Methods for Prioritizing and Quantifying the Benefits of Bicycle and Pedestrian Investments by Cascade Bicycle Club

the benefits of

bicycle and pedestrian projects quantifying & prioritizing non-motorized transportation investments Made possible by funding from Public Health - Seattle & King County and the U.S. Centers for Disease Control & Prevention

Executive Summary

Quantifying the Benefits Associated with Bicycle and Pedestrian Investments

Introduction

Literature Review

Methods for Calculating the Benefits and Costs of Non-motorized Investments

Overview

Methods for Estimating Bicycle and Pedestrian Demand

NCHRP Report 552 Benefit-Cost Analysis Framework

Overview

Estimating Bicycle Demand

Estimating Bicycle Project Costs

Calculating Bicycle Project Benefits

Online Tools

Benefit-Cost Analysis of Bicycle Facilities: bicyclinginfo.org

HEAT (Health Economic Assessment Tool for Bicycling and Walking)

New Zealand Transport Agencyâ&#x20AC;&#x2122;s Economic Evaluation Manual

Section Conclusion

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Non-motorized Transportation Project Prioritization Overview

Introduction

Background

Purpose

Washington State Transportation Planning Context

Non-motorized Project Selection and Prioritization Techniques

Overview

Example Methodologies

Summary

King County Transportation Programming Tool: Prioritizing Non-motorized Projects

Overview

The uses

The structure

Scoring Projects Using the TPT

King County TPT Instructions

Steps to Using the TPT

TPT Conclusion

Data Utilization for Non-motorized Planning and Project Prioritization

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Existing Data Sources: Supporting Non-Motorized Transportation Planning and Project Prioritization

Data Collection Opportunities: Building Support for Non-Motorized Transportation Investments

Non-motorized Data Collection Case Studies

Conclusion

Tools

Reports

Data Sources and Collection

References

Bicycle Facility Cost Estimates: NCHRP 552

Estimating Non-Motorized Demand

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7 Executive Summary Communities face many choices when it comes to investing in transportation. With limited transportation dollars available, it is important to evaluate transportation projects based on their ability to achieve positive outcomes within a community. Constructing new highways and providing additional automobile capacity is becoming less common in transportation planning across the country. Communities face budget and built environment constraints, and are also beginning to see the automobile as an obsolete solution to serving mobility needs in a sustainable, healthy and economically feasible way. However as the population continues to rise in communities across King County and the country, identifying opportunities to improve mobility and maximize the efficiency of existing transportation systems is of critical importance. Bicycling and walking offer tremendous opportunities to improve public health and the health of the environment, support economic development and serve a significant number of trips in communities. In King County, for example, 43 percent of trips are three miles or less yet currently the majority of these trips are made by automobile (Lawarence Frank and Company, 2005). While trends toward bicycling and walking are evident in cities across the country, there remains a need to communicate the value and benefits associated with non-motorized investments to support continued investment in these modes. To this end, this guide focuses on methods and resources to support jurisdictions in quantifying and demonstrating the benefits of nonmotorized transportation investments, ultimately supporting transportation planning, project funding and project implementation efforts. The overarching purpose of this guide is to provide resources and methodologies for community and transportation planners to quantify, evaluate and prioritize non-motorized transportation project investments. The resources synthesized in this guide serve a variety of objectives: (1) to provide communities with monetized information regarding the benefits of investing in bicycling and walking to assist in communicating the importance of these modes from a variety of policy perspectives; (2) to provide online tools for jurisdictions seeking to estimate the demand associated with bicycling quantifying

and walking investments along with the benefits achieved through the implementation of such projects; and (3) to provide a framework and tools for identifying and prioritizing non-motorized projects based on key criteria, including health, climate, safety and mobility outcomes. Section 1 summarizes literature pertaining to the benefits and costs associated with bicycling and walking and investments that support the use of these modes. Specifically, this section provides a series of examples whereby the benefits of bicycle and pedestrian investments have been monetized â&#x20AC;&#x201C; focusing primarily on research that has assigned value to the resulting environmental, economic, public health and societal benefits associated with non-motorized transportation investments. Section 2 outlines a benefit-cost analysis methodology, including a demand estimation procedure, for evaluating non-motorized investments, specifically those related to bicycling. In addition, this section highlights existing online tools that can assist jurisdictions in measuring the benefits and costs associated with non-motorized investments. These resources provide a framework for comparing investments in bicycling and walking with investments in other modes, while also helping jurisdictions to select specific facility types, as well as providing quantitative information for integrating the costs and benefits of non-motorized infrastructure into general transportation planning processes. Section 3 provides an overview of non-motorized transportation project prioritization, summarizing a selection of existing methodologies that have been utilized to define a non-motorized network based on key criteria and ultimately to inform the prioritization of projects. Section 4 builds on the information provided in Section 3 to demonstrate the King County Transportation Programming Tool â&#x20AC;&#x201C; a tool developed based on findings from the 2005 Land Use, Transportation, Air Quality and Health (LUTAQH) research to provide King County and its jurisdictions with an accessible framework for prioritizing non-motorized transportation projects based on key criteria determined to influence the level of benefits attributable to a given a project. This tool can be applied as jurisdictions seek to prioritize projects based on a desired set of outcomes, specifically

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8 improved safety, health, mobility and air quality. The tool itself is available for download here. Section 5 examines the use of data in non-motorized planning and project prioritization efforts. This section provides an overview of existing data sources available to communities to support non-motorized project planning and prioritization in addition to a summary of data collection methodologies that can be utilized to build support for non-motorized projects while evaluating trends.

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S ecti on 1

this, their merits are often brought into question, requiring City staff to seek ways to demonstrate their overall value.

Quantifying the Benefits Associated with Bicycle and Pedestrian Investments Introduction It is commonly understood that bicycling and walking are beneficial activities. As modes of transportation and recreation, they provide physical activity, generate no pollutants or emissions, and offer affordable opportunities for people to access destinations without relying on an automobile. In addition to offering substantial health, environmental and economic benefits, bicycling and walking networks can serve a significant number of trips in communities, thereby reducing congestion and improving the overall efficiency of the transportation system. Moreover, a greater number of trips can be served at a lower cost through bicycle and pedestrian infrastructure, as compared to motor vehicle infrastructure. Bike lanes, for example can accommodate up to 12 times as many people per meter of lane per hour than motor vehicle lanes. Similarly, one on-street parking space can accommodate 12 bicycles. A report from the Rails-to-Trails Conservancy compares the cost of a complete bicycle network to that of one mile of a four-lane urban freeway, indicating that 1,000 miles of bicycle lanes and boulevards and 150 miles of trails can be built for the same investment of about $50 million (Rails to Trails Conservancy). Bicycling and walking rates continue to grow in communities across the United States, yet in most communities, non-motorized transportation remains underutilized and underfunded. This unfortunate trend demonstrates that quantifying and communicating the value of these modes is important, especially as communities seek to fund and build non-motorized projects. Fundamental to encouraging bicycling and walking is ensuring that safe and attractive infrastructure and facilitiesâ&#x20AC;&#x201D;such as bicycle lanes, sidewalks, multiuse trails and end-of-trip facilitiesâ&#x20AC;&#x201D;exist. Although bicycle and pedestrian improvements are inexpensive by comparison to highway and motor-vehicle investments, they still require a monetary investment. Given

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Comprehensive analysis of non-motorized investments can help communities accurately assess tradeoffs, costs and benefits among different solutions to transportation problems and, ultimately, prioritize improvements. Similarly, communicating the range of benefits offered through investments in nonmotorized infrastructure can be an effective strategy for building support for these projects with key community stakeholders and decision makers. The literature reviewed for this Guide revealed that in nearly all cases the benefits associated with bicycle and pedestrian improvements overwhelmingly outweigh the costs. For example, one study estimates that the benefits of new and improved bicycle and pedestrian facilities were four to five times the costs. The following section summarizes the literature pertaining to the benefits of non-motorized transportation investments.

Literature Review Section Overview There are many examples of communities today that experience high rates of bicycling and walking as a direct reflection of investments in nonmotorized infrastructure. However, local jurisdictions still are in great need of quantitative information regarding the benefits and costs of bicycle and pedestrian investments and their ability to address policy goals, such as improved public health and the environment (Transportation Research Board, 2006). This information may be used to accomplish various objectives, including communicating the value of non-motorized investments to the public, demonstrating the benefits to public officials and prioritizing limited transportation dollars. Benefits of increased bicycling and walking through non-motorized investments can be assessed both quantitatively and qualitatively. Quantifiable benefits include reductions in daily vehicle miles traveled and traffic congestion, health care cost savings, reduced mortality and morbidity

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10 rates1, crash cost reductions, changes in mobility and average commute times, improved air quality and economic benefits. Other indirect and less easily quantifiable benefits include improved livability and quality of life, social capital impacts, economic development and social equity. Substantial literature exists related to the benefits and costs of non-motorized investments; perhaps the most straightforward set of guidelines pertaining to benefit-cost analyses of bicycle investments comes from the Transportation Research Board’s National Cooperative Highway Research Program (NCHRP). NCHRP Report 552 (Report 552), Guidelines for Analysis of Investments in Bicycle Facilities, provides research and guidelines to agencies seeking to measure the benefits and costs associated with new and improved bicycle facilities. Report 552 also provides a framework for comparing investments in bicycling to investments in other modes, selecting appropriate bicycle facilities and, and at a systematic level, integrating the benefits and costs of bicycling into the general transportation planning process. The guidelines from Report 552 are summarized in Section 2. The research and guidelines within Report 552 also form the basis of Bicycling Info’s Benefit-Cost Analysis Tool, summarized in Section 2 along with other online tools available for the public to use to quantify the benefits associated with non-motorized investments. In addition to the guidelines recommended in Report 552, substantial research quantifies specific benefits associated with bicycling and walking investments. This literature, summarized below, offers case studies useful for communicating the value of such community investments. Non-motorized Transportation: Benefit-Cost Analysis Literature Conducting a benefit-cost analysis for non-motorized projects can be useful for agencies seeking to compare multiple projects and can form the basis of project comparison across transportation modes. In addition, the results from a benefit-cost analysis offers quantitative information for communicating the value of investing in non-motorized facilities. 1 Morbidity refers to the disease state of an individual, or the incidence of illness in a population.Mortality refers to the state of being mortal, or the incidence of death (number of deaths) in a population.

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Several studies demonstrate the benefit-cost ratio for bicycle and pedestrian investments, based on quantifiable benefits, such as accident cost savings, health care cost savings and pollution reductions. The examples summarized within this section range from local case studies of specific infrastructure projects to national analyses of the monetary benefits of increased bicycling and walking rates.

Example Benefit Cost Ratios for Bicycle Treatments Benefit-cost analyses are not traditionally conducted for bicycle and pedestrian treatments in the United States, however existing research has demonstrated that the benefits of such projects overwhelmingly outweigh the costs, as illustrated in the benefit-cost ratios reported from a European study below. • Speed reduction strategies. BCR = 9:1

A study from Norway assessing the costs and benefits of • Segregated bicycle facilities. bicycling and walking networks in BCR = 9:1. three Norwegian cities concluded that benefits of investments in • Bicycle intersection priority non-motorized networks are treatments (advanced stopbar). four to five times the costs. This BCR =12:1 (uitgeverij, 2000). research took into account the benefits associated with reduced insecurity, increased physical activity, reduced pollution and costs linked with motor vehicle use (Saelensminde, 2004). The City of Sydney conducted a benefit-cost analysis of its proposed bicycle investments, accounting for congestion and pollution reductions, reduced transportation costs, travel time reductions, reduced mortality, increased productivity and improved trip quality. The analysis showed that in comparison to doing nothing at all, the bicycle network would generate a net economic benefit of $507 million, at a benefit-cost ratio of 3.88 (AECOM, 2010). Another study from Australia concluded that every 1,000 kilometers shifted from driving to non-motorized modes generated a net benefit to society of

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11 $1,118 over 10 years and $2,339 over 30 years (2001 Australian dollars). This analysis accounted for vehicle cost savings, improved health, crash risk, and reduced air, water and noise pollution (Ker, 2001). The Rails-to-Trails Conservancy estimates the monetary value of the benefits of bicycling and walking in the United States to be $4.1 billion per year, an amount that reflects transportation costs, oil dependence, climate change and public health benefits. This research also estimates that increasing the mode share of bicycling and walking from its current 9.6 percent to 25 percent would result in $65.9 billion annually in accrued monetary benefits. (Rails to Trails Conservancy). A comprehensive analysis and review of studies estimating the monetary benefits attributable to investments in walking and bicycling from the United Kingdomâ&#x20AC;&#x2122;s Department of Health and Government Office for the Southwest concluded that the median ratio of economic benefits to costs of walking and bicycling interventions was 13:1 (19:1 for UK data alone) (Davis, 2010). The UK report also highlights a model that was developed by SQW Consulting for Cycling for England to predict the monetary benefits for each additional cyclist who chooses to bicycle regularly for a year. The estimates are based on health benefits, productivity gains, pollution and congestion reductions and improved ambience (quality of experience). The benefits are delineated according to location characteristics â&#x20AC;&#x201C; urban, rural, on-road and off-road, as reflected in Table 1(SQW Consulting for Cycling for England, 2008). Table 1: Monetary Benefits of New Cyclists (based on health, productivity, pollution and congestion reductions, and improved ambience) Urban

Rural

$ in pounds

On Road

Off Road

On Road

Off Road

Total Benefits per one additional cyclist per year

$601.06

$641.46

$538.66

$579.06

(SQW Consulting for Cycling for England, 2008)

Research Monetizing Specific Benefits of Non-motorized Investments Analyzing specific benefit categories is useful for cities looking to demonstrate how bicycle and pedestrian projects can address specific quantifying

policy objectives, such as increased physical activity, improved air quality and economic development. The following section summarizes research related to specific benefit categories associated with improving bicycling and walking networks. Public Health

The Following Diseases Have Been Associated with Physical Inactivity Heart disease Hypertension

Stroke Perhaps the greatest benefit of increased bicycling and walking is improved health. Depression In King County, Washington, 54 percent of Diabetes adults are overweight or obese, 20 percent are Osteoporosis obese, and 5.4 percent have been diagnosed Cancer with diabetes. Meanwhile, 30.5 percent of adults do not meet daily physical activity Dementia recommendations (Public Health - Seattle & King County). Bicycling and walking are forms of physical activity and have been correlated with significant health benefits, such as reduced mortality and morbidity rates, stroke, Type 2 Diabetes and heart disease. Meanwhile, approximately half of all deaths in the United States are attributable to preventable behaviors including physical inactivity. Moreover, reduced physical activity has been linked with increasing automobile use and there is also evidence linking adult obesity rates with transportation (Bassett, 2008). In recent years, research findings have shown that the characteristics associated with where people live are determinants in their level of health. Specifically, people living in areas with multimodal features are significantly more likely to One study found that a one percent decrease achieve the U.S. in the use of automobiles can decrease obesity by Surgeon Generalâ&#x20AC;&#x2122;s .4 percent (Samimi, 2008) recommended physical activity levels (Litman, 2011). For example, findings from an Atlantabased study found that residents in walkable areas of the Atlanta region were 2.4 times more likely to achieve 30 minutes of moderate daily physical

