Guidebook on Pedestrian and Bicycle Volume Data Collection (2014)

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10 C H A P T E R 2 This chapter describes potential uses of pedestrian and bicycle data, using actual projects as examples of how the data can be applied. The practitioner survey conducted as part of the research leading to this guidebook found that the most common applications of non-motorized count data in the United States and Canada were (in decreasing order of usage): • Tracking changes in pedestrian and bicycle activity over time, • Evaluating the effects of new infrastructure on pedestrian and bicycle activity, • Prioritizing pedestrian and bicycle projects, • Modeling transportation networks and estimating annual volumes, and • Conducting risk or exposure analyses. 2.1 Measuring Facility Usage 2.1.1 Potential Applications Details Pedestrian and bicycle counting can serve as part of a larger transportation system monitoring program. This effort typically entails counting at set locations at regular intervals (e.g., annually) to identify how particular facilities are being used. As continuous data collection technologies are increasingly available, user volumes (especially on key facilities) can be observed throughout the year, providing a richer understanding of how usage changes over time. An agency may also select locations to regularly collect non-motorized counts as a means of identifying growth trends in walking and bicycling on a particular facility or on the system as a Non-Motorized Count Data Applications Chapter 2 Topics • Measuring the usage of a pedestrian or bicycle facility • Measuring the change in pedestrian or bicycling activity following the develop- ment or improvement of a facility • Monitoring non-motorized travel patterns • Using non-motorized count data to evaluate risk or exposure as part of safety analyses • Applying count data when prioritizing transportation projects • Using count data in developing and validating multimodal travel demand models

Non-Motorized Count Data Applications 11 whole. The FHWA released a policy statement in 2010 that included the following recommended action: “Collecting data on walking and biking trips: The best way to improve transportation networks for any mode is to collect and analyze trip data to optimize investments. Walking and bicycling trip data for many communities are lacking. This data gap can be overcome by establishing routine collection of non- motorized trip information. Communities that routinely collect walking and bicycling data are able to track trends and prioritize investments to ensure the success of new facilities. These data are also valuable in linking walking and bicycling with transit” (FHWA 2010). As more agencies develop goals and targets for increasing the number of persons who walk and bike, having a database of pedestrian and bicyclist count data is critical for tracking progress and measuring success. 2.1.2 Example Applications City of San Mateo, California The City of San Mateo began conducting pedestrian and bicycle counts as a result of a Bicycle Master Plan adopted in 2011. The plan stated that bicycle counts “evaluate not only the impacts of specific bicycle improvement projects but can also function as a way to measure progress toward reaching City goals such as increased bicycle travel for trips one mile or less” (Alta Plan- ning + Design, Bicycle Solutions, and Hexagon Transportation Consultants 2012). The city uses these counts to evaluate bicycle and pedestrian mode share and may create annual “report cards” in the future to document bicycling activity. The City sees these counts as important for putting bicycling and walking on equal footing with motor vehicles. The costs “add legitimacy” to the non-motorized modes. The city conducts manual counts (at 17 locations at the time of writing), using both city staff and volunteers. Rou- tine count locations were identified in the master plan and grouped into Tier 1 (high priority) and Tier 2 locations. The City hopes to expand its program to include all Tier 1 locations and to begin counting some Tier 2 locations as well. The City also conducts routine pneumatic tube counts, which are integrated into the motorized count database. In addition, San Mateo County requires private developers to conduct vehicular, pedestrian, and transit counts as part of their development’s traffic impact study. The City of San Mateo adds the data from these studies to its count database. Washington State Department of Transportation (WSDOT) WSDOT’s Washington State Bicycle Facilities and Pedestrian Walkways Plan (2008) identified bicycle and pedestrian counts as a key data need for assessing growth in multimodal trips and measuring progress toward the state’s goal of doubling the number of bicycle and pedestrian trips by 2027. WSDOT launched the Washington State Bicycle and Pedestrian Documentation Project in 2008 to track changes in bicycling and walking across the state. The project has col- lected counts in late September or early October annually since 2008, in conjunction with the National Bicycle and Pedestrian Documentation (NBPD) Project. The project relies on volunteers to collect manual counts at identified count locations throughout the state. The locations are selected by local count coordinators, following siting criteria suggested by the state, in an effort to make the data valuable to the local jurisdiction as well as to the state. The locations may be selected to demonstrate the prevalence of walk- ing or biking, illustrate before-and-after volumes at a location with a planned improvement, or assess exposure rates at a high crash location. Local count coordinators are encouraged to choose count locations that demonstrate at least one of the following siting criteria (which

