residential and industrial areas, along with special facilities like schools, hospitals, care facilities, and recreational destinations
Use judgment to identify priority pedestrian connections
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 18
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 19
Streets
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 20
Quality of Service: Transit
Quality of Service (QOS) rather than Level of Service (LOS)Transit frequency: Headway (minutes)Span of service (hours)Reliability: Coefficient of variation or
Probability of delay >0.5 HLoading: Percent capacityTravel speed: Percent of posted speed
limit
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 21
Quality of Service: Cars
Travel time between specific points Standard deviation of average speed – making
sure auto travel is slow and steady, rather than start-and-stop
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 22
Quality of Service: Bicycle Compatibility Index
Geometric and roadside data: Number of through lanes Curb lane width Bicycle lane or paved shoulder presence and
width Area character (residential or non-residential)
Traffic operations data: Posted speed limit 85th percentile speed of motor vehicles Average Annual Daily Traffic volume Percentage of traffic constituted by trucks Percentage of vehicles turning right into
driveways or minor intersections Parking data:
Presence of on-street parking On-street parking occupancy Parking time limit Flickr photo by jennchantal
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 23
Quality of Service: Pedestrians
Perceived safety: Walk survey, including gender, age, ability
Quality environment: Proper sidewalk width for
land use context Active ground floor use,
including frequency of doors and percent storefront
Height and frequency of trees, and dimension of landscape setback
Ratio of pedestrians to vehicles
Frequency of protected crossings
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 24
Setting Standards and Significance Criteria Next, for each context zone and mode network,
think through the absolute minimum, acceptable and preferred Quality of Service for each mode…
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 25
Setting Standards and Significance Criteria
FUNCTION CONTEXT ZONE Minimum Desirable Preferred
Transit T-QOS T-QOS T-QOS
Primary All ≥+1 ≥+1.5 ≥+2Secondary Downtown
Retail StreetsIndustrial Single family residential areas
≥-1≥-1
≥+0.5≥+0.5
≥-0.5≥-0.5≥+1≥+1
≥+1≥+1≥+1≥+1
Other transit All - ≥-1 ≥-0.5Auto Vehicular
V:CVehicular
V:CVehicular
V:CArterial Downtown
Retail StreetsIndustrial Single family residential areas
<1.2<1.2<1.0<1.0
<0.8<1.0<0.8<0.6
>0.6>0.6>0.6<0.4
Collector/ Feeder
DowntownRetail StreetsIndustrial Single family residential areas
<1.2<1.2<1.2<1.2
<0.8<1.0<0.8<0.6
>0.6>0.6>0.6<0.4
Local All - <0.9 <0.8
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 26
Setting Standards and Significance Criteria
FUNCTION CONTEXT ZONE Minimum Desirable
Preferred
Bicycle Bicycle QOS
Bicycle QOS
Bicycle QOS
Primary Bicycle
DowntownRetail StreetsIndustrial Single family residential areas
DDCB
BCBA
AAAA
Secondary Bicycle
DowntownRetail StreetsIndustrial Single family residential areas
DDDD
BDBB
AAAA
Pedestrian
Ped QOS Ped QOS Ped QOS
Primary Pedestrian
DowntownRetail StreetsIndustrial Single family residential areas
BBCD
AAAB
AAAA
Secondary Pedestrian
DowntownRetail StreetsIndustrial Single family residential areas
CCCD
BBBB
AAAA
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 27
Application
Montana Ave: Neighborhood retail context, 1st priority for pedestrians, 2nd priority for cars and transit
Result: OK to slightly degrade auto QOS to improve transit and pedestrian QOS. Signal prioritization OK, but not dedicated transit lane.