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12 activity (Frank). Another study indicated that people living in more walkable neighborhoods make four times as many walking and biking trips and three times as many transit trips, take fewer car trips, and drive fewer miles, than people living in less walkable neighborhoods (Burden, 2001). Health benefits can be quantified in a variety of ways, including increased physical activity levels and corresponding reductions in health care costs, savings in the value of statistical lives, and accident cost savings. Predictions of the health benefits of new investments often take into account the number of people that will shift from driving and other modes to bicycling and walking. A study of the Barcelona public bike share system quantified the overall health impacts of users shifting to bicycling from driving. The study concluded that based on a population of 181,982 bike-share system users, 12.46 fewer deaths were predicted from improved fitness and health (Rojas-Rueda, 2011). Meanwhile, a Danish study found the health and life expectancy benefits of bicycling to be seven times greater than the accident costs and that the societal costs of driving were approximately 12 cents per kilometer (City of Copenhagen, 2008). A recent study concluded that the costs associated with residential sidewalk building were repaid through the health benefits of increased physical activity and reduced pollution. This study found that building sidewalks increase residentsâ&#x20AC;&#x2122; average non-motorized travel by .097 miles, leading to increased physical fitness. The researchers concluded that this type of intervention could counter weight gain in 37 percent of the population, leading to significant health care savings (Guo JY, 2010) (Litman, 2011). Congestion Reduction Improved walking and bicycling conditions can alleviate traffic congestion, providing benefits to all roadway users, particularly in urban areas where approximately 50 percent of all trips are fewer than three miles in length. The factors considered in evaluating the benefits and costs associated with traffic congestion include travel time, vehicle operating costs, stress, and pollution emissions imposed on other roadway users. By reducing automobile travel quantifying

Health Case Study: Walking and cycling trails in Nebraska, USA A benefit-cost analysis was conducted of bicycle and pedestrian trail use in Lincoln, Nebraska. The results indicated that the per capita annual cost of maintaining the trails was $209.28, whereas the per capita direct medical benefit to people using the trails was $564.41. The study concluded that building trails was cost beneficial, as the cost-benefit ratio was 2.94: every one dollar investment in trails for physical activity led to 2.94 dollars in direct medical benefit (Wang, 2005). Health Case Study: Costs and Benefits of Bicycling Investments in Portland, Oregon In 2011, a study was conducted in Portland, Oregon evaluating the City of Portlandâ&#x20AC;&#x2122;s past and planned investments in bicycling infrastructure to identify positive health outcomes. The research quantified two types of health benefits: health care cost savings and statistical life savings (estimates applied to the value of life). The research used past trends in bicycling and future mode-share goals to predict the number of bicyclists who would benefit from investments. The study concluded that investments in the range of $138 to $605 million would result in health care cost savings of $388 to $594 million, fuel savings of $143 to $218 million, and savings in value of statistical lives of $7 to $12 billion. The benefitcost ratios for health care and fuel savings are between 3.8 and 1.2 to 1. This study provides strong evidence to the cost-effectiveness of investments in bicycle infrastructure (Gotschi, 2011). Another report from Portland estimated that the regionâ&#x20AC;&#x2122;s trail network saves the city approximately $115 million per year in healthcare costs and is responsible for countering 17 million pounds per year in weight gain among residents of the Portland metropolitan area (Beil, 2011).

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13 during peak hours, congestion cost savings can average 10 to 35 cents per vehicle-mile traveled in urban areas during peak times (Litman, 2011). Another study estimates that shifting from driving to bicycling for 160 annual trips of average length can reduce congestion costs to other road users by approximately 28 cents per kilometer in urban areas and 14 cents in rural areas (SQW Consulting for Cycling for England, 2008). Environment The transportation sector accounts for nearly 28 percent of greenhouse gas emissions in the United States (Bureau of Transportation Statistics) and nearly 50 percent in Washington state (Washington State Department of Transportation, 2009). Shifting trips from driving in single-occupant vehicles to bicycling and walking reduces the use of fossil fuels and per capita air pollutants and greenhouse gas emissions. An Atlanta-based study, for example, found that for each one-point increase on a five-point walkability scale in communities, levels of nitrous oxides and volatile organic compounds (ozone) decreased 6 percent and 3.7 percent, respectively. Researchers also found that residents in the least walkable neighborhoods generated about 20 percent more CO2 emissions than those living in the most walkable areas (Frank). Other studies have demonstrated the costs associated with motor vehicle use by quantifying motor vehicle pollution. For instance, automobile, air, noise and water pollution are estimated to cost the society between two and 15 cents per vehicle-mile (lower in rural areas and higher in urban areas). Todd Litman of the Victoria Transportation Policy Institute (VTPI) suggests a reasonable estimate for the monetary environmental savings of shifting from driving to non-motorized modes is 10 cents per mile for urban, peak-hour driving, five cents for urban off-peak driving and one cent for rural driving (Litman, 2011). Equity and Mobility Benefits Improving bicycling and walking infrastructure can offer significant mobility and equity benefits to people who are unable or choose not to drive. In the quantifying

United States, more than 60 million people are below the driving age, and another 30 million do not drive for other reasons, such as old age, disability, choice and economics. In a typical community, between 20 and 40 percent of residents do not drive. In King County, Washington, around 9 percent of all households do not own a vehicle – approximately 74,000 households (Place, 2011). Moreover, the demographics across the country and within King County are shifting – the proportion of King County residents who are 65 and over is projected to nearly double by 2050 and approximately 22 percent of older adults do not have access to a vehicle or don’t drive. There are different approaches to monetizing the mobility and equity benefits of non-motorized transportation improvements. Litman suggests calculating mobility benefits based on transit subsidies, which provide a estimate of society’s willingness-to-pay to provide mobility for non-drivers. These subsidies average about 60 cents per transit passenger-mile, 30 cents of which is intended to address basic mobility needs for non-drivers (Litman, 2011). Real Estate Benefits Several studies have shown the relationship between property values and proximity to bicycle and pedestrian amenities. Based on both primary and secondary data analysis, the NCHRP 552 research found that an urbanarea home 400 meters closer to an off-street bicycle facility resulted in a net benefit of $510 (Transportation Research Board, 2006). Another study evaluating home values in relationship to WalkScore numbers concluded that for every one point increase in the WalkScore value, there was an associated $500 to $3,000 increase in home value (Cortright, 2009). In San Diego, a report released by WalkSanDiego revealed that neighborhoods characterized as more walkable maintained almost 5 percent more of their property value than those in unwalkable neighborhoods. Studies have also shown walkability and proximity to trails to be a primary factor in homebuyer preference (Litman, 2011). Results from a 2002 National Association of Home Builders and National Association of Realtors survey found that 27 percent of respondents wished they could walk to more

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14 places from home. Also, when asked about the most important community amenities, 36 percent indicated that jogging and bicycle trails were most or very important and 26 percent indicated sidewalks. Another study from Lake Worth, Florida found that people were willing to pay $20,000 more for homes in pedestrian-friendly communities (Shea, 1998).

person households that use public transit save $6,251 on average annually, compared to those without access to public transit (Complete Streets Coalition). Findings from SMARTRAQ research indicated that households in the least walkable parts of the Atlanta-region spend approximately $2,600 on gas alone as compared to $1,940 in more walkable neighborhoods (Frank).

Employment Benefits

Residents of Portland, where the city government has worked to reduce automobile reliance, travel 2.9 billion fewer miles per year than people in the median American city, saving $2.6 billion per year in transportation costs (Flusche, 2009). According to Todd Litman, by replacing a car trip with a bicycle trip, cost savings to the individual and society are $2.73 per mile (Litman, 2004).

In 2011, the Political Economy Research Institute (PERI) produced a national study looking at employment impacts of bicycle and pedestrian projects from 11 cities in the United States, including Seattle. The research evaluated 58 projects and found that for every $1 million invested, bicycle projects created 11.4 jobs within the respective states and pedestrian-only projects created 10 jobs. Multi-use trails created about 9.6 jobs per million of expenditure. Meanwhile, road projects that included bicycle and pedestrian elements created 8.53 jobs per million, whereas road-only projects created the lowest number of jobs at 7.75 (Garrett-Peltier, 2011). Economic Benefits In addition to reducing individual transportation costs, investments in bicycling and walking can provide significant economic benefits to communities in the form of increased employment, tourism and additional revenue opportunities. Transportation in the United States is the second-largest household expense. In areas where people have limited transportation choices, individual transportation expenditures are often higher. Investments that increase transportation choices, such as improvements to bicycling and walking infrastructure, can reduce individual costs associated with transportation. Research has shown that households in more auto-dependent communities allocate more than 20 percent of household expenditures to transportation compared to 14 percent in communities with more diverse transportation systems (Surface Transportation Policy Partnership, 2000). One study found that people living in households near public transit drive 16 fewer miles per day than those without access to public transit. Litman reports that two-

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Employers can also benefit from improved non-motorized infrastructure and investments. Benefits include: improved health and productivity among employees and reduced health care costs; reduced employer costs associated with the provision of automobile parking for employees; and increased business for local and retail business (discussed in Community and Economic Development section below). A study from Copenhagen found that employees who bike to work had a 40 percent lower mortality

Economic Benefit Case Study Numerous studies have demonstrated the economic benefit of improved non-motorized facilities to communities across the United States. For example, one analysis from the Institute for Transportation Research and Education at North Carolina State University assessed the economic impact of investments in bicycle facilities in the North Carolinaâ&#x20AC;&#x2122;s Outer Banks region. The study concluded that bicycle facilities encouraged tourism and boosted the economy â&#x20AC;&#x201C; finding that bicycle activity in the area contributed to an estimated $60 million annually. The study also found that 53 percent of tourists reported bicycling as a strong influence in returning to the area and 43 percent indicated that bicycling was an important reason for vacationing in the Outer Banks area. The annual economic impact of bicyclists was determined to be 9 times greater than the one-time investment in infrastructure (North Carolina Department of Transportation, 2008).

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15 risk as compared to those that didnâ&#x20AC;&#x2122;t (Andersen et al, 2000), and that bicycle commuters average one less absence per year than those who donâ&#x20AC;&#x2122;t bicycle commute (Davis & Jones, 2007). Meanwhile, employees who participate in physical activity report fewer days off due to illness (6-32 percent) and increased productivity by 2-52 percent compared to physically inactive employees. Another studies shows that physically active employees work at full-efficiency all day, resulting in 12.5 percent greater productivity, and in companies can save $571 per employee per year (British Columbia Cycling Coalition, 2011). Community and Economic Development The economic value of bicycle and pedestrian facilities for local businesses and retail districts has been shown through a variety of research. Studies and surveys that have explored travel behavior in certain business districts report that the majority of patrons arrive to stores by means other than the automobile, indicating the importance of bicycle, pedestrian and transit access for businesses. For example, a survey along Bloor Street in Toronto found that only 10 percent of customers drive to stores and that patrons arriving by foot and bicycle visited and spent the most per month (Sztabinski, 2009). Another study quantified the monetary benefit from sales tax receipts to businesses immediately following installation of a series of roundabouts to calm traffic at La Jolla Boulevard in San Diego, finding that sales increased from approximately $122,000 to $152,000 in one year (WalkSanDiego). Before and after studies quantifying the value of improved bicycle and pedestrian infrastructure in business districts demonstrate the business case for improving non-motorized infrastructure. For example, in downtown Lodi, California, improvements made to the pedestrian environment resulted in a 30 percent increase in sales for downtown businesses, a drop in the vacancy rate from 18 to 6 percent, and 60 new businesses.

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Section 2 Methods for Calculating the Benefits and Costs of Nonmotorized Investments Overview The research referenced in Section 1 offers examples of research monetizing the benefits of investing in bicycling and walking. As communities seek to fund and build non-motorized projects, calculating a benefit-cost analysis specific to such projects can provide valuable information. An initial step in quantifying the benefits of a specific project is determining the number of users that will benefit from its implementation as the number of bicyclists or pedestrians predicted to use a facility will ultimately influence the degree of benefit received through the project. Different methodologies exist for estimating bicycle and pedestrian demand, some of which are described in the following section. In the 1990’s, two comprehensive studies were conducted synthesizing existing non-motorized travel estimation models, one by the Texas Transportation Institute and most recently by the Federal Highway Administration (FHWA) in 1999. The FHWA study identified five major demand estimation models, summarized in Appendix 2. The full FHWA report can be accessed online here.

Methods for Estimating Bicycle and Pedestrian Demand Transportation surveys consistently indicate a high level of demand for improved bicycle and pedestrian facilities. For example, in 2002, a National Highway Transportation Safety Administration (NHTSA) survey found that 70 percent of people polled would like to bicycle more than they do now, however less than half were satisfied with the opportunities presented in their communities to do so. Similarly, a 2010 Transportation for America Survey found that two-thirds of people would like more transportation options so they have the freedom to choose their mode of travel (NHTSA, 2008). Based on surveys alone, there is strong evidence that people would like to bike and walk more than they do. Meanwhile research has also found that quantifying

bicycle and pedestrian infrastructure will lead to increased bicycling and walking. For example, the Rails-to-Trails Conservancy reports that cities in the United States that have invested in bicycle infrastructure have seen increases in bicycling of 10 percent year to year, and in some cases as high as 100 percent. There is also evidence that people are drawn to safe and attractive infrastructure for bicycling and walking – a GPS study conducted in Portland found that the majority of bicycling occurred on streets with bicycle lanes, separated paths, or bicycle boulevards, and that cyclists were willing to travel out of their way to access such bicycle facilities (Dill, 2008). This information demonstrates both a demand for bicycling and walking and a correlation between bicycling and walking infrastructure and increasing use. However in order to determine the demand associated with specific facility improvements, more discrete estimation methodologies are necessary. Predicting the use of a non-motorized facility is useful for a variety of investment and policy decisions. Determining the number of people who will benefit from a project can help to evaluate the extent to which the project will address policy goals, such as reduced greenhouse gas emissions and improved public health, along with the total individual and community benefits that will result through the implementation of the project. Forecasting non-motorized trips can also help agencies when developing bicycle and pedestrian plans, assessing the viability of a non-motorized project, prioritizing projects (as discussed in Section 3), land use policy and planning, estimating transit ridership and establishing public health metrics (Transportation Research Board, 2006). Non-motorized demand is based on a number of factors – factors that influence one’s decision to bicycle or walk to a destination. Based on a number of studies, the following factors have been identified as key predictors of non-motorized travel. Estimating the use of a new or improved bicycle or pedestrian facility should consider many of these factors. •

Distance, both proximity to the project and to key destinations

•

Destinations and trip attractors - within proximity of the project

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18 •

Socioeconomic & demographic characteristics (age, gender, race, income)

•

Travel conditions – roadway characteristics, presence and quality of non-motorized facilities

•

Topography and climate

•

Land use patterns (density and mix)