12 Guidebook on Pedestrian and Bicycle Volume Data Collection have been identified through the NBPD method as typical characteristics that provide valu- able data): • Historical count location • Bicycle facility • High collision area • Smart growth area • Transit corridor • Planned project • Mixed land use • Stakeholder recommendations Thirty-eight cities participated in the 2012 counts. In addition to tracking the total vol- ume of pedestrian and cyclists at each location, Washington counts users by gender and (for bicyclists) by helmet use. Based on data collected at locations statewide, Washington has shown that non-motorized travel is up significantly since the project’s inaugural counts in 2008. Because count locations have changed over the years, the total number of non-motorized travelers cannot be compared. However, the data from select locations where counts have been conducted consistently since 2009 can be isolated and compared, as shown in Fig- ure 2-1. The graph suggests an overall increase in non-motorized travel, particularly between 2009 and 2010. 2.2 Evaluating Before-and-After Volumes 2.2.1 Potential Applications Collecting bicycle and pedestrian counts before and after a new facility is opened can be valuable for measuring volume changes and making conclusions about the success of the facil- ity. The data can also be used to forecast the usage of planned facilities and to justify additional system improvements based on past results. 2.2.2 Example Applications Delaware Valley Regional Planning Commission, Philadelphia Region The Delaware Valley Regional Planning Commission (DVRPC) has actively counted bicycles and pedestrians since 2010 and has data from over 5,000 locations throughout its region. Source: Washington State DOT (2012). Figure 2-1. Change in walking and bicycling activity at Washington State count sites, 2009–2012.

Non-Motorized Count Data Applications 13 Counts are generally conducted as part of before-and-after studies of new infrastructure. All of the DVRPC data are accessible to the public on line, using a map-based application (Figure 2-2). District Department of Transportation, Washington DC. The District Department of Transportation (DDOT) evaluated three facilities where new bicycle treatments had been implemented. The evaluation used various data and analysis tools, including before-and-after bicycle counts. One of the treatments added buffered bicycle lanes in the center median of Pennsylvania Avenue between 3rd Street NW and 15th Street NW. Bicycle counts were collected before and after the installation to assess the change in bicycle volumes. As shown in Figure 2-3, bicycle volumes increased significantly after the treatment was installed. Source: DVRPC, http://www.dvrpc.org/webmaps/pedbikecounts/. Figure 2-2. Example pedestrian and bicycle count website. Source: Kittelson & Associates, Portland State University, and Toole Design Group (2012). Figure 2-3. Before-and-after bicycle facility usage example.