Goal: Bring all measures into balance
FUNCTION
CONTEXT ZONE Minimum
Desirable
Preferred
Measured
Transit -0.5Secondary
Neighborhood retail
≥-1 ≥-0.5 ≥+1
Auto 0.8Secondary
Neighborhood retail
<1.2 <0.8 >0.6
Pedestrian
A
Primary Neighborhood retail
B A A
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 28
Finally, Set Citywide Performance Indicators Build from adopted policy language:
LUCE Principles– Residential Neighborhoods– Placemaking– Walkable Community– Mixed Use Neighborhoods– Parking & Transportation– The Boulevards
Circulation Element Goals Sustainable City Goals
– Resource Conservation– Environmental and Public Health– Transportation– Economic Development– Open Space and Land Use– Housing– Community Education and Participation– Human Dignity
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 29
Process
Identify local values Identify long list of performance measures Refine into short list:
Assess today’s conditions Predict future conditions Evaluate projects Conduct EIRs
Create tools and gather data Establish targets and thresholds Report back to public and Council Adopt impact fee
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 30
Start with Transportation Principles
Measure Success Management Streets Quality Public Space Environment
Health Affordability Economy Equity Safety Public Benefits
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 31
Creating a Shortlist
For each principle, a long list of potential measures – and tools for measuring
Next step: Short list: Shortest list of measures that captures Santa
Monica values Minimize data collection costs Maximize clarity
Some measures, like per capita Vehicle Miles Traveled, capture many values: Greenhouse gases, congestion, air quality, etc.
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 32
The Long List
Measure Cost/Time Consumption
Implementation EIR Project Review
Corridor
Review
Report
Card
Travel Model
MANAGEMENT
•Relative travel times by mode
Medium Can be modeled; see WeHo traffic model. Can also be collected through data collection. Transit travel times can be automated in GPS.
•Person capacity – walking, bike, transit, auto, parking, bike parking
Medium - Heavy
This is a GIS/Excel type function that can be included if there is survey data available. Can be modeled. This needs to be further defined.
? ?
•Transit LOS: productivity, farebox return, delay, reliability
Medium - Heavy
This will take extensive model development if we want to get to this level in the demand model. Direct ridership modeling would be another option and would require less data/development time. Transit LOS could also be developed and monitored separate from the model in an Excel spreadsheet. BBB already does a basic collection of this info, and full transit LOS data may be available in upcoming GPS reporting from BBB. Seattle uses transit LOS in an annual GIS report card map, focusing on transit speed and frequency. SF uses transit LOS in their EIRs
•Neighborhood spill-over Medium Either traffic volumes or driver behavior (speed, etc)
Congestion Light The sustainability report card currently measures intersection LOS. Congestion is also indirectly measured in the relative travel times by mode and the person capacity analysis above. (There is community resistance to using intersection LOS.) Adjust significance thresholds if used for EIRs.
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 33
Tools and Data
GIS mapping Transportation Demand Management reporting
data Big Blue Bus GPS data Public perception surveys Traffic counts
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 34
Existing Traffic Analysis Tool
Straight-line projection of past trends Does not consider traveler choice of mode or
route in response to congestion Cannot measure travel times Trip assignment is done by hand Trip generation and assignment does not
consider trip interaction between different land uses
Does not consider non-auto modes
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 35
New Traffic Analysis Tool
Fehr & Peers 4D model Land use based – at parcel level – allowing consideration
of interaction among land uses Consider shifts in driving patterns due to congestion Consider effects of TDM strategies and alternate modes Measure factors such as corridor travel time Ability to provide various performance indicators (e.g.,
total trips generated, travel time, VMT, VHT, etc.) and comparison plots of results
Address new statewide requirements (e.g., CO2/GHG) Provide estimates of extent of regional traffic passing
through the city Nexus analysis in support of trip fee
36Traffic Volume and Levels of Service
Davis, CA
Showing Traffic Volumes and Delay
Source: Fehr & Peers
37Differential Traffic Effects of Growth Scenarios
Petaluma, CA
Traffic Levels in Different Growth Scenarios
Source: Fehr & Peers
38
Yosemite National
Park
Tuolumne County, CA
Show Origin and Destination Patterns
Source: Fehr & Peers
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 39
Reporting Back
Measure progress every year, like Sustainable City Report Card
Establish thresholds and triggers. For example: Specific increase in net vehicle trips Specific increase in delay on key corridors Increase in per capita CO2 Loss of transit ridership or walk/bike mode
share Specific increase in injuries or fatalities
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 40
Reporting Back
Establish required response when threshold is crossed. For example: Development phasing Stronger development requirements, like TDM Increased impact fees (within what is
justifiable by nexus study) Traffic calming investments Investments in Big Blue, Safe Routes to
Schools, etc.
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 41
For More Information
Jeffrey Tumlin
Mobility Accessibility Sustainability
785 Market Street, Suite 1300San Francisco, CA 94103
415-284-1544
Measuring Success in Santa Monica – CNU Denver 2009
Jeffrey Tumlin, Nelson\Nygaard Consulting 42