•

Community perceptions and attitudes

Most project-level demand estimates include data pertaining to existing levels of bicycling and walking. While data sources exist to help determine the amount of existing bicycling and walking in a given area, these sources often undercount nonmotorized travel. Census data, for example, is commonly used to assess the amount of bicycling and walking in communities, however this data only captures the primary mode of travel for work-related trips, excluding discretionary trips, recreational trips, and multimodal trips that include bicycling and walking. Also, because non-commute trips comprise approximately two thirds of all trips in communities, Census data fails to capture the majority of all trips. Other surveys, such as household travel surveys, can provide a more comprehensive picture of bicycle and pedestrian travel, however depending on the scale of the survey, may not provide local data regarding travel behavior. One study found that non-motorized travel is typically three to six times greater than surveys, such as Census, indicate (Rietveld, 2000). In a baseline survey conducted in five U.S. cities, the findings indicated that on a given day, between 15 and 35 percent of adults walked for transportation and 2 to 4 percent biked. The average commute distances were 1.5 to 2 miles and 5 to 8 miles by foot and bike, respectively (Krizek K., 2007). The following section outlines the NCHRP 552 recommended methodology for predicting the demand (in addition to the quantifying

Gibbs Street Pedestrian Bridge: Predicting Existing and Future Bicycle and Pedestrian Demand (ALTA) Complete Methodology Available Here

In 2008, Alta Planning + Design conducted an analysis estimating the bicycle and pedestrian demand for a proposed non-motorized bride over I-5 in Portland, Oregon (Gibbs Street Pedestrian Bridge). As of 2012, the bridge is currently under construction. The demand analysis evaluated existing bicycle and pedestrian trips within the affected neighborhoods to predict the number of trips that would be made across the bridge. Census data was used as a baseline, however because Census data undercounts groups that traditionally have higher bicycling and walking mode shores, additional estimation procedures were used to compensate. In addition to conducting an existing demand estimate, Alta utilized two approaches to predict future bridge demand in 2035 and compared the results. Existing demand for pedestrians and bicyclists in the study areas were calculated for summer and fall, given the anticipated variance in non-motorized trips with school in session. The estimates accounted for existing bicycle and pedestrian commuters, trips made by people who work from home or take transit, and other utilitarian and discretionary trips. Based on the analysis, at the time, 3,845 bicycle and pedestrian trips were predicted to generate in the study neighborhoods and when students were factored in, 7,124 trips were estimated in the neighborhoods. These numbers were then used to predict how many bicyclists and pedestrians would travel across the Gibbs Street Bridge, based on assumptions about commute trip distribution, derived from experience in other areas of the city, future land use projections, and the Bridge’s proximity to recreational trails. The analysis concluded that 422 pedestrian trips would be made across the bridge daily along with 310 bicycle trips. Two approaches were used to forecast future pedestrian and bicycle demand – one based on the Census-based analysis of demand (described above) and the second based on previous experience with non-motorized use of facilities that had not previously existed. The results from both approaches were averaged to determine the likely non-motorized demand for the Gibbs Street Bridge in 2035. The Census-based approach estimated 2,259 daily pedestrian trips and 675 bicycle trips in 2035, whereas the comparison analysis predicted approximately 4,000 bicycle and pedestrian trips per day in 2035. The comparison analysis was based on historic trends with other Portland-area bridges which had experienced an annual growth of about 11 percent per year in bicycle bridge traffic. Taking into account similarities and differences between these bridges, Alta applied a 5 percent growth rate to estimate 2035 Gibbs Street bicycle and pedestrian volumes (Alta Planning + Design).

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19 costs and benefits) for a bicycle project. The framework includes information pertaining to the existing percentage of people bicycling in the project area, along with additional multipliers to capture other bicycle trips that are not accounted for in Census data. The Gibbs Street Bridge Case Study summarized on page 18 demonstrates another approach to estimating both bicycle and pedestrian demand for a proposed non-motorized bridge project. The full methodology can be accessed online here.

NCHRP Report 552 Benefit-Cost Analysis Framework Overview Perhaps the most straightforward methodology for evaluating the benefits and costs associated with non-motorized transportation infrastructure is the NCHRP Report 552, Guidelines for Analysis of Investments in Bicycle Facilities (Report 552). While this report focuses primarily on bicycle investments, the concepts can offer some guidance for pedestrian investments as well. This section describes the approach recommended in Report 552 for calculating a benefit-cost analysis, including a demand estimation methodology (derived from a comprehensive analysis of existing methodologies at the time Report 552 was produced). The demand estimation methodology only applies to bicycle demand, however other methodologies exist to determine pedestrian demand (see Gibbs Street Bridge Case Study on page 18). The guidelines also form the basis for the Bicycling Info’s online benefit-cost analysis tool. This tool is further described in this section, under Tools. The following guidelines offer transportation planners a straightforward methodology for analyzing the costs and benefits associated with specific bicycle investments.

Estimating Bicycle Demand The first step in the process involves estimating the number of users that will benefit from the implementation of the proposed project. The results of the following calculations should be considered within the context of additional,

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relevant factors, such as land use and future planning, topography, demographic factors and the connection to existing bicycle networks. Estimating the use of a new facility under these guidelines is based on two underlying assumptions (1) all existing bicycle commuters near a new facility will shift from another facility, and (2) the new facility will induce new cyclists as a function of the existing number of cyclists. The updated guidelines for Bicycling Info’s online benefit-cost analysis tool suggest that people are more likely to use a new facility if they live within 1.5 miles (2,400 meters), and the likelihood increases even more at 1,600 meters and 800 meters. Therefore, predicting the use of a new facility under this methodology uses 800, 1,600, and 2,400-meter buffers around the project. In order to calculate the demand, the following data is necessary: •

Population density within each buffer area (800, 1,600 and 2,400 meters)

•

Bicycle commute percentage (this can be drawn from the U.S. Census Journey to Work Data for the Metropolitan Statistical Area or for the project area if available)

Step 1: Determine the number of existing bicycle commuters for each buffer area. ⇒ Multiply the area of each buffer by the population density for the area to determine the number of residents in each buffer (R) ⇒ Multiply the number of residents in each buffer (R) by .4 (based on the national average indicating that 80 percent of residents are adults and 50 percent of adults are commuters) to calculate the number of existing commuters. ⇒ Multiply the resulting number for each buffer area by the bicycle commute percentage (C) (U.S. Census Journey to Work). Daily existing bicycle commuters = R * C * .4

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20 Step 2: Calculate Total Daily Existing Bicyclists (adults)

Step 4: Calculate the Number of Induced Bicyclists for Each Group

Because adult commuters only comprise a portion of total adult bicyclists, a comparison among National Household Travel Survey data and U.S. census data revealed that the total adult bicycling rate ranges from the census commute rate at the low end to .6 percent plus 3 times the commute rate at the high end.

Based on the research informing these guidelines, the following multipliers should be applied to each group to determine the number of new cyclists that will be generated through the new facility. These multipliers should be applied to each group (commuters, total adults, and children) for each buffer area. Because Step 2 resulted in a range (low, medium, high), this will need to factor into the final calculations.

⇒ Determine the range of total daily adult bicycling rates by computing the following equations:

⇒ Calculate the new cyclists using the following equations and multipliers:

•

T (high) = .6 + 3C

•

New commuters = existing commuters * L

•

T (moderate) = .4 +1.2C

•

New adult cyclists = existing adult cyclists * L

•

T (low) = C

•

New child cyclists = existing child cyclists * L

⇒ Multiply these rates (low, moderate and high) by the number of adults in each buffer (estimated to be 80 percent of population) to estimate of the total number daily adult cyclists. This will result in three numbers for each buffer:

Where:

Total daily existing adult cyclists= Tj * R * .8 •

Tj = Adult Bicycle Rates (low, moderate, high)

•

R = Residents

Step 3: Calculate the Total Daily Child Bicyclists Based on National Household Travel Survey data (NHTS), approximately 5 percent of children ride a bike on a given day. To estimate the number of existing child bicyclists in each buffer, the following equation is recommended. ⇒ Daily child cyclists = R · 0.2 · 0.05 (20 percent is an average estimate of the number of children living in a given area)

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•

L (800m) = 0.51

•

L (1600m) = 0.44

•

L (2,400m) = 0.1

Step 5: Summarize the numbers to produce an overall estimate for the number of people that will benefit from the new bicycle facility. It should be noted that the guidelines suggested above are largely based on population, existing percentages of bicycle commuters and national averages for multiplying factors, such as percentage of adults and children living in a given area. There are other relevant factors that may influence the use of a new facility, such as its connection to key existing and future destinations, connectivity to the existing bicycle network, connections to transit, and the type of facility that will be provided. Given this, it is important to ensure that the results of these calculations are considered within the context of the project of other relevant factors.

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21 Estimating Bicycle Project Costs A benefit-cost analysis for non-motorized projects requires an estimate of the total project costs. Based on substantial research and outreach to jurisdictions and agencies around the United States, the NCHRP 552 research team produced a bicycle facility cost estimating tool – integrated in the online benefit-cost analysis calculator. The tool is based on four general categories of bicycle facilities: on-street facility with parking, on-street facility without parking, off-street facility, and bicycle related equipment. The cost estimating tool can also be used to determine pedestrian facility costs for some projects. The cost estimates take into account the following project cost categories: •

Roadway Construction

•

Structures

•

Equipment

•

Real Estate Costs

•

Maintenance

•

Other Capital Costs, such as Planning and Design/Engineering.

A table is provided in Appendix 1 outlining the costs associated with specific bicycle project elements as recommended in NCHRP 552. In most cases, the cost estimates are an average derived from a review of existing studies and reported project costs from around the United States.

recreation, and reduced congestion. Other benefits should be considered when communicating the value of the project. The values assigned to each benefit are largely based on the number of cyclists predicted to use the facility. These predictions can be calculated using the information in the Estimating Bicycle Demand section. Mobility The methods proposed for calculating mobility benefits are based on the amount of additional time bicycle commuters are willing to spend to use a facility type. The NCHRP 552 research concluded that bicycle commuters are willing to spend, on average (1) 20.38 minutes extra per trip to travel on an off-street bicycle facility, (2) 18.02 minutes extra for an on-street bicycle lane without parking and (3) 15.83 minutes for an on-street bicycle lane with parking. This research assumes an hourly time of $12 resulting in a per trip benefit of $4.08, $3.60, and $3.17 respectively. To calculate the per-trip benefit, multiply the appropriate benefit factor (depending on the facility) by the number of daily existing and induced bicycle commuters. This number is then multiplied by 2 to reflect return trips, resulting in the daily mobility benefit. The annual mobility benefit is calculated by multiplying the daily mobility benefit by 50 weeks per year and 5 days per week. (Annual mobility benefit = M*V/60 * (existing commuters + new commuters) * 50 * 5 * 2).

Calculating Bicycle Project Benefits

Health

There are direct and indirect benefits that can be assigned to bicycle and pedestrian investments. Direct benefits are those that directly benefit the user – where as indirect benefits are benefits accrued to the community as a whole (such as decreased congestion, reduced pollution, increased livability and fiscal savings). The following values are derived from the NCHRP 552 guidelines and the updated guidelines for Bicycling Info’s Benefit-Cost Analysis calculator (Krizek, Poindexter, Barnes, & Mogush). It should be noted that benefits considered under these guidelines include mobility, health,

The health benefits are calculated using an annual health costs savings from physical activity, per capita. Based on the median value of ten studies that were reviewed, NCHRP 552 concluded the per capita health cost savings to be $128. To compute the health benefit of a new bicycle facility, multiply 128 by the total new cyclists predicted to use the facility.

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Annual health benefit = 128 * total new bicyclists

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22 Recreation

The equation for calculating the reduced auto-use benefit is:

To estimate the recreational benefits for new and improved bicycle facilities, the NCHRP 552 research team reviewed studies analyzing the values generated through outdoor recreation activities, concluding that $40 per day (2004 dollars) could be generated through outdoor recreational facilities.1 A typical day of recreation is estimated to be four hours long generating a $10 per hour net benefit for recreational facilities. With the average adult cycling day around 40 minutes in length, the annual recreation benefit is calculated as follows:

Annual decreased auto use benefit = new commuters * L * S * 47 * 5 •

L = average trip length

•

S = savings per mile

•

47 = number of commuting weeks/year

•

5 = number of commuting days/week

Annual recreation benefit = D · 365 · (Total new bicyclists – Total new commuters) D= $10 (value of a typical day of bicycling)

Online Tools

Congestion

The following online tools can be utilized to calculate the benefits and costs associated with bicycling and walking and specific non-motorized projects.

The guidelines for estimating the benefits of reducing automobile trips considers commute and utilitarian travel that may take the place of an automobile trip. The equation reflects reduced congestion, reduced air pollution and user cost savings. The following assumptions are the basis of this estimation: •

Total amount of new bicycle commuter mileage can represent the total amount of new bicycle riding that will replace a driving trip.

•

The per-mile benefit of replacing auto travel with bicycle travel is dependent on the time of day and location – in other words, there is no congestion benefit in areas where no congestion exists.

•

Reducing congestion through the provision of bicycle facilities is estimated at 13 cents per mile in urban areas, 8 cents per mile in suburban areas and 1 cent in towns and rural areas (Transportation Research Board, 2006).

1 One study, for example, concluded that urban trail facilities in Indianapolis provided a recreational value per trip ranging from $7-$20. Indianapolis Urban Trails Valuation, Bike Cost Guideline

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Benefit-Cost Analysis of Bicycle Facilities: bicyclinginfo.org Bicycling Info’s online benefit-cost analysis tool was developed around the research and recommended guidelines in NCHRP 552. The previous section summarizes the methods and values used to calculate demand, cost and benefits of bicycle projects. The online tool is straightforward and simple to use, asking users to respond to a few questing regarding the project. The online steps include: 1.

Selecting Costs, Demand, or Benefits, depending on the desired calculation (all three can be selected)

Selecting your metro area/region, or choosing “other” if it’s not listed

Selecting “urban” or “suburban” location

Selecting mid-year of construction

Selecting one of the following facility types: on-street bicycle lane

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23 with or without parking, off-street bicycle trail or bicycle-related equipment. 6.

Selecting the type of improvement from a list including: restripe, overlay, full depth, or signed route.

Entering specific information into a cost estimation chart (length of facility, number of bicycle symbols etc.)

Entering bicycle commute percentage for the project area or metro area

Entering the number of persons per square mile within 800, 1,600 and 2,400 meters of the project

Table 2: Example Application, Bicycling Info’s Benefit-Cost Analysis Tool Example Application Using the average density of the city of Seattle, Washington as an example, we estimated the demand and benefits of a 1-mile bicycle lane with parking using Bicycling Info’s online tool. The estimate is based on a 2.9 percent existing bicycle commute percentage. The following table provides the results: Demand (within 1.5 miles of the proposed facility)

Low Estimate

Mid Estimate

High Estimate

Residents

27,776

Existing Commuters

322

New Commuters

100

Total Existing Cyclists

600

5,109

7,911

Total New Cyclists

285

1,681

2,548

Annual Benefits Recreation

Low Estimate

Mid Estimate

High Estimate

$677,635

$5,769,937

$8,934,744

Mobility - Bicycle lane with parking

Per Trip

Daily

Annually

$3.17

$1,337

$8,934,744

Health

Low Estimate

Mid Estimate

High Estimate

$36,526

$215,105

$326,090

Decreased Auto Use

Urban

Suburban

Rural

$5,678

$3,494

$437

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10.