14 Guidebook on Pedestrian and Bicycle Volume Data Collection This information could be used to (1) demonstrate project success, (2) support installing similar treatments in the future, and (3) help estimate future bicycle activity levels when planning future treatments. 2.3 Monitoring Travel Patterns 2.3.1 Potential Applications Continuous pedestrian and bicycle counts can be used to identify usage patterns across the day, week, or year, and to identify factors that influence bicycling and walking levels (e.g., weather, land use patterns, and transportation network characteristics). Developing Extrapolation Factors Extrapolation factors are used to expand short-duration counts to estimate volumes over longer time periods or to compare counts taken under different conditions. Volume patterns across the day, week, or year are identified so that shorter duration counts can be extrapolated to longer time periods. For example, if a given 2-hour period has been shown to typically contain 10% of the daily volume, then a 2-hour count during this same time period at a similar site could be multiplied by 10 to estimate the daily volume. Extrapolation factors can be used to control for pedestrian and bicycle activity patterns near specific land uses, the effect of weather conditions, access/infrastructure sufficiency, or surround- ing area demographics. Extrapolation is useful when resource limitations prevent organizations from collecting data over an extended period of time at all locations where volumes are desired. Chapter 4 provides additional information on how to apply extrapolation factors and the level of uncertainty associated with applying adjustment factors. Evaluating User Behavior Patterns Non-motorized counts can be used to evaluate user behavior patterns and to identify factors that influence bicycling and walking. Some factors that affect pedestrian and bicycle activity are outside an agency’s control (e.g., the day of the week, temperature, and rainfall). For example, Copenhagen, Denmark, schedules extra buses for its busiest bus line on days when rain is predicted, because bike riders shift to transit on those days, and buses would become overcrowded if they operated under the regular schedule (Jacobsen and Lorich 2013). Other factors may be more controllable, such as land use type, facility type, or motorized vehicle volumes (e.g., through motorized traffic calming and diversion). Understanding how these factors influence bicycling and walking rates can help agencies better plan their transportation systems. 2.3.2 Example Applications San Diego County, California The Seamless Travel Project, using San Diego County as a case study, developed a database of pedestrian and bicycle count and survey data to analyze and identify factors that influence bicy- cling and walking. The project evaluated the effects that land use, density, access, roadway traffic volumes, facility type, and other factors have on walking and bicycling rates. In addition, the project was designed to provide a comprehensive count of pedestrian and bicycle activity in the county. The project included two manual peak-period counts (from 2007 and 2008) at 80 locations and 1 year of continuous automated counts at 5 locations. The data from the five locations were

Non-Motorized Count Data Applications 15 Source: Jones et al. (2010). Figure 2-4. Example of pedestrian travel patterns by facility type and day. used to identify peak-hour patterns. For example, Figure 2-4 illustrates hourly volumes as a percentage of daily volume for two facility types (off-street paths and pedestrian districts) and two time periods (weekdays and weekends), based on counts taken between April and Septem- ber. According to the project, this figure is expected to be “generally accurate for pathways and sidewalks in areas with moderate climates, relatively high visitor trips, and mixes of land uses (residential and commercial)” (Jones et al. 2010). Mid-Ohio Regional Planning Commission, Columbus Region The Mid-Ohio Regional Planning Commission (MORPC) has conducted pedestrian and bicycle counts on off-street trails since about 2002. The counts document change in usage over time, which helps inform evaluations on whether to widen selected trails. In addition, MORPC has used the counts to assist with grant applications, provide information to elected officials, and support or justify budget decisions. In 2012, MORPC produced a Trail Count Report as part of an effort to better understand how trail usage is influenced by various factors, including temperature and precipitation. Figure 2-5 illustrates the percentage of average daily trail usage by temperature on portions of one trail. These data helped the commission draw conclusions about which portions of the trail are most affected by weather, and thus which trail users are likely to be more or less reliant on biking or walking. 2.4 Safety Analysis 2.4.1 Potential Applications Non-motorized counts can be used to inform a safety analysis of a facility or area and bet- ter evaluate crash data. Pedestrian and bicycle volumes may be used to quantify exposure and develop crash rates and to identify the before-and-after safety effects of upgrading a facility. Volumes may also be used to estimate pedestrian or bicycle miles traveled for use as a regional exposure metric.

