Bicycle Supply & Demand Methodologies Seattle, WA Lindsay Donnellon Collaborators: Adam Neel, Tim Pemberton, Elizabeth Padget, Jennifer Hamilton UW GIS Certificate Program June 2010
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Importance of Bicycle Supply and Demand Analysis Whether a city, region, or state is planning for bicycle travel it is important to measure current supply and demand, as well as perform future forecasts for these measurements. Engineers, planners, and decision makers are able to use supply and demand methodologies to evaluate bicycle level of service, bicycle suitability, and demand forecasts. Bicycle supply and demand methodologies can be used to prioritize bicycle facility improvement projects when held accountable to financial resources, identify gaps or inefficiencies in existing bicycle networks, and evaluate roadway conditions for use by cyclists- for commuting or recreational purposes. The bicycle supply and demand methodologies reviewed in this report address the quantitative analysis needed to identify improvements to transportation networks. In performing quantitative analysis it is also important to validate bicycle suitability ratings against actual bicyclists’ perceptions (Shawn P. Turner, July 1997, p.18). Qualitatively, it is important to recognize trends or themes of bicycle analyses or master plans as these may be just as important. In reviewing bicycle plans, bicycle maps, and supply and demand methodologies there was the recurrence of certain themes. These themes centered around ideas such as equity, sustainable, heterogeneous, safety, and complete.
Supply & Demand Methodologies Cycle Network is bicycle-supply specific computer software that calculates current bicycle volumes on road segments. Slight variations are made to simulate a new bicycle facility to see how numbers in ridership also shift. This method is used in the Netherlands, Belgium, Germany, and England. Because calculations are presented graphically gaps are more easily identified. This model can probably be recreated utilizing ArcGIS. Bicycle Level of Service (BLOS) is a supply methodology tool for measuring bicycle suitability. The BLOS Model quantitatively measures the effect roadway width, bike lane widths and striping combinations, traffic volume, pavement condition, motor vehicle speed and type, and on street parking have on bicycle suitability. This model has been adopted by the Florida Dept of Transportation as a methodology for
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determining existing bicycle supply conditions. As of 2007 these cities were using BLOS: Baltimore, MD; Birmingham, AL; Philadelphia, PA; San Antonio, TX; Houston, TX; Buffalo, NY; Anchorage, AK; Lexington, KY; and Tampa, FL) (Sprinkle Consulting, INC, April 2007). (Diego, April 2008) Bicycle Interaction Hazard Score Analysis (FWHA, 1998) is a supply methodology that complements Latent Demand Score, which is described below.
This method was developed by Landis, whom also
developed the Latent Demand Score. Bicycle Compatibility Index (BCI) is another tool for measuring bicycle suitability.
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The Bicycle Compatibility Index model allows planners or engineers to evaluate the capability of roadways to support motorists and cyclists.
Latent Demand Score (LDS) estimates the probability of bicycle travel on individual road or street segments based on their proximity, frequency, and magnitude of adjacent bicycle trip generators and/or attractors. Demand measurements place large emphasis on trip generators like proximity to schools, parks, and other high volume destinations. Latent demand is a GIS algorithm analysis based on an entire region’s corridors. It is a simplified gravity model that recognizes that impedances play a significant role in reducing trip amounts between attractors and generators. This model also recognizes that different trip types and impedances account for differing shares of the total and therefore probability will differ with trip types.
Bicycle Forecasting utilizes extensive bicycle counts to model actual bicycle traffic. This could then be used to weight improvement projects. However, this model does not often capture all cyclists in the city as it is impossible to cover every intersection or block. Additionally, this method does not capture potential or give insight into who is not riding and why.
Note: Figure 2.1 and Figure 2.2 (page 7 & 8) outlines supply and demand methodologies as well as the data needed to conduct each method, respectively.
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Seattle Background (and how this lead to selection of methodology) Seattle’s Bicycle Master Plan and the SDOT’s bicycle facility prioritization model had great influence over the chosen methodologies presented in this report. The current Bicycle Master Plan has the primary goals of increasing bicycle trips and improving the safety of bicyclists. The plan also calls for the existence of an accessible bicycle facility within ¼ mile of all residences. The bicycle facility prioritization model established by SDOT’s Bicycle Program prioritizes new bicycle facilities based on a variety of characteristics. Points may be earned if the facility completes the system proposed in the Bicycle Master Plan; if it is a safety concern or in the proximity of a high number of reported collisions; if the facility is an improvement to mobility—connects to transit, assists across barriers, or links to existing infrastructure; and if the bicycle facility spans a high density area with many destination types. While the existing plan does not make mention of equity, the most recent bicycle facility prioritization model puts great emphasis on equity. Equity has very much become the catch phrase of bicycle plans- Portland just announced their 2040 Bicycle Master Plan and the consistency with which “equity” occurs in the plan’s literature makes it a clear goal in creating and renovating bicycle facilities. Bicycle facilities earn points for addressing areas of high priority for socio-economic and health equity. Since great emphasis is put on safety and equity, it is important to investigate these terms to understand other safety concerns of the cycling community as well as other inequalities of cycling infrastructure and investment. Equity can be assessed in many ways- social equity, gender equity, racial equity, economic equity, network/geographic equity. Socio-economic, racial, and network equity can be assessed using GIS models but gender equity is not as easily identified. To work toward a more enticing cycling network for women, it is important to know their needs and perceptions of cycling in the city of Seattle. Safety has been a particular concern for women nationwide. Much like the term “equity”, safety can be meant to describe collision prevention or crime prevention. In this case, women are afraid of being victims of crime. To listen to their needs, it is important to build bicycle facilities that are well lit and well attended. In advocating for bicycling policy and amenities it is important to be clear on actual needs versus perceived needs, and to connect these needs to an existing population that will be utilizing existing or proposed bicycle networks. However, for the purpose of this report, we will focus on the fact that the
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bicycle facility prioritization criteria is clear on the chosen definition of safety as a means of representing collisions/crashes and equity to define socio-economic and health disparities.