Entering the facility length in meters

Once these values have been entered, a chart is displayed summarizing the total costs, demand and benefits of the project, as shown in Table 2. The Online Tool can be accessed here.

HEAT (Health Economic Assessment Tool for Bicycling and Walking) The HEAT tool was developed by the World Health Organization of Europe, with collaboration from EPA Europe and the Pan-European Programme on Transport. The tool can assist agencies in assessing the value of new bicycle and pedestrian infrastructure from a benefit-cost perspective, in addition to quantifying the health and associated economic benefits of current and future bicycling and walking rates. The tool is primarily used in Europe, but it can be applied across the world to calculate health and economic benefits of cycling and walking projects2. HEAT analysis for cycling and walking investments allows the following calculations to be conducted using the online tool: •

When planning a new project, HEAT provides a value to the estimated level of cycling or walking when the new infrastructure is implemented. Different treatments can be compared to determine which will have the greatest health impact. The costs associated with the project can also be included in the analysis to generate a benefitcost ratio.

•

The tool allows users to place value on the reduced mortality from past and/or current levels of bicycling and walking. This can be applied to different geographic scales, such as at a workplace, in a neighborhood or citywide. It also allows health benefits to be calculated based on projected cycling and walking rates. For example, if the city’s bicycle mode split was 2 percent with a goal

The tool is based on the following data and assumptions from previous literature and research: Cohort studies in Copenhagen concluded that commuter cyclists had a .72 relative risk of all-cause mortality compared to non-cycling commuters. The study controlled for socioeconomic data as well as leisure time physical activity data. Physical activity has positive effects on many aspects of morbidity. Nevertheless, as of now, the current evidence on morbidity, both for walking and for cycling, is more limited than that on mortality and therefore the tool analyzes only mortality. 2

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24 of 4 percent by 2020 – an estimate of the reduced mortality risk by accomplishing this goal can be calculated. In order to use the tool, the following data is needed. There are cases in which the data may not be available to answer the question and assumptions will need to be made. •

An estimate of how many people are walking or bicycling (from counts, surveys, census data)

•

An estimate of the average time spent walking or bicycling in the study area (this can be entered as average duration, average distance, average per person trips or average number of steps per person)

Example Application Auckland Harbour Bridge: The HEAT tool was used to calculate the benefits of a proposed project providing bicycle and pedestrian facilities along the Auckland Harbour Bridge. Based on the assumption of how many bicyclists and pedestrians would use these facilities, the mortality benefits and economic savings were calculated for the investment. The calculation predicted that for every 1,000 additional adults who regularly commuted across the Bridge, the annual savings would be approximately 1,529,000 dollars (New Zealand). Austria Bicycle Mode Split: The HEAT tool was also used in Austria to estimate the benefits accrued through the existing percentage of bicycle commuters (5 percent). The calculations showed that this amount of cycling saves 412 lives every year through physical activity, with an annual savings from this reduced mortality to be 405 million (Austria). The HEAT online tool can be accessed online here, and the User Guide can be accessed online here.

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New Zealand Transport Agency’s Economic Evaluation Manual The New Zealand Transportation Agency’s (NZTA) Economic Evaluation Manual provides a robust methodology for calculating a Benefit-Cost Ratio associated with bicycle and pedestrian improvements. For example, the methodology assumes a $2.70 health and environment benefit factor for 1 kilometer of improved or new pedestrian facilities – such as a new pedestrian path or an intersection improvement. In addition to a comprehensive set of instructions and guidelines, the tool (excel spreadsheet) can be downloaded online here. The instructions and excel spreadsheet guide the user through a series of steps in order to calculate the following costs and benefits: total project costs (maintenance, capital), travel time cost savings, environmental, health and safety benefits, and accident cost savings. The methodology provides a calculation procedure for combining each of these values to produce an estimated benefit cost ratio for each project. Guidelines on estimating bicycle demand are also provided, based on surrounding population, density and existing commute percentage. NZTA provides a wealth of resources available for download on its website, including a word document of simplified procedures for computing the benefit-cost ratio of non-motorized projects (New Zealand Transport Agency, 2010).

Section Conclusion This section provided a snapshot of tools and methodologies that exist to support benefit-cost analyses for non-motorized investments. While each of these tools and frameworks should be considered within the context of the project being evaluated, they can serve as a starting point in generating quantitative information to demonstrate the potential benefits that may accrue through the implementation of a project.

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Section 3 Non-motorized Transportation Project Prioritization Overview Introduction Prioritizing non-motorized transportation projects can serve a variety of needs in transportation planning, project funding and implementation. Prioritization methodologies and corresponding project lists are often incorporated into transportation planning documents such as bicycle and pedestrian master plans. There are a variety of methodologies that can be used for jurisdictions to prioritize projects, ranging from conducting a benefit-cost analysis (discussed in Section 2) of projects and ranking based on the results, to conducting a multimodal level of service analysis (as discussed in Cascade Bicycle Club’s Multimodal Level of Service Guide) to determine where deficiencies exist, to evaluating a list of projects based on criteria and policy goals defined by the jurisdiction. This section offers examples of existing prioritization methodologies that can be used to identify where projects serve the greatest need and offer the greatest benefit – and can be applied city or region-wide or to a short list of projects.

Background Evaluating and prioritizing non-motorized transportation projects is not a new endeavor. Beginning in the 1990’s, research was conducted in an effort to determine level of service needs for non-motorized users. The next step in this process was to develop demand forecasting and level of service strategies for non-motorized transportation calculations. Level of Service methodologies and demand estimation strategies are still relatively new, and not largely utilized in the United States. Cascade Bicycle Club’s recently produced Multimodal Level of Service (LOS) Guide synthesizes existing methodologies for calculating LOS for all roadway users. While non-motorized transportation project prioritization can follow level of service calculations and demand estimation strategies, a need remains for systematic approaches to evaluating non-motorized transportation projects based on quantifying

policy goals and desired outcomes within a community. For example, as communities seek to objectively determine projects with the greatest impact on physical activity, air quality, mobility and safety, project prioritization methodologies offer a useful framework. The methodologies discussed in this section focus on non-motorized project evaluation, however there is considerable opportunity to compare transportation project benefits across all modes.

Purpose There are a variety of reasons why a community may adopt a non-motorized transportation project prioritization framework, for example: •

Objectively prioritizing projects based on key criteria can ultimately generate the desired outcomes of projects. For instance, prioritizing projects based on their ability to impact physical activity and health can result in improved health within the community.

•

As communities face significant budget constraints, a prioritized project list based on an objective analysis can assist agencies in demonstrating the merits of a particular project based on sound data and a documented need and ultimately lead to funding opportunities.

•

Prioritizing projects can provide cities with information to allocate limited funding to projects to address the highest need, greatest demand, and potential return on investment.

•

Prioritization methodologies can also help cities to determine where the needs are within the community and can also provide the framework for a non-motorized transportation plan.

•

Generating non-motorized project lists is a key part of a typical transportation planning process, which can provide the appropriate projects for inclusion in a TIP and CIP.

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26 Washington State Transportation Planning Context The framework for transportation planning in Washington State warrants the use of non-motorized transportation prioritization tools. The Washington State Growth Management Act requires jurisdictions fully planning under the GMA to develop a non-motorized element to their transportation and comprehensive plans. In addition, cities are required to include a Capital Facilities element in their Comprehensive Plan and to update a Capital Improvement Plan (CIP) and Transportation Improvement Program (TIP), listing all projects planned for a six-year time period. Also, projects proposed in local jurisdictional plans are submitted to county and region-wide plans, ultimately enabling funding to be allocated to specific projects. A list of prioritized non-motorized projects will help agencies in all of these aspects of transportation planning and funding. Capital Improvement Plan & Transportation Improvement Program The State Growth Management Act (GMA) requires that communities plan for capital facilities to ensure there is an adequate level of service in place at the time of development. The Capital Facilities Element of the City Comprehensive Plan is a long-range financial plan that allows the City to prioritize public projects and identify adequate funding sources. Although the TIP is technically part of the CIP, GMA requires that transportation be addressed through the Transportation Element of the City Comprehensive Plan, which includes the TIP. Washington State Law also requires that every municipality annually update their TIP for the following six years – this is a list of projects that are both funded and unfunded, but planned for the following six years. In most cases, projects must be included in the city’s TIP in order to be eligible for state and federal funding sources.

identifies the need and opportunity based on a variety of factors. Complete Streets and Multimodal Level of Service Level of Service and Multimodal Level of Service – and its connection to Complete Streets -- is discussed extensively in Cascade Bicycle Club’s Multimodal Level of Service Guide and Complete Streets Guide. Adopting and achieving LOS standards is a requirement for cities planning under the Growth Management Act. Having LOS standards and LOS procedures that address and reflect all modes of transportation can assist agencies in identifying multimodal deficiencies in the system, prioritizing improvements and ultimately facilitating the building of Complete Streets. There are methodologies that provide a framework for calculating the LOS for each mode. Most recently, the 2010 Highway Capacity Manual recommends an integrating approach for evaluating the multimodal level of service for urban arterials and intersections. While Multimodal LOS calculations can be used as a tool to prioritize improvements, the calculations evaluate existing condition factors, rather than provide information about how a project would impact public health or the environment. Multimodal LOS calculations and standards are important for assessing the existing level of performance and safety provided for each mode on a given roadway, while ensuring that cities have the opportunity to build and fund complete streets; this information can help to determine roadway improvements that will improve the functionality and safety for all modes. The project prioritization tools discussed in this guide provide a more complete picture of where the demand is based on a broader set of goals. An entire project list, or complete city street network, can be evaluated through some of the approaches discussed in the following section.

While a City may have a long-range non-motorized plan, all of the projects listed in this plan will likely not end up in a 6-year CIP or TIP. Both the TIP and CIP provide the opportunity and need for an adopted transportation project prioritization methodology. Projects that are listed in these programs are those eligible for funding and ultimately a road-map for what the City intends to build. The projects listed in these plans should be based in a process that quantifying

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27 Non-motorized Project Selection and Prioritization Techniques Overview

demographics and household income. The tool chosen for each community should reflect factors such as available data, desired outcomes and policy goals, and community values as well as information about how the results of the prioritization will be used.

There are a variety of techniques that can be utilized to inform non-motorized project selection and prioritization. As discussed throughout this guide, the value of having a prioritization methodology in place can range from being able to objectively quantify the benefits of a specific project to strategically investing a limited source of funding. When considering the type of prioritization methodology or framework that will be useful in a community, it is important to consider a range of questions, including:

This guide focuses primarily on quantitative-based tools, however there are an array of approaches that can be used depending on the needs and constraints of the implementing agency. The Victoria Transport Policy Institute recommends the following four factors to consider when prioritizing non-motorized improvements. Projects can be evaluated using these four categories to assign ratings, such as High, Medium, Low, or a numerical rating system can be used.

•

What are the community goals related to non-motorized transportation? Defining the desired outcomes can help to establish the criteria and ultimately a weighting system for the transportation prioritization framework.

•

Level of demand: it is important to consider the potential use of the facility, which can be determined through methods discussed in Section 2 or through general knowledge of the surrounding density, both population and employment, and existing travel patterns.

•

What is the political landscape within the community and how should the prioritization framework reflect this? For example, there may be important factors that resonate with decision makers in the community, such as equity or health. These factors should be considered early on as they will help guide the prioritization framework.

•

Degree of barrier: An understanding of the barrier that will be implemented through the project can help in assessing the overall value of the project. The degree of barrier can range from a partial barrier, such as no bicycle facilities on a roadway, to total barrier, such as prohibited bicycle and pedestrian use of a bridge.

•

Are there clear non-motorized transportation strategies and priorities that have already been identified in the community, such as safe routes to school and access to transit?

•

What type of data and how much time is available to conduct an analysis?

Potential benefits: Determining the benefits that would result from the implementation of the project, such as improved health and reduced environmental impacts, can help to prioritize projects based on the degree of benefit and the potential to address key policy objectives.

•

Cost and ease of improvement: The costs and feasibility of implementing the project should be a consideration prioritizing projects. If one project alone costs more than several projects together, it may not be the most appropriate way to allocate limited funding.

•

What level of objectivity is desired in the prioritization framework?

Project prioritization methodologies can range from a simple qualitative matrix to a rigorous GIS or excel-based analytical tool. Tools often include an assessment of the overall project demand, the cost associated with implementing the project, and other social and economic factors, such as population quantifying

In addition to a ranking system, non-motorized transportation project

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Equity (1-3)

Policy Considerations (1-3)

Connections to Transit (1-3)

Ease of implementation (0-2 points)

Lack of parallel facilities (0-3 points)

Potential Cyclist Usage (0-3 points)

Connectivity (o-4 points)

Barriers Overcome: safety (0-5 points)

Land Uses Served (1-2 points; 2 points for light rail, 1 point for schools, parks, business districts etc.)

Table 3: Sample Bicycle Project Ranking Criteria

Project 1

prioritization methodologies often include a weighting system. Depending on the jurisdictional policy framework, some categories may be weighted higher to pull out projects that have a greater potential to address key factors. For example, Safety might receive a higher weight in communities that want to focus non-motorized investments primarily in areas that address safety barriers. Categories may have the same point system, such as 1-3, with different weights assigned to each category, or the point system for categories may vary. Table 3 provides an example of a project scoring and ranking methodology. Using this framework, each project would be assigned a value in each category based on an objective or subjective analysis.

