Santa Monica Street Typologies Tumlin Cnu

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  • 1. Street Typologies for a Complete Community A New Approach in Santa Monica CA CNU 17 Denver CO June 12, 2009 Jeffrey Tumlin 1
  • 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, NelsonNygaard Consulting 2
  • 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, NelsonNygaard Consulting 3
  • 4. Congestion in Santa Monica: Where we are now Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 4 4
  • 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, NelsonNygaard Consulting 5
  • 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, NelsonNygaard Consulting 6
  • 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, NelsonNygaard Consulting 7
  • 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, NelsonNygaard Consulting 8
  • 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, NelsonNygaard Consulting 9
  • 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, NelsonNygaard Consulting 10
  • 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, NelsonNygaard Consulting 11
  • 12.  Insert auto map Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 12
  • 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, NelsonNygaard Consulting 13
  • 14. Street Typologies: Priority for Each Mode  Insert transit map Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 14
  • 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, NelsonNygaard Consulting 15
  • 16. Street Typologies: Priority for Each Mode Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 16
  • 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, NelsonNygaard Consulting 17
  • 18. Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 18
  • 19. Streets Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 19
  • 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 H  Loading: Percent capacity  Travel speed: Percent of posted speed limit Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 20
  • 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, NelsonNygaard Consulting 21
  • 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, NelsonNygaard Consulting 22
  • 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, NelsonNygaard Consulting 23
  • 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, NelsonNygaard Consulting 24
  • 25. Setting Standards and Significance Criteria FUNCTION CONTEXT ZONE Minimum Desirable Preferred Transit T-QOS T-QOS T-QOS Primary All ≥+1 ≥+1.5 ≥+2 Secondary Downtown ≥-1 ≥-0.5 ≥+1 Retail Streets ≥-1 ≥-0.5 ≥+1 Industrial ≥+0.5 ≥+1 ≥+1 Single family residential areas ≥+0.5 ≥+1 ≥+1 Other transit All - ≥-1 ≥-0.5 Auto Vehicular V:C Vehicular V:C Vehicular V:C Arterial Downtown <1.2 <0.8 >0.6 Retail Streets <1.2 <1.0 >0.6 Industrial <1.0 <0.8 >0.6 Single family residential areas <1.0 <0.6 <0.4 Collector/ Feeder Downtown <1.2 <0.8 >0.6 Retail Streets <1.2 <1.0 >0.6 Industrial <1.2 <0.8 >0.6 Single family residential areas <1.2 <0.6 <0.4 Local All - <0.9 <0.8 Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 25
  • 26. Setting Standards and Significance Criteria FUNCTION CONTEXT ZONE Minimum Desirable Preferred Bicycle Bicycle QOS Bicycle QOS Bicycle QOS Primary Bicycle Downtown D B A Retail Streets D C A Industrial C B A Single family residential areas B A A Secondary Downtown D B A Bicycle Retail Streets D D A Industrial D B A Single family residential areas D B A Pedestrian Ped QOS Ped QOS Ped QOS Primary Downtown B A A Pedestrian Retail Streets B A A Industrial C A A Single family residential areas D B A Secondary Downtown C B A Pedestrian Retail Streets C B A Industrial C B A Single family residential areas D B A Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 26
  • 27. Application  Montana Ave: Neighborhood retail context, 1st priority for pedestrians, 2nd priority for cars and transit FUNCTION CONTEXT ZONE Minimum Desirable Preferred Measured Transit -0.5 Secondary Neighborhood retail ≥-1 ≥-0.5 ≥+1 Auto 0.8 Secondary Neighborhood retail <1.2 <0.8 >0.6 Pedestrian A Primary Neighborhood retail B A A  Result: OK to slightly degrade –auto QOS to improve transit and Measuring Success in Santa Monica CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 27
  • 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, NelsonNygaard Consulting 28
  • 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, NelsonNygaard Consulting 29
  • 30. Start with Transportation Principles  Measure Success  Health  Management  Affordability  Streets  Economy  Quality  Equity  Public Space  Safety  Environment  Public Benefits Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 30
  • 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, NelsonNygaard Consulting 31
  • 32. The Long List Measure Cost/Time Implementation EIR Project Corrid Repo Consumption Review or rt Travel Review Card Model MANAGEMENT •Relative travel times by Medium Can be modeled; see WeHo traffic model. Can also be collected mode through data collection. Transit travel times can be automated in √ √ √ √ √ GPS. •Person capacity – walking, Medium - This is a GIS/Excel type function that can be included if there is bike, transit, auto, parking, Heavy survey data available. Can be modeled. This needs to be further √? √ √? bike parking defined. •Transit LOS: productivity, Medium - This will take extensive model development if we want to get to this farebox return, delay, Heavy level in the demand model. Direct ridership modeling would be reliability 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 √ √ √ √ √ Measuring Success using intersection LOS.) Adjust significance resistance to in Santa Monica – CNU Denver 2009 thresholds if used for EIRs. Jeffrey Tumlin, NelsonNygaard Consulting 32
  • 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, NelsonNygaard Consulting 33
  • 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, NelsonNygaard Consulting 34
  • 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 Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 35
  • 36. Showing Traffic Volumes and Delay Davis, CA Traffic Volume and Levels of Service Source: Fehr & Peers 36
  • 37. Traffic Levels in Different Growth Scenarios Petaluma, CA Differential Traffic Effects of Growth Scenarios Source: Fehr & Peers 37
  • 38. Show Origin and Destination Patterns Yosemite National Park Tuolumne County, CA Source: Fehr & Peers 38
  • 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, NelsonNygaard Consulting 39
  • 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, NelsonNygaard Consulting 40
  • 41. For More Information Jeffrey Tumlin Mobility Accessibility Sustainability 785 Market Street, Suite 1300 San Francisco, CA 94103 415-284-1544 jtumlin@nelsonnygaard.com www.nelsonnygaard.com Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 41
  • 42. Measuring Success in Santa Monica – CNU Denver 2009 Jeffrey Tumlin, NelsonNygaard Consulting 42