How to Utilize Supply/Demand Methodology in Seattle Based on Seattle’s Bicycle Master Plan and current prioritization criteria, the Seattle Department of Transportation could utilize Bicycle Level of Service tools in assessing the current state of bicycle facility supply. Bicycle level of service (BLOS) is popular in quantifying bicycle safety as a function of per-lane motor vehicle traffic volume, speed of motor vehicles, traffic mix, potential cross street traffic generation, pavement surface condition, pavement width for cycling. 2
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BLOS=0.507ln(vol15/Ln)+0.199SP1(1+10.38HV) +7.066(1/PR5) -0.005 Scores are given one of six grades (a ≤ 1.5; b 1.51-2.5; c 2.51-3.5; d 3.51-4.5; e4.51-5.5; f > 5.5) with the lowest score representing the best safety conditions for bicyclists. Worth noting, in a case study done in the city of Milwaukee, residential roads received grades of either an A or B while collector and arterials received grades of a C and lower (Greg Rybarczyk, April 2010). One additional factor for SDOT to consider adding to the BLOS calculation would be the entrances and exits of bicycle lanes, trails, and sharrows (Lindblom, 2010). To assess the current state of bicycle facility demand, bicycle counts are effective in capturing current trends of riders- namely who is riding. Bicycle counts would be a worthy measure if one’s main purpose was to model the current number of cyclists and where they are riding, in this case counts would have to be quite conclusive. However, bicycle counts do not capture who is not riding and more importantly why they are not riding, nor do counts capture what generates riding (Sprinkle Consulting, INC., April 2002). Bicycle Latent Demand Score (LDS) is an effective tool in measuring bicycle travel demand, and thus bicycle facility demand. Bicycle Latent Demand Score is modeled after LDS for automobiles in that trips are directly related to trip productions or origins and trip attractions or destinations. Bicycle Latent Demand Score can include all key trip generators and attractors, recognizes different trip types account
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for differing shares of trips, estimate the trip making probability of each trip type as a function of distance, and quantifies potential trip route between trip generators and attractors. The graphic below illustrates the Latent Demand Score equation and defines each variable.
Fehr and Peers recommend taking LDS one step further in using a GIS based bicycle demand forecasting that is based on bicycle latent demand forecasting ((Fehr&Peers), April 29 2010). GIS based bicycle demand forecasting selects a combination of land use factors that best describe variation in bicycle demand. These dependent variables are easily remembered by the phrase “rooted in the D’s”, whereas density, diversity, design, destination accessibility, and distance to transit are significant variables. These D’s can be further broken down into population density, employment density, land use diversity, socio-economic diversity, roadway speeds, adjacent roadway volumes, distance to activity centers, proximity to bicycle facilities, and more. This method is a significant model for assessing bicycle exposure, filling in data gaps, and prioritizing improvements. This model is most similar to the prioritization model that SDOT currently employs and could be easily adopted. If SDOT were to adopt this demand methodology it would be recommended that a GIS layer illustrating socio-economic equity and a GIS layer illustrating high collision spots be added to the GIS based bicycle forecasting model.
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What Needs to Be Inventoried
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Bibliography Andrzejewski, M. H. (April 29 2010). GIS Based Bicycle & Pedestrian Demand Forecasting Techniques. Travel Model Improvement Program Webinar. Virtual: http://tmip.fhwa.dot.gov/sites/tmip.fhwa.dot.gov/files/fehr_and_peers.pdf. Bicycle/Pedestrian Trip Generation. (November 1996). Trip Generation Workshop (p. http://www.tfhrc.gov/safety/pedbike/pbworkshop.htm). Washington D.C.: US DOT/FHWA. CRC Transportation Planning Team. (2008, April 27). Columbia River Crossing Memorandum. Columbia River. Diego, E. B. (April 2008). Estimating Bicycle and Pedestrian Demand in San Diego. FWHA. (1998, December). Development of Bicycle Compatibility Index: A level of Service Concept. http://ntl.bts.gov/DOCS/98072/appa/appa_03.html: FHWA. Greg Rybarczyk, C. W. (April 2010). Bicycle facility planning using GIS and multi-criteria decision analysis. Applied Geography , 282-293. Saghir, C. (April 29, 2010). Southeast Michigan Council of Governments State of the artTraffic Count Program. Travel Model Improvement Program Webinar. virtual: http://tmip.fhwa.dot.gov/sites/tmip.fhwa.dot.gov/files/fehr_and_peers.pdf. Shawn P. Turner, C. S. (July 1997). Bicycle Suitability Criteria: Literature Review and State of the Practice Survey. College Station: Texas Transportation Institute. Sprinkle Consulting, INC. (April 2007). Bicycle Level of Service: Applied Model. Tampa, FL: Sprinkle Consulting. Sprinkle Consulting, INC. (April 2002). Technical Analysis- Latent Demand for Bicycle Travel. Laurel, Maryland.
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