Example Methodologies The following section provides an overview of three prioritization methodologies that offer guidance for jurisdictions seeking a citywide methodology for identifying areas of high non-motorized demand and need. Each methodology is based on a GIS analysis utilizing data to inform where areas within a given city may generate higher non-motorized activity – in these specific examples, pedestrian activity – in addition to data highlighting where key gaps and opportunities exist in the pedestrian network. These methodologies can be used in the initial stages of developing a pedestrian network and master plan and can also serve to inform the prioritization of projects later in the process. Pedestrian Improvements: Identifying Priority Locations In 2001, Targeting Pedestrian Infrastructure Improvements: A Methodology quantifying

to Assist Providers in Identifying Suburban Locations with Potential Increases in Pedestrian Travel, was published under sponsorship of the US DOT and FHWA. The research suggests three tools for agencies to use in allocating investments to improve pedestrian infrastructure, with primary applicability to suburban locations. A primary objective of the research was to identify tools that would assist state and local jurisdictions in identifying locations where improvements would yield the greatest return in pedestrian travel. Three tools were developed through this research: Tool 1 and 2 provide a methodology for determining where pedestrian demand is greatest, and Tool 3 provides a prioritization framework for prioritizing the results from Tool 1 and/or Tool

Example Prioritization Criteria Federal Way, Washington Draft Bicycle and Pedestrian Master Plan The City of Federal Way’s Draft Bicycle and Pedestrian Master Plan proposes the following prioritization framework for selecting priority routes and projects. Priority Route Selection Criteria: •

Suitable for bicycling and walking/without improvements

•

Closes critical gap

•

Provides/enhances Safe Route to School connection

•

Serves immediate safety need

•

Serves key origins and destinations

•

Geographic distribution

•

Right-of-way availability

•

Interface w/other transportation modes

Example Bicycle Project Prioritization •

Proximity to Grocery Stores

•

Proximity to parks

•

Proximity to schools

•

Proximity to public facilities

•

Proximity to transit

•

Number of users (employment density)

•

Number of users (population density)

•

Range of user types

•

Gap identified in existing conditions

•

Connection to existing shared-use path

•

Collision locations

•

Community input

•

Feasibility

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29 2. The methodology recommended for Tools 1 and 2 follows a robust GIS analysis, and is described in detail in the report, which can be accessed here. The tools are designed around research concluding that pedestrian investments focused in areas with high population densities and a mix of land uses can lead to a significant increase in pedestrian travel. The methodologies should be considered particularly in the suburban communities of King County to identify and ultimately prioritize pedestrian need locations. A brief overview of the three tools is provided in this section, however a step-by-step instruction manual is available online to assist agencies in utilizing the tools. Tool 1 & 2: Pedestrian Location Identification (PLI) 1 and 2. PLI 1 & 2 provide guidance on identifying areas with potential for pedestrian travel. These tools emphasize medium-density residential land development and consider the combination of land uses that together serve as generators and attractors for pedestrian travel and thus provide the land use ingredients to support pedestrian travel. Utilizing these tools requires Census and parcel-level data along with aerial photos. Tool 3: Pedestrian Infrastructure Prioritization (PIP) Decision System. PIP provides guidance on allocating investments in infrastructure improvement areas that have potential for pedestrian travel. The framework is flexible allowing for jurisdictions to work under their own set of priorities. This tool is a synthesis of prior efforts to identify environmental and policy variable that influence pedestrian travel, recognizing that the key drivers of pedestrian travel are: land use and development patterns, transportation facility characteristics, and policies that dictate the level of support for pedestrian travel. See pages 72-75 in the report for the prioritization framework. The prioritization tool requires significant knowledge about the project area, including built environment characteristics, topographical considerations, socio-demographic information and transportation facility information. The application of these tools is advised as jurisdictions seek to identify project locations that have the greatest potential to increase pedestrian travel. The three tools require a robust analysis and may only be appropriate quantifying

in jurisdictions with the capacity to undertake such an analysis, however the structure can provide useful guidance and information for agencies developing their own prioritization methods as well. The steps along with the supporting research and example applications can be found on the Washington State Department of Transportationâ&#x20AC;&#x2122;s website here (Moudon, 2001). Pedestrian Improvements: Portland Pedestrian Master Plan Prioritization Framework In Portlandâ&#x20AC;&#x2122;s 1998 Pedestrian Master Plan, a pedestrian prioritization framework was used to identify key investments that would provide the greatest possible public benefit in the most efficient way. Two tools were developed as part of this process: (1) the Pedestrian Potential Index and (2) the Pedestrian Deficiency Index. The tools were intended to evaluate the potential for specific pedestrian projects to increase opportunities for walking and were based on the premise that people are more likely to walk for short trips when specific environmental characteristics are in place. Similar to the Seattle Pedestrian Master Plan analysis (see following section), the highest pedestrian priority projects in the Portland Master Plan were located in areas with deficient pedestrian infrastructure and where the built environment characteristics favored more walking. The Pedestrian Potential Index: To calculate the Pedestrian Potential Index (PPI) factor, every street in the City of Portland was assigned a value based on a series of variables, under three categories: (1) Policy factors, (2) Proximity factors, and (3) Quantitative pedestrian environmental factors. Policy factors were integrated in the overall index by assigning value to street segments that served a greater pedestrian function in the Cityâ&#x20AC;&#x2122;s comprehensive land use plans and policy efforts. For example, street segments located within a Pedestrian District received five points whereas a City Walkway received only two points. The second category, Proximity, assigned value based on the proximity of street segments to key land use destinations, such as schools, parks and transit. The third category was based largely on the results of a Portland-region household activity survey, which indicated that people walked more in areas that exhibited a good land use balance, where many destinations were located within a quarter mile, where the street network was

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30 well-connected, and where the development was at the human-scale. Table 4: Portland Pedestrian Master Plan Network Prioritization Framework

The Pedestrian Deficiency Index: The Pedestrian Potential Index High/Low High/High Deficiency Index (PDI) Low/Low Low/High provided an assessment of Deficiency Index high-deficiency pedestrian Portland Pedestrian Master Plan, 1999 areas, based on a street segment analysis evaluating sidewalks, difficult and dangerous street crossings, and lack of existing connectivity. Dangerous street crossings were determined through traffic speed and traffic volume data, roadway width, and locations with pedestrian – vehicle crashes. The results of the analysis highlighted areas of need, which were primarily located in the outer parts of Portland. To determine projects with both a high potential and a high deficiency, the matrix shown in Table 4 was used. To prioritize the projects, a preliminary list was developed by taking the projects with the highest relative score on both indices. Ultimately, this list was adjusted based on a number of other qualitative factors to reflect community values and to leverage existing opportunities. Projects with little community support and low index values were removed (City of Portland, 1998). Pedestrian Improvements: Seattle Pedestrian Master Plan Prioritization Framework In 2007 the City of Seattle hired SvR Consultants to prepare the Seattle Pedestrian Master Plan. The framework for this plan utilized a GIS-based demand methodology and project prioritization tool to identify needs and priorities for improving the pedestrian infrastructure across Seattle, similar in nature to the methodology used in Portland’s 1998 Pedestrian Master Plan (discussed in the previous section). The results of the analysis provided information to the City on where pedestrian demand was greatest and where critical gaps in the system existed. The GIS framework utilized in the Seattle Pedestrian Plan provides other agencies with a methodology that can be useful as both a preliminary analysis to identify demand across the City and ultimately as a prioritization tool based on the results from the analysis. quantifying

The project prioritization methodology used for the Seattle Pedestrian Master Plan to determine high priority areas was based on a citywide analysis of existing and future needs – accounting for the quality of the pedestrian environment and potential pedestrian activity levels. The data-based analysis allows the City to determine where the highest priority projects are, in order to strategically invest limited transportation dollars. The analysis was based on the following four key steps: Step 1: Base Analysis The base analysis evaluated (a) pedestrian demand, (b) equity and (c) corridor function. The pedestrian demand analysis was based on identification and weighting of pedestrian generators, such as transit stations, parks, schools, grocery stores and libraries, and population and employment forecasts, where pedestrian activity is predicted to be high. Pedestrian generators are weighted differently, depending on their potential trip generation. For example, a high pedestrian generator is a University or College, or a light rail station. The analysis also considers the distances that people are likely to walk to certain destinations, with the understanding that not all destinations will have the same pedestrian catchment area. For example, a University would typically draw more pedestrians within a ½-mile walking distance than a park might. The pedestrian demand index also incorporates citywide population and employment forecasts with a higher weighting assigned to locations with higher forecasts. Map 1 illustrates the pedestrian demand analysis by street segment. The second step in the base analysis evaluated socioeconomic and health characteristics using 2000 Census Block Group data and Health data. The equity analysis was based on the following six factors: automobile ownership, low-income population, disability population, diabetes rates, physical activity rates and obesity rates. The categories were divided into five quantiles, with relatively equal sample sizes in each category. The top quantile received 5 points with a total of 30 possible points given the aggregate of the six factors. The scores for each of the categories were combined to determine a final equity score by Block Groups, with the higher score indicating a higher level of demand or need.

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31 The third step in the base analysis was an assessment of corridor function. With the understanding that some streets serve a greater function for pedestrians than others, the corridor analysis was based on street types (derived from street classifications), which define the intended use of a street. This was also used as a proxy for measuring land use and transportation. The City’s weightings of street types are outlined in Table 5 (the higher the score, the greater the function in the pedestrian realm). Table 5: Street Typology Points 25 Points

15 Points

10 Points

Regional connectors Commercial connectors Local connectors

Main streets Mixed streets Green streets

Residential Residential green Industrial access Industrial arterial

Step 2: High Priority Project Areas To determine the City’s high priority project areas, the results of Step 1 were combined to generate a total score, with the three categories weighted as follows:

experience, such as the width of the roadway, presence of pedestrian infrastructure, and motor vehicle speed limits. Combined, this data provided an index of the quality of the existing pedestrian environment. The Needs Analysis was divided into two categories to comprise the walking experience (1) walking along the roadway and (2) crossing the roadway. The “walking along the roadway” analysis considered the following variables: •

Street classifications (as an indicator of traffic volumes)

•

Arterial speed limit

•

Buffer presence

•

Sidewalk width and presence

•

Slope (low = 0-8%, moderate = 9-12%, high = 13+%)

•

Curb presence

•

Block length (<600 ft, 600+ ft)

The “crossing the roadway” analysis considered the following intersection and roadway crossing variables:

•

Potential Pedestrian Demand: 40% of total score

•

Equity (Socioeconomic and Health): 35% of total score

Table 6: Intersection and Roadway Crossing Variables Segment

Intersection

•

Corridor Function: 25% of total score

Street classifications (to indicate traffic volumes) Arterial speed limit Road width (0-24, 24-36, 38-48, 48-60, 60+) excluding residential areas and interstate highways Distance between traffic signals and stop signs

Crosswalk Curb ramps Signal control Stop sign control Number of collisions at intersection (within three years)

The consultant team produced a map of the combined scores, highlighting the critical roadway improvement locations, based on the analysis in Steps 1 and 2.

Step 4: Developing Priority Projects

Step 3: Needs Assessment The needs assessment determined where key opportunities exist to improve the pedestrian environment in Seattle. This section of the analysis considered environment and infrastructure characteristics that contribute to the walking

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The final step in the analysis combined the results from the Potential Demand Analysis and the Needs Assessment to develop a composite ranking that accounted for areas where conditions are difficult to walk and where the greatest demand for walking exists. Based on these numbers, the City

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32 was able to develop priority project lists for implementation (SvR & City of Seattle, 2008).

Map 1: Seattle Pedestrian Master Plan (Segment Demand Assessment)

Summary There are many choices available to jurisdictions when defining a prioritization framework for non-motorized transportation projects. As discussed in the Introduction to this section, there are a variety of factors that should be considered when developing a prioritization framework in addition to a variety of factors that should be included within the prioritization framework. Non-motorized projects have the potential to provide significant benefits to individuals and communities; a project prioritization tool can assist jurisdictions in identifying those projects that will have the greatest impact. If a jurisdiction is considering developing its own prioritization framework, it is important to first consider what the communityâ&#x20AC;&#x2122;s vision and what the desired objectives of the non-motorized projects are. Understanding these guiding principles can help determine how a prioritization framework can be structured to help identify projects that address the communityâ&#x20AC;&#x2122;s vision. For example, if the primary objective is to address health and equity, the tool would likely consider data regarding demographics, health and socioeconomic factors, such as demonstrated in Seattleâ&#x20AC;&#x2122;s Pedestrian Master Plan prioritization framework. The King County Transportation Programming Tool (TPT) is discussed in the following section, which provides jurisdictions with a ready-to-use tool and methodology for comparing benefits and prioritizing non-motorized projects. This tool was developed for King County and its jurisdictions however can be applied in any community across the country. Along the Roadway Demand Score 0 - 15 16 - 28 29 - 42 43 - 58 59 - 90

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Section 4 King County Transportation Programming Tool: Prioritizing Non-motorized Projects Overview In 2007, the King County Transportation Programming Tool (TPT) was developed using the results of study commissioned by King County known as HealthScape (formerly LUTAQH – Land Use, Transportation, Air Quality and Health). Urban Design 4 Health (previously Larry Frank and Company) was hired to conduct the HealthScape research, focusing on an assessment of the relationships between land use, travel behavior, air quality and climate change, and obesity and physical activity. The following HealthScape findings, along with input from state, regional, and local agencies, helped to frame the development of the TPT - an excel-based tool (download here) used to prioritize non-motorized transportation projects based on health, environment, safety and mobility criteria: •

Residents in the most walkable parts of King County were less likely to be overweight or obese and more likely to meet the 30 minutes of recommended physical activity by the U.S. Surgeon General.

•

Residential density corresponded with a 23-percent increase in the odds of walking for non-work travel.

•

The number of retail establishments corresponded with a 19-percent increase in the odds of walking for non-work travel.

•

Fewer vehicle miles were observed for residents living in areas with greater land use mix and density, street connectivity and retail floor area ratio (Lawrence Frank and Company, 2005).

In 2008, after piloting the TPT using a project list of 10-20 non-motorized projects in King County, the tool was demonstrated using a list of 130 projects from King County’s Transportation Needs Report. To our knowledge,

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the tool has received little use since, however remains a viable and accessible tool for jurisdictions seeking to rank projects based on their ability to achieve positive health, environmental and mobility outcomes. The objective behind the development of the TPT was to provide planners and city staff with an easy-to-use tool to predict the potential benefits of nonmotorized projects. The results from the TPT provide quantitative information for jurisdictions to prioritize non-motorized transportation improvements. Currently, the tool only allows for evaluation of non-motorized projects, however there may be future opportunities to adapt the tool in order to compare benefits across all modal projects. The King County TPT is designed around the overarching goals of increasing benefits and reducing risk to non-motorized travelers. The tool scores projects based on health and environmental criteria, assisting planners and decision makers in selecting projects that will support these goals. The criteria are based on extensive literature and research findings around the benefits of non-motorized transportation projects. The benefits of investing in bicycle and pedestrian projects are discussed extensively in Section 1, however specific to the criteria for the TPT, the following benefits provide the underpinnings: Direct Benefits •

Improving Mobility: Many studies have demonstrated that nonmotorized transportation infrastructure can increase bicycle, walk and transit mode share while decreasing vehicle mode share (Ewing & Cervero, 2001).

•

Improving Safety: Non-motorized projects can improve safety for all modes of transportation by reducing travel speeds and volumes, reducing intersection crossing distances and providing dedicated space for bicyclists and pedestrians to operate. Non-motorized projects can also induce bicycle and pedestrian travel, which based on Safety in Numbers research, positively correlates to improved safety (LFC, Inc, 2006).

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34 Indirect Benefits •

Environmental Benefits: By shifting automobile trips to non-motorized modes, improvements to non-motorized infrastructure can reduce environmental pollutants (Lawrence Frank and Company, 2005).

•

Economic Benefits: A range of economic benefits can result from investments in bicycling and walking infrastructure – from reduced health care costs associated with physical activity to retail benefits of improved walk-and bikeablity.

•

Equity Benefits: Non-motorized projects located in areas with sensitive populations can afford additional mobility to people with limited vehicle access – such as low income, youth and elderly populations.

•

Health Benefits: By encouraging physical activity – bicycle and pedestrian investments can increase the number of people who meet recommended daily physical activity levels, thereby improving health.

The uses The TPT can be applied to answer a variety questions at different planning levels. For example, the tool can be used to develop a citywide prioritized non-motorized project list, or to compare potential benefits between two projects, or even to look at segments of a project that should be implemented first, given limited funding. The tool can also be used as an initial step in the planning process to determine areas of higher demand and need, or areas where land use changes could support additional nonmotorized travel. Projects can be compared across different geographic scopes (countywide, citywide etc), however the results should be considered within this context. In other words, comparing a list of regional projects will likely result in higher scoring projects in urban areas, given the density of land uses and population. Projects can be categorized by urban, rural or suburban to address this. Projects that can be evaluated using this tool include bicycle and pedestrian improvements, such as bike lanes, multi-use trails, traffic calming measures, quantifying

intersection improvements as well as corridor improvements such as roadway rechannelizations.

The structure The TPT is an excel spreadsheet comprised of 24 evidence-based questions within the following topic-areas: (1) Project Type (2) Safety (3) Proximity to Transit (4) New Connections (5) Accessibility, and (6) Potential Demand. Within each of these topic-areas, a series of questions is used to score each project, across the 6 following benefit categories: 1.

Transportation: increase in bicycling and/or walking and decreasing vehicle use

Air Quality/Climate Change: decreasing greenhouse gas emissions

Physical Activity: increase in physical activity and decreasing rates of obesity

Safety: improved bicycle and pedestrian safety

Transit: increasing opportunities to access transit

Accessibility: increased access for populations more likely to bicycle, walk or take transit

Each benefit category is scored separately; the Excel spreadsheet automatically calculates these numbers based on the responses (values) provided for each question. Some of the questions influence each of the benefits, whereas others only apply to specific benefits. For example, the questions that evaluate Project Demand influence all of the benefit categories as the number of people intended to use the project will ultimately impact the total benefits resulted from that project. Table 7 and the section descriptions (within the table), replicated from the King County Healthscape TPT User Guide (download here), illustrate the number of points from each topic area that contribute to the distinct benefit categories.

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35 Scoring Projects Using the TPT

Table 7: Points contributed to each Benefit Category Topic Areas

Benefit Categories Transportation

Air Quality

Physical Activity

Safety

Transit

Accessibility

Maximum Score Section A- Project Type: applies to all benefit categories as the level of benefit will depend on the type of project.

Section B- Safety: questions within the Safety category only apply to the safety benefit, given improvements to safety are generally less related to the other benefit categories.

N/A

Section C- Proximity to Transit: questions regarding proximity to transit assign points to each benefit category (minus Safety and Accessibility) as improved walking and biking connections near transit have been shown to increase bicycling, walking and transit leading to improvements in all categories.

N/A

Section D- New Connections: similar to Proximity to Transit, increases in connectivity lead to increased mode share and benefits in all categories minus safety and accessibility..

N/A

Section E- Accessibility: improved accessibility assigns points only within the Accessibility category; while there may be impacts to mode shift, health and air quality, the research supporting this was not well documented

N/A

Section F -Potential Demand: the demand for the facility will influence the degree of benefit in each category.

The total score for each benefit category is calculated using the following equation, based on the results from each section. The TPT scoresheet also allows the user to calculate a score-to-cost ratio and score-to-project length ratio, which can help in comparing a benefit-cost ratio among projects. Used as a multiplier because the demand will influence each category

N/A

In order to normalize the values â&#x20AC;&#x201C; so scores are comparable between categories

N/A (((Sum of scores for sections A â&#x20AC;&#x201C; E x Section F)/total possible points for each category) x weight by category) x 100 So scores are legible

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36 King County TPT Instructions The TPT tool is primarily an Excel-based tool, however specific questions within the tool should be answered using Geographic Information Systems (GIS). The following section outlines the methodology for using the prioritization tool, and the specific questions asked in each category (Safety, Proximity to Transit, Connectivity, Accessibility and Demand). The complete methodology, including instructions for conducting the GIS analysis, can be downloaded here. The excel spreadsheet (TPT) can be downloaded here.

Map 2: Base Map, Sample Federal Way Projects (Case Study)

Case Study: The following section outlines the steps to using the King County TPT in addition to providing an example application of the tool to a subset of bicycle projects listed in the City of Federal Wayâ&#x20AC;&#x2122;s draft Pedestrian and Bicycle Master Plan. The purpose of highlighting these projects is to demonstrate the potential use of this tool in communities of King County. The proposed project corridors for which the TPT has been applied in this Case Study include: SW 320th St (Enhanced Sidewalks), 9th Ave S (Bike Lane, Trail), 1st Ave S (Shared Lane Markings, Bike Lanes), and S 304th St (Bike Lanes). Map 1 illustrates the project locations and shows how they were delineated to perform the TPT analysis. The results of this analysis are shown in each of the tables that follow. Before beginning the TPT analysis, the following decisions should be made. 1.

Determine how to group projects: depending on the intended use of the project prioritization, it is important to determine how projects will be grouped and ultimately compared. In other words, a city might want to compare all non-motorized projects across the board, or it may want to break the project list into distinct categories, such as by project type, project location or by project cost. Separating projects by categories may be more useful for funding or functional divisions, but it can also make priority setting less straight forward.

Determine how other factors may influence prioritization: Itâ&#x20AC;&#x2122;s quantifying

important to consider how other criteria, beyond the outcomes of the TPT, might influence decision making and the evaluation process.

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37 For example, opportunities may come up to construct a project that is not identified as a top priority, or community support/opposition may become a factor in implementing projects. It should be decided early on how external factors will influence the project evaluation and implementation. 3.

Determine evaluation procedure: There are different analytical approaches to evaluating projects using the TPT. Procedural decisions should be made before beginning the analysis and the steps should be documented to ensure consistency between users. The approach to scoring will likely depend on the availability of data, software and time constraints. In addition, the categories should be clearly defined, so that questions can be answered consistently across projects.

Determine data availability: The following data is needed to run the TPT. If specific data is not available, it should be determined early on how the questions in the corresponding categories will be answered. Specific data needs are outlined below: •

Project Locations

•

Transit stops, including bus and light rail

•

Bus stop boarding data

•

Locations of key destinations (schools, hospitals & medical centers, parks and trails, government facilities including libraries)

•

Urban form variables, including: intersection density, mean retail floor area ratio, residential density

•

Existing bicycle and pedestrian networks

•

Connections between proposed projects and transit, retail, residential, office and educational land uses

Steps to Using the TPT The tool itself is an excel spreadsheet comprised of 9 separate, but linked, worksheets, covering each topic area, weightings, project types and a total score sheet. The TPT can evaluate up to 250 projects at a time. There are different sections, outlined below, that provide a series of questions regarding the project. Some of these sections can be answered based on general knowledge of the project, however for a majority of the questions it is beneficial to use GIS. Specifically, GIS can assist in responding to the questions in Section C, E, and F. The general steps are described below (sections correspond to Excel worksheets in the Tool), and the full methodology is available here (King County HealthScape). To begin, each project should be given a unique Project ID, as each will have a separate row in the corresponding Excel document. STEP 1: Assign Weighting

Population data at the Census Block Group level (age demographics, disabled populations, low income households)

•

Bicycle and pedestrian hazard and crash data

•

Traffic volumes and speeds quantifying

It is up to the user to determine the weighting for each topic area. The default weightings are “1” for each category however weightings can be adjusted to support jurisdictional priorities. For example, a jurisdiction might decide that physical activity should have a higher weighting than air quality. The weightings can be adjusted at any time in the evaluation process, depending on the desired outcomes and the context of the prioritization. Case Study: the weightings assigned to each category for the Federal Way case study are “1”, giving each benefit category equal weighting. STEP 2: Section A. Determine the Project Type The first section asks the user to select from a list of project types. This section will generate automatic numbers for each benefit category depending on the type of project selected. The scores within this category

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38 Table 8: Step 2: Section A - Determine a Project Type

TPT Project Type Examples: 1. Non-motorized trails or paths: projects that offer separation from motor vehicle traffic. 2. Elimination of barriers: projects that offer new accessibility for bicyclists and/or pedestrians. For instance, a new bicycle and pedestrian bridge. 3. Spot improvements: improvements that occur at “spot” locations, such as intersection redesigns, rather than as part of a corridor-wide improvement. 4. Traffic calming: projects that occur on short sections of a street or at intersections with the intent of slowing and reducing vehicle traffic, improving safety, and creating a more attractive place to walk and bike.

are based on assumptions regarding the level of benefit that will result from different improvement types. For example, if an intersection improvement is one the projects being evaluated, the scores will not be as high in Air Quality and Physical Activity as the impact will largely influence safety rather than resulting in a significant mode shift. Each project that is being compared should be included in a unique row for each worksheet and these will need to be the same across each worksheet. STEP 3: Section B. Safety

Project Type

Project Type code

Transportation

Air Quality

Physical Activity

Safety

Transit

Accessibility

1. Non-motorized trails or pathways

2. Elimination of barriers or hazards; creation of pedestrian walkways through superblocks

3. Intersection improvements, curb cuts, signalization changes, crosswalks

4. Traffic calming improvements

5. Corridor improvements, such as road diets, sidewalk additions, bicycle lane additions / improvements, shoulders

Case Study: the case study analyzes 13 segments of the city of Federal Way’s proposed bicycle network along 4 proposed bicycle corridors. Projects types include bicycle lanes, enhanced sidewalks, trails and shared lane markings. For the purposes of this example, sharrows are categorized as Project Type 5, the same as bicycle lanes. Project Code

Project Description

Bike Lane: 1st Ave S (348th-373rd )

Bike Lane: 1st Ave S (320th - 344th St)

Shared Lane Markings: 1st Ave S (Redondo Beach Rd - Dash Point Rd)

Bike Lane: 1st Ave S (Dash Point Rd 320th)

Trail: 9th Ave S (330th - 320th)

Bike Lane: 9th Ave S (320th - 312th)

Bike Lane: 9th Ave S (333rd - 348th )

Trail: 9th Ave S (348th - SR 99)

Bike Lane: 9th Ave S (SR 99 - 16th Ave S)

Bike Lane: S 304th St (Pacific - Military)

Enhanced Sidewalk: SW 320th St ( 1st Ave S and 11th/14th Ave S)

Enhanced Sidewalk: SW 320th St (20th-1st )

Enhanced Sidewalk: SW 320th St (20th - Hoyt )

The next part of the tool 8 focuses on safety and the 9 5. Corridor improvements: a potential impact each majority of projects will fall project will have on bicycle 10 into this category as this refers or pedestrian safety. The 11 to most sidewalk, bike lane criteria used to determine the and shoulder projects, as well 12 safety impact of the project as comprehensive corridor redesigns. includes the history of bicycle 13 and pedestrian collisions, perceived hazards along the project corridor, and traffic volumes and speeds. The jurisdiction using the tool should determine the criteria for classifying a location as a “crash or hazard location,” however because bicycle and pedestrian crashes are often underreported, quantifying

information about traffic volumes and speeds can help determine if the location is a potential hazard to bicyclists and pedestrians regardless of crash and hazard data. The correlation between higher traffic volumes and traffic

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Table 9: Step 3: Section B - Safety Project ID

Q1. Does the project address an accident location (bikevehicle or pedvehicle)?

Q2. Does the project address a known or perceived hazard?

Q3. What is the traffic volume on the closest adjacent street?

Q4. What is the traffic speed on the closest adjacent street?

Yes = 5 pts

ADT over 20,000 (5 pts)

45 mph (5 pts)

Partially = 3

Maybe or partially = 3 pts

ADT 15-20,000 (4 pts)

40 mph (4 pts)

No = 0

ADT 10-15,000 (3 pts)

35 mph (3 pts)

ADT 5-10,000 (2 pts)

30 mph (2 pts)

ADT 5,000 or less (1 pts)

25 mph (1 pts) 20 mph or less(0 pts)

Case Study: The Federal Way sample projects were analyzed using bicycle and pedestrian collision data from 1999 to 2004. Traffic volumes were assessed through the city’s traffic count map (download here). Volumes were taken from the roadway in which the project is proposed. Traffic speeds were extracted from Google Mapmaker. In this case, 1st Ave and 320th received the most points for Safety. 1

speeds and bicycle and pedestrian crash severity provides the rationale for using these categories to predict the safety of the project area and ultimately the safety benefit given to users through infrastructure improvements.

Traffic volumes for adjacent streets

•

Traffic speeds for adjacent streets

Table 9 provides the questions that are asked within the safety category and the corresponding point classification system. If traffic volumes for the adjacent street are unknown, functional classification can provide general guidance on the use (volume) of the street. For instance, a principal arterial will likely have greater traffic volumes than a collector arterial or a non-arterial commercial street. STEP 4: Section C. Proximity to Transit Section C assesses the proximity of the project to transit stops and stations and the existing transit use at those locations. Increasing non-motorized access to transit stops and stations offers additional mobility benefits for bicyclists, pedestrians and transit users, and can serve an even greater function in the overall transportation system. The questions that are asked in this section measure the degree to which the non-motorized project can increase access to public transit. The following data is needed for this section: •

Bus stop locations within ¼ mile of the project

•

Light rail/Bus Rapid Transit stops within ½ mile of the project

•

Bus stop boardings within ¼ mile walking distance/1 mile bicycling distance of the project

To determine proximity to transit stops and transit stop boardings, a GIS analysis should be conducted (steps outlined here). STEP 5: Section D. New Connections

For this section, the following data is needed: •

Bicycle and or pedestrian/vehicle collision data

•

Information regarding perceived hazards in the project area (may be through complaints, project requests etc) quantifying

•

This section measures the level of increased connectivity offered by the proposed project. The analysis determines the degree to which the project fills gaps in pedestrian and bicycle networks and offers connections between origin and destination land uses. This section was developed around LUTAQH research findings, which showed that distance was an important

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Table 10: Step 4: Section C - Proximity to Transit Project ID

Q1. How many bus stops within a 1/4 mile walk/ how many light rail or BRT stops within a Â˝ mile walk of the project?

Q2. What is the transit LOS (level of service, as measured by bus stop boardings) within Âź mile walking distance / 1 mile cycling distance of the project?

5 or more (5 pts) 3-4 (3 pts) 1-2 (1 pt) 0 (0 pts)

High (over 800 boardings at a single bus stop) = 5 pts Med-high (501-800 boardings) = 4 pts Med (201-500 boardings) = 3 pts Med-low (51-200 boardings) = 2 pts Low (0-50 boardings) = 1 pt

Map 3: Proximity to Transit (Federal Way Case Study)

Case Study: The majority of projects analyzed in the Federal Case Study were within close proximity to a high number of bus stops (see MAP). Based on King County Metro boarding data, the number of average daily boardings within the Federal Way project areas did not exceed 200 daily boardings. The majority of projects therefore received 1 or 2 points in the transit LOS category. In this case, the jurisdiction may desire to adjust the transit boarding scale and corresponding point classifications to fit the geographic context of the projects being evaluated. The existing transit boarding scale (listed under Q2) are based on boardings across King County, including Seattle, which may not provide an appropriate rating scale if projects are being compared in smaller jurisdictions. 1

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41 Table 11: Step 5: Section D - New Connections Project ID

Q1. Does the project create a new connection to retail areas from residential, educational or office areas?

Q2. Does the project create a new connection to transit from a residential, retail, educational or office area?

Q3. Does the project fill a gap in the street, pedestrian or bicycle network?

Yes=5 pts

Yes = 5 pts

Improved connection = 3 pts

Yes, improves regional network connectivity = 3 pts

Map 4: New Connections (Federal Way Case Study)

Yes, improves citywide network connectivity = 2 pts Yes, improves neighborhood network connectivity = 1 pt No = 0 pts

Case Study: For Federal Way, the questions in this section were answered using the City’s land use data. Points were assigned based on the project’s location in proximity to the land uses identified in each of the questions above. Question 3 was answered using the City’s existing bicycle network to determine the connection of the proposed project to the network. Projects along 9th Ave S, given the proximity to commercial land uses in the Pacific Hwy S corridor, received the highest scores in these categories (See Map 4). 1

determinate of mode choice as was overall increases to network connectivity. The points received in these categories influence the larger categories of transportation, air quality, physical activity and transit. It should be decided by the jurisdiction, prior to running the TPT, what constitutes an “improved” connection versus an “new” connection. A new connection might mean a new mult-iuse trail or a non-motorized bridge, whereas an improved connection might refer to an upgraded facility, such as new bicycle lanes.

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To respond to the questions in Step 4, the following data or information is necessary: •

Existing bicycle and pedestrian networks (infrastructure)

& prioritizing non-motorized transportation investments

•

Land use data

•

Transit routes and stops

Table 12: Step 6: Section E - Accessibility Proximity to: Project ID

STEP 6: Section E. Accessibility For this section, the user will determine the proximity of the project to key destinations such as schools, libraries, parks and hospitals. This is an important component to the overall analysis in order to assess the potential use and demand for the non-motorized facility. Providing access to key destinations should be a key consideration in developing bicycle and pedestrian networks, particularly as communities seek to reduce singleoccupant-vehicle trips. The point system in the Accessibility category is based on the type of destination and the overall proximity to the project. For example, a civic facility (government building, library) located within 5 miles of the project receives more points than an elementary school located within 5 miles given the scale of impact that the land use will have on non-motorized trip generation. For pedestrian projects, the distances are based on average walking distance thresholds and for bicycle projects, the distances reflect average trip time and distances. A 5-15 minute trip (1-3 mile bicycle trip) is considered a “short trip” and a 15-30 minute trip is a longer trip,

Q1. Elementary school?

Q2. Middle or high school?

Q3. Park or recreational facility?

Q4. Hospital or medical center?

Q5. Civic facility, such as a government building or library?

Q6. Does this project currently meet ADA standards?

For pedestrian or offroad path/trail projects: ¼ mile or less (3 pts) ¼ - ½ mile (3 pts) ½ - 1 mile (1 pts over 1 mile (0 pts)

For pedestrian or offroad path/trail projects: ¼ mile or less (4 pts) ¼ - ½ mile (3 pts) ½ - 1 mile (2 pts over 1 mile (0 pts)

For all projects: 1/8 mile or less (3 pts) 1/8 - ¼ mile (2 pts) 1/4 – 1/2 mile (1 pts) over 1/2 mile (0 pts)

For pedestrian or offroad path/trail projects: ¼ mile or less (5 pts) ¼ - ½ mile (4 pts) ½ - 1 mile (3 pts over 1 mile (0 pts)

No (5 pts) Partially (3 pts) Yes (0 pts)

For bike projects: 1 – 3 miles (3 pts) 3 – 5 miles (2 pts) 1 mile or less (1 pts) Over 5 miles (0 pts)

For bike projects: 1 – 3 miles (4 pts) 3 – 5 miles (3 pts) 1 mile or less (2 pts) Over 5 miles (0 pts)

For bike projects: 1 – 3 miles (5 pts) 3 – 5 miles (4 pts) 1 mile or less (3 pts) Over 5 miles (0 pts)

Case Study: Each of the project segments were located within 1mile of an elementary school, providing only 1 point to each project (based on the assumption that a distance of less than 1 mile will likely walked rather than biked). Most projects were also located within 1 mile of a middle school or high school except for those proposed along 1st Ave S and 9th Ave S just south of 320th St, which are located within 1-3 miles of a middle school or high school (a bikeable distance). Meanwhile, all projects were located within close proximity of a park, according to our analysis; however a local jurisdiction conducting the analysis would have greater knowledge of the parks that should be considered destinations. Hospitals and/or Medical Clinics were located closest to projects along 9th Ave S, 1st Ave S and 320th St. For Question 6, all projects were scored “0” under the assumption that projects currently meet ADA standards, however when using this tool, this question should be scored by someone who is familiar with each project. 1

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& prioritizing non-motorized transportation investments

around a 3-5 mile bicycle trip distance. For trips less than 1 mile in length, it is assumed that the majority of people will walk rather than bike, which is reflected in the point system. It is helpful to use GIS to determine the proximity of each project to key destinations. The data needed to calculate this section include the locations of the following destinations: •

Elementary Schools

•

Junior High Schools

•

High Schools

•

Parks and recreational facilities, including trails

•

Hospital/Medical Centers

•

City, County, State and Federal Government Buildings

•

Libraries

STEP 7: Section F. Potential Demand This section is comprised of questions pertaining to the surrounding population and built environment, acknowledging the importance of population characteristics and urban form in predicting the demand for a bicycle and/or pedestrian project while also addressing social equity. The points generated in this section will have a significant impact on the overall project score, as the total points received in this section function as a multiplier for each of the benefit categories. Questions in this section regard demographics, income, residential density, mean floor area ratio (FAR), land use mix, intersection density and pedestrian-oriented street environment. The questions asked within this section – particularly those referencing built environment characteristics – require a GIS analysis. For King County, a GIS shapefile of the Urban Form variables was developed by the consultants, which can be used by King County jurisdictions to respond to questions 5 through 8. It should be noted that this data is from 2006 and may not quantifying

reflect recent trends. This data shapefile can requested from King County. Alternatively, if the TPT is being used outside of King County or if a King County jurisdiction would like the responses to these questions to reflect more recent data, the methodology for compiling this data is provided in the HealthScape TPT User Guide here. With regard to the scoring, higher scores are assigned to projects that are located in areas with greater numbers of disabled, low income, elderly and youth populations, as these populations tend to have greater dependency on non-motorized modes and transit. The factors considered in this section were derived from the LUTAQH research findings, indicating that projects in walkable areas (comprised of the variables listed in this section) led to increasing walking and bicycling rates and ultimately lower per capita emissions and higher rates of physical activity. For all questions listed in this section, if the project spans more than one Census block group, the block group with the highest score should be used to score the project. To establish thresholds for scoring Questions 1-8, Census block data is quartiled such that an equal number of block groups are in each quartile. The HealthScape TPT User Guide here provides the quartiles and scoring thresholds used for King County, however quartiles should be adjusted to provide locally-relevant scoring ranges, as illustrated in the Table 13 below. The following data is necessary to answer the questions in this section: Census Block Data: •

% disabled households by census block group (Calculated civilian noninstitutionalized population 5 years and over with disabilities)

•

% low-income households (earning 80 percent or less of the King County area median income)

•

Percentage of residents are over age 65 in the Census block group surrounding the project

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44 Table 13: Step 7: Section F - Potential Demand Project ID

Q1. Percentage of disabled households in the Census block group surrounding the project. If the project spans more than one block group, score the block group with the HIGHEST percentage of disabled households.

Q2. Percentage of low income households are there in the Census block group surrounding of the project. If project spans more than one block group, score the block group with the HIGHEST percentage of low income households.

Q3. Percentage of residents are over age 65 in the Census block group surrounding the project. If project spans more than one block group, score the block group with the HIGHEST percentage of residents over age 65.

Q4. Percentage of residents are under age 18 in the Census block group surrounding the project. If project spans more than one block group, score the block group with the HIGHEST percentage of residents under age 18.

Q5. Average net residential density in the block group around the project. If project spans more than one block group, score the block group with the HIGHEST residential density.

Q6. Mean retail FAR in the block group around the project. If project spans more than one block group, score the block group with the HIGHEST mean retail FAR.

Q7. Land use mix in the block group around the project. If project spans more than one block group, score the block group with the HIGHEST level of land use mix.

Q8. The intersection density/ sq mile in the block group around the project. If project spans more than one block group, score the block group with the HIGHEST intersection density.

Q9. Is all or part of the project in an area with a pedestrian-oriented street environment OR where the building codes support the future development of such a street environment? (buildings close to the street, windows, awnings and a high level of detail, street trees/ landscaping, etc.)

TOTAL Score

Case study: The projects that scored the highest in these categories – reflecting a high level of potential demand—crossed through areas with relatively high land use mix and residential density, as well as areas with a greater percentage of youth, elderly and low income populations (populations that tend to have a higher dependence on non-motorized transport). Project #2, 1st Ave S (between 320th and 344th St), Project #6, along 14th Ave S (north of 320th), and Project #11, along 320th (between 1st Ave S and 11th/14th Ave S), scored the highest based on the criteria in this section. Census block quartiles based on Federal Way data

3.4-19.6 (0 pts)

0-3.8 (0)

1.06-4.65

.95-2.98

0-.02 (1 pt)

0-.11(1 pt)

19.6-26.48 (0 pts)

3.8-7.02 (0)

4.65-5.877

2.99-4.45

03-.11 (2 pts)

.12-.18 (2 pts)

26.48-34.49 (1 pt)

7.02-11.23 (1 pt)

5.87-7.56

4.46-5.85

.12-.17 (3 pts)

.19-.36 (3 pts)

34.49-69.52 (2 pts)

11.23-61.6 (2 pts)

7.56-14.66

5.85-13.41

.18-.27 (4 pts)

.37-.77 (4 pts)

12.13-34.64 (1) 34.69-51.77 (2)

Yes = 2 pts

51.78-65.71(3) 65.72-94.38 (4)

(percent below 200% poverty level)

: •

•

Percentage of residents are under age 18 in the Census block group surrounding the project.

Average net residential density in the block group around the project. quantifying

•

Mean retail FAR in the block group around the project.

•

Land use mix in the block group around the project.

•

The intersection density/sq mile in the block group around the project.

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Table 14: Final Scoring Sheet Project ID

Transportation

Air Quality

Physical Activity

Safety

Transit

Accessibility

TOTAL

Cost ($1000s)

Length (linear ft)

Score/ Cost ratio

Score/ length ratio (linear foot)

Map 4: Total Point Scores (Federal Way Case Study)

Case study: Based on the example projects scored in the Federal Way Case Study, projects 2, 6 and 11 received the highest total score. However when considering project costs, others may rise to the surface. As shown in Map 4, the highest scoring projects are located in the core of Federal Way, providing access between residential properties and commercial centers. Each of the high scoring projects are also located in areas characterized by high non-motorized demand. It should be noted that this Case Study serves as an example of how the TPT can be applied, rather than to inform Federal Way’s project selection process. Federal Way should undergo its own comprehensive analysis of projects including data that was not available in our analysis of these projects. 1

41.03

39.70

30.77

43.67

19.85

214.72

1,296.67

8508

0.17

0.15

56.41

54.59

62.05

60.05

40.94

328.64

1,246.99

8182

0.26

0.15

31.70

29.78

30.77

33.75

23.82

179.59

441.37

2896

0.41

0.15

51.40

49.50

53.85

54.71

31.27

290.23

959.09

6293

0.30

0.15

56.64

55.83

39.23

55.83

33.50

296.87

324.66

1345

0.91

0.24

58.97

57.32

36.67

62.78

30.02

303.09

1,021.43

6702

0.30

0.15

53.61

52.11

33.33

57.07

32.26

280.49

842.05

5525

0.33

0.15

40.91

40.32

28.33

40.32

22.58

212.79

356.03

1475

0.60

0.24

31.82

30.65

21.67

33.87

17.74

166.39

241.72

1586

0.69

0.15

44.06

42.43

30.00

46.90

26.80

232.62

669.83

4395

0.35

0.15

58.97

57.32

56.41

62.78

35.48

328.29

753.11

3887

0.44

0.19

41.96

40.20

34.62

44.67

26.80

228.43

1,249.49

6449

0.18

0.19

48.95

46.90

40.38

52.11

31.27

266.51

1,626.92

8397

0.16

0.19

Costs are estimated based on numbers provided with Federal Way’s Bicycle and Pedestrian Master Plan.

•

Pedestrian-oriented street environment or where the building codes support the future development of such a street environment (buildings close to the street, windows, awnings and a high level of detail, street trees/landscaping, etc).

Calculations for each Benefit Category The equations developed for each benefit category in the TPT are as follows. These equations are built into the Excel Spreadsheet. To calculate the Transportation Benefit:

Final Steps: After each of the worksheets have been filled in, the TPT scoresheet (worksheet 1, Table 14) will automatically generate the total project score along with individual scores for each benefit category (Transportation, Air Quality, Physical Activity, Safety, Transit and Accessibility). The category weightings that were defined in the beginning will automatically adjust the score. As mentioned previously, weightings can be adjusted at any point to align with jurisdictional policy goals. quantifying

= Section A (transportation points) +Section C (Q1 + Q2) + Section D (Q1+Q2+Q3) * (Section F Sum of all questions))/858) * Transportation weighting)*100 To calculate the Air Quality Benefit: = Section A (air quality points) + Section C (Q1+ Q2) + Section D

& prioritizing non-motorized transportation investments

46 (Q1+Q2+Q3) * (Section F Sum of all questions))/806) * Air Quality weighting)*100 To calculate the Physical Activity Benefit: = Section A (physical activity points) + Section C (Q1+ Q2) + Section D (Q1+Q2+Q3) * (Section F Sum of all questions) * Physical Activity weighting)*100 To calculate the Safety Benefit: = Section A (safety points) + Section B (Q1+ Q2+Q3+Q4) * (Section F Sum of all questions))/780)* Safety weighting)*100

TPT Conclusion As demonstrated through the Federal Way Case Study, the King County TPT provides a prioritization tool that can be scaled and adapted to fit different jurisdictions, available resources and policy objectives. The TPT is primarily applicable in King County jurisdictions, however can be adapted and utilized in any jurisdiction seeking to prioritize non-motorized projects based on health, environmental, safety and mobility objectives. As mentioned throughout, the a complete TPT user guide is available here, providing a detailed outline of the steps necessary to conduct the GIS analysis portion of the TPT, and the tool itself can be accessed here.

To calculate the Transit Benefit: = Section A (transit points) + Section C (Q1+ Q2) + Section D (Q1+Q2+Q3) * (Section F Sum of all questions))/806)* Transit weighting)*100 To calculate the Accessibility Benefit: = Section A (accessibility points) + Section E (sum of all questions) * (Section F Sum of all questions))/806)* Accessibility weighting)*100 TOTAL SCORE = Sum of the above for each project Score : Cost = Sum/total project cost Cost : Length = Cost/Length

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& prioritizing non-motorized transportation investments

Section 5 Data Utilization for Non-motorized Planning and Project Prioritization Collecting data about bicycling and walking and utilizing existing data sources is an important component to non-motorized transportation planning. Moreover, data is a powerful way to build support for nonmotorized investments, identify the need for those projects in local jurisdictions and support prioritization methods discussed in Sections 3 and 4. This section discusses useful data sources, such as Census data, and data collection methods, such as bicycling and pedestrian counts, that can be utilized to demonstrate the need for non-motorized projects and highlight project benefits. Case studies are provided as well demonstrating the application of non-motorized data collection at different levels.

Existing Data Sources: Supporting Non-Motorized Transportation Planning and Project Prioritization Census data Census data provides local information pertaining to a variety of factors that influence transportation behavior and demand. Census data also provides transportation planners with an indication of how many people are commuting by each mode of transportation. Data reflecting socioeconomic and demographic factors, such as vehicle ownership, household income and age demographics, can help jurisdictions to determine where non-motorized transportation needs are greatest. For example, youth, elderly and lowincome populations tend to utilize non-motorized modes and transit more frequently than other populations; having spatial information to determine where these populations are greatest can be useful in identifying local project needs. Census data also provides population and employment data which can help jurisdictions to predict where non-motorized travel demand is greatest. While there are deficiencies in using Census data (as discussed in quantifying

Section 2) in terms of providing comprehensive information about bicycling and walking (the Census only considers primary mode to work), Census data remains a valuable data source for identifying transportation needs and priorities. National Household Travel Survey1 The National Household Travel Survey (NHTS) provides â&#x20AC;&#x201C; at the national level â&#x20AC;&#x201C; a more complete picture of travel behavior in the United States, including information about trip mode, trip distance by travel mode, trip purpose and various other household information. NHTS data is a useful data source for determining existing demand and predicting future demand for a non-motorized facility. NHTS data can help determine how many actual trips will be generated in a specific area. The most recent dataset is from 2009, which provides information on trip purpose, mode of transportation, travel time, time of trip, and vehicle trip characteristics (U.S. Department of Transportation: Federal Highway Administration, 2011). Like decennial Census data, there are constraints with using NHTS to estimate travel behavior within a small geographic area like a Census tract (although some methods exist for doing so, discussed here). In some regions, like the central Puget Sound, regional household travel surveys offer data around travel patterns and behavior at a smaller geographic scale than NHTS. The data from the Puget Sound Regional Council surveys can be accessed here. Local Data: Land Use, Transportation & Safety Data The prioritization methodologies discussed in Sections 3 and 4 offer guidance on the type of data that can help inform decisions about nonmotorized project needs and priorities. Objective data can provide a strong analytical basis for identifying and prioritizing projects that will yield the The National Household Travel Survey (NHTS) is a U.S. Department of Transportation (DOT) effort sponsored by the Bureau of Transportation Statistics (BTS) and the Federal Highway Administration (FHWA) to collect data on both long-distance and local travel by the American public. The joint survey gathers trip-related data such as mode of transportation, duration, distance and purpose of trip. It also gathers demographic, geographic, and economic data for analysis purposes. 1

& prioritizing non-motorized transportation investments

48 greatest results. Data surrounding land use characteristics can inform where non-motorized demand is highest, whereas transportation data, such as traffic volumes and speeds, can provide useful information about where conditions may be unsafe as well as opportunities for improving nonmotorized transportation systems. Similarly, safety data, specifically bicyclemotor vehicle and pedestrian-motor vehicle crash locations, can indicate where spot and corridor improvements may be necessary. Bicycle and Pedestrian Counts As discussed in the following section, bicycle and pedestrian counts can offer location-specific information about bicycling and walking in communities. In Washington state, bicycle and pedestrian accounts occur on an annual basis, collecting annual data from nearly 30 cities across the state. Meanwhile, the Puget Sound Regional Council initiated a bicycle count program in 2010 to collect data at more than 300 locations across the 4-county region. Data for each of these projects can be accessed through the links below. Washington State Bicycle and Pedestrian Documentation Project PSRC Bicycle Counts

may be identified that allow planners to assess modal shifts and estimate the future demand over time. Non-motorized counts can be conducted on a variety of scales using a variety of methodologies. Bicycle and pedestrian counts can be conducted manually (often by volunteers) or can incorporate innovative count technologies, such as video cameras or tube counters. Bicycle and pedestrian count resources are provided in the Resources section. Travel Intercept Surveys Travel intercept surveys can provide useful information as jurisdictions seek to determine how specific projects will be used while also providing general information about travel behavior in a given area. Intercept surveys involve randomly “intercepting” participants at the site of study to answer specific questions around travel behavior. For example, steps include random selection of persons approached by interviewers, distribution of interviews across the survey time period and across the study area. The purpose, objectives, and methodology of intercept surveys depend on the purpose of the survey itself. The specific elements of two case studies – Seattle and Vancouver, B.C. – are discussed in further details below. Before and After Studies

Data Collection Opportunities: Building Support for Non-Motorized Transportation Investments Bicycle and Pedestrian Counts Bicycle and pedestrian counts provide geographically specific information about bicycling and walking patterns. In most cities across the United States, data is collected about motor vehicle usage along roadways, which ultimately informs transportation planning and dictates where enhancements are needed to improve flow or increase capacity. Collecting data about how and where non-motorized travelers are operating can serve similar functions; it can provide information about the use of specific corridors for bicycling and walking. Non-motorized counts can also demonstrate the impact of a new or improved facility, and if the counts are conducted on a recurring basis, trends quantifying

Before and after studies are essential in evaluating the impacts of a nonmotorized project, from a bike lane installation, to a walking trail, to a full road rechannelization. They are also useful in setting a precedent for future non-motorized investments. In Seattle, for example, before and after analyses are typically conducted in roadway rechannelization projects to gauge the impact on safety, speed and traffic volumes along the corridor. Typical data that is collected through a before-and-after study of a road rechannelization include: •

Speed (85th percentile speed and rates of vehicles exceeding the speed limit)

•

Volume (mode split, average daily travel, peak hour volumes)

& prioritizing non-motorized transportation investments

49 •

Neighborhood cut-through traffic

•

Collisions (by injury severity and by type of collision)

Often, the studies reveal that while peak hour capacity has been maintained, rates of collisions have decreased and rates of bicycle and pedestrian volumes have increased. The following section highlights examples of data collection efforts – including road rechannelizations – that have demonstrated the positive outcomes of bicycling and walking investments while helping to build the case for future investments of this nature.

Non-motorized Data Collection Case Studies When jurisdictions or city staff proposes a new bicycle or pedestrian project, it is often met with opposition. Opposition can be from a diverse range of stakeholders, from adjacent business owners, adjacent residents, the freight community and even local politicians. There are a variety of reasons as to why community members might oppose project, however commonly it is based in perceptions of how the specific project would negatively impact the functionality of a corridor or roadway space. It is important for transportation planners and city staff to be able to demonstrate both the need for the specific project but also be able to show how similar projects have had positive results on the factors of concern. The following case studies demonstrate examples of data collection efforts that have either demonstrated positive effects of non-motorized investments on the study area, been used to help build the case and justify future investments, or both. Washington State Bicycle and Pedestrian Documentation Project The Washington State Bicycle and Pedestrian Documentation Project provides an example of a statewide non-motorized data collection effort. In 2008, the Washington State Department of Transportation initiated the bicycle and pedestrians counts to collect annual data about non-motorized travel across the state. The project is coordinated by Cascade Bicycle Club with volunteers conducting counts in nearly 30 cities across the state. The quantifying

data provides useful information to determine where bicycle and pedestrian travel is occurring and if infrastructure projects are contributing to increasing volumes of bicyclists and pedestrians. The data is also useful for local jurisdictions in seeking non-motorized project funding (Washington State Department of Transportation, 2011). SDOT Neighborhood Business District Access Survey The SDOT Neighborhood Business District Survey took place from September 29 to October 16, 2011 at two to four specified intersections in each neighborhood business district (NBD). A total of six neighborhoods were surveyed for the purpose of gaining a better understanding of visiting and travel behaviors in these Seattle business districts. The objectives were five-fold: 1.

Gauge the general frequency and length of stay of each neighborhood’s visitors

Investigate reasons for visiting each neighborhood business district

Understand how visitors travel to each business district and why they use their chosen modes of travel

Identify ways the City can sustain and improve visitation to its business districts

Quantify the demographic characteristics of travelers to each neighborhood business district

Among the important findings was that most residents (over 61 percent) either walk or take transit to get to their NBD. In fact, over 50 percent of residents in each neighborhood typically walk or bike to their NBD. That number declines for non-residents, but not by much: a high of 43 percent of non-residents walked or bike to the NBD and a low of 13 percent of nonresidents did the same. Also worth noting is that 23 to 31 percent of people surveyed took transit, walked, or biked because out of necessity (fastest, cheapest, no car, etc.).

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50 The findings of this survey clearly support the case for improving walking, biking, and transit conditions to NBDs, both for residents and non-residents of those neighborhoods (Seattle Department of Transportation, 2011). Hornby Street Bike Lane On-site Random Survey Between August 28 and September 2, 2010 a random selection of 500 visitors to the Hornby Street area in Vancouver, B.C. were intercepted and interviewed. The purpose of the Hornby Street Bike Lane On-site Random Survey (Mustel Group, 2010) was to assess public opinion on the topic of dedicated bicycle lanes in the Downtown area in general and Hornby Street specifically. The survey found that on the whole, visitors intercepted on Hornby Street were more likely to support having a protected bike lane on Hornby than to oppose it, and by a significant margin. Furthermore, although vehicle users had a tendency to not support the Hornby bike lane (52 percent), about 25 percent of those who usually drive a car to the area would consider using a protected bike lane. This important finding indicated room for some shift from vehicle use. Shortly after the survey was completed on September 2010, the Vancouver City Council approved the construction of a two-way separated bicycle lane on Hornby Street, including a monitoring and evaluation program. Since then, the introduction of a separated bicycle lane on Hornby Street has resulted in a measurable increase in the number of cyclists, a larger percentage of women and children cycling, and fewer cyclists on sidewalks (City of Vancouver, 2011). Stone Way Rechannelization: Before and After Study In July of 2007, Stone Way N in Seattle was repaved from N 34th St to N 45th St. Prior to the paving project, the street consisted of four general purpose lanes. After the paving project was complete, Stone Way N was rechannelized to provide two general purpose lanes, a two-way left- turn lane, and bicycle facilities (combination of bike lanes and sharrows). The findings from the Stone Way N Rechannelization: Before and After Study (City quantifying

of Seattle: Department of Transportation, 2010) showed: •

Top speeders – those traveling over 40 mph – declined more than 80 percent, while 85th percentile speed declined approximately 3 mph

•

Total collisions declined 14 percent, bicycle collision rate declined, and injury collisions had declined 33 percent

•

Pedestrian collisions reduced 80 percent

•

The volume of cyclists increased 35 percent from 2007 to 2010

•

Peak hour capacity has been maintained and motor vehicle traffic has not been diverted to neighborhood streets

The findings of this study indicated that the Stone Way N rechannelization was a success on many fronts. The findings were not just important to demonstrate that the project achieved its goals, but that future rechannelizations of this nature could be completed with similar outcomes. Nickerson Street Rechannelization: Before and After Study In August of 2010, the Seattle Department of Transportation (SDOT) completed a similar rechannelization on Nickerson Street from 13th Ave W to Florentia St. The goal of the project was to improve pedestrian safety by reducing exposure to multiple lanes of traffic and increasing driver compliance with the speed limit. The Before and After Report revealed that the percent of drivers traveling over the speed limit reduced by more than 60 percent and the percent of top-end speeders reduced by 90 percent. Collisions reduced 23 percentage points (City of Seattle, Department of Transportation, 2011). Like Stone Way N, the Nickerson Street rechannelization was a great success. However, it was also a very controversial project for the City of Seattle and was heavily opposed by the freight community. The findings from the Stone Way N before and after study were critical in helping to convince Seattle residents, drivers, and the freight community that the project would improve safety while maintaining corridor capacity, and providing the City the evidence it needed to proceed with the project.

& prioritizing non-motorized transportation investments

51 Conclusion The information provided in Section 5 is intended to demonstrate the importance of collecting and utilizing data in non-motorized transportation planning and design, while supporting efforts to communicate the merits of non-motorized investments. This section along with Section 3 and 4 also provide information as to the use of existing data sources in determining where non-motorized projects are in high demand or need. For example, demographic and socioeconomic data from U.S. Census surveys show where populations with greater mobility needs exist, whereas land use data, employment and population estimates, provide information on where the greatest number of non-motorized trips may be made.

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Resources

The Hidden Health Costs of Transportation

Data Sources and Collection

Tools

PSRC Bicycle and Pedestrian Counts

Benefit-Cost Analysis of Bicycle Facilities: Bicyclinginfo.org

PSRC Household Activity and Travel Surveys

World Health Organization Health Economic Assessment tool (HEAT) for cycling and walking

National Household Travel Survey

New Zealand Transport Economic Evaluation Manual and Tool

2010 Census Data

King County Transportation Programming Tool

Washington State Bicycle and Pedestrian Documentation Project

Targeting Pedestrian Infrastructure Improvements: A Methodology to Assist Providers in Identifying Suburban Locations with Potential Increases In Pedestrian Travel

National Bicycle and Pedestrian Documentation Project

Reports King County Transportation Programming Tool Documentation, HealthScape NCHRP 552: Guidelines for Analysis of Investments in Bicycle Facilities, Transportation Research Board, 2006 Guidebook on Methods to Estimate Non-Motorized Travel, FHWA, 1999 Economic Benefits of Bicycle Infrastructure Investments, League of American Bicyclists, Alliance for Biking and Walking, 2009 Economic Benefits of Cycling for Australia Realizing the Benefits of Accelerated Investment in Cycling, British Columbia Cycling Coalition, 2011 Value for Money: An Economic Assessment of Investment in Walking and Cycling, Department of Health (UK), 2010 A Great Summary of Health-related benefits of bicycling Active Transportation for America: The Case for Increased Federal Investments in Bicycling and Walking, Rails to Trails Conservancy, 2008

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References

Summer 2011. Vancouver.

References

Complete Streets Coalition. (n.d.). Complete Streets Lower Transportation Costs. Retrieved from http://www.completestreets.org/webdocs/factsheets/ cs-individuals.pdf

British Columbia Cycling Coalition. (2011). Realizing the Benefits of Accelerated Investment in Cycling. AECOM. (2010). Inner Sydney Regional Bicycle Network: Demand Assessment and Economic Appraisal.

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quantifying

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Bicycle Facility Cost Estimates: NCHRP 552 Cost Estimates (NCHRP) – 2002 bicycle project cost estimates Category

Cost Average

Earthwork