16 Guidebook on Pedestrian and Bicycle Volume Data Collection Quantifying Exposure Exposure relates to the frequency of a bicyclist or pedestrian being present in a conflict zone with the potential to be involved in a crash and is used in assessing risk. One of the biggest chal- lenges in pedestrian and bicycle crash data evaluation is evaluating the number of crashes at a location without knowing the volume of pedestrians or bicycles at those locations. Crashes are often disproportionately high on suburban arterials, where there are few pedestrians, compared to downtowns, where there are more pedestrians. However, an analysis based only on the total number of crashes may not reveal this disparity. Various methods have been proposed to measure an area’s pedestrian and bicyclist exposure, considering such variables as population; volumes of pedestrians, bicyclists, and vehicles; and distance traveled (Molino et al. 2012). Some studies have investigated the potential for measuring pedestrian exposure based on the number of pedestrians observed in the roadway. One of the simplest models of assessing pedestrian exposure defines relative risk as the num- ber of annual pedestrian–vehicle collisions divided by the average annual pedestrian volume. This technique was pioneered by the City of Oakland, which applied the method in its first pedestrian master plan adopted in 2002. As noted in the plan, FHWA and the National Highway Traffic Safety Administration (NHTSA) have identified “pedestrian exposure data as the least understood and most important area of research for pedestrian planners and decision-makers” (City of Oakland 2002). The plan used model-generated pedestrian volumes and crash data to identify the city’s most dangerous intersections; however, actual count data would be preferred to modeled volumes when available. Most research has focused on collisions between vehicles and pedestrians or bicycles; agencies should also consider collisions between pedestrians and bicycles, collisions between two or more Source: Mid-Ohio Regional Planning Commission (2012). Figure 2-5. Example evaluation of temperature effects on trail usage.

Non-Motorized Count Data Applications 17 bicycles, and accidents involving only a bicycle. Although these types of crashes are typically not fatal, they can still cause injury and damage. Identifying Before-and-After Safety Effects Non-motorized count volumes may be used to evaluate pedestrian and bicycle crashes and develop rates relative to the volume of users. These data can be used in crash prediction models to estimate the before-and-after safety effects of various safety treatments. The Highway Safety Manual (HSM) presents crash and analysis methods for quantitatively assessing crash frequency or severity and estimating the effect of countermeasures (AASHTO 2010). Section 3 of the HSM includes a discussion of roadway treatments for pedestrians and bicyclists, although crash modification factors (CMFs) were not available at the time of publication to quantitatively assess the effect of these treat- ments. FHWA hosts an online CMF clearinghouse, which provides a regularly updated repository of CMFs and includes research and data relevant to bicycle and pedestrian treatments. 2.4.2 Example Application City of Montreal, Quebec Strauss, Miranda-Moreno, and Morency (2014) describe the use of manual pedestrian and bicycle count data for 647 signalized and 435 unsignalized intersections in Montreal for evaluating pedestrian and bicycle safety. Annual bicycle, pedestrian, and motor vehicle volumes for each inter- section were developed from 8-hour counts conducted by the City of Montreal on weekdays dur- ing warmer weather months in 2008 and 2009, using adjustment factors developed from the city’s permanent count stations. A similar process was used to develop annual volumes by travel mode for the unsignalized intersections, but starting from 1-hour counts taken in summer and fall 2012. Injury crash data by travel mode were also available for all of the intersections over a 6-year period. Based on these data, the researchers developed models estimating the change in injuries by mode that would be expected with changes in either intersection demand (e.g., motor vehicle right-turning volume) or intersection characteristics (e.g., total crosswalk width, bus stop pres- ence, all-red traffic signal phase provided). For example, bicyclist injuries would be expected to increase by 10% for every 2.4% or 1.85% increase in right-turning and left-turning volumes, respectively, at signalized intersections. Pedestrian injuries would be expected to increase by 10% for every 3.2% or 4.2% increase in motor vehicle volumes at signalized or unsignalized intersections, respectively. 2.5 Project Prioritization 2.5.1 Potential Applications Agencies may use multimodal counts to help identify high-priority locations for improve- ments. Existing pedestrian and bicycle counts may help define essential multimodal networks where projects should be a priority. In addition, an agency may use historic counts to identify which factors most influence rates of walking and bicycling and prioritize projects accordingly. Counts that measure improper user behaviors (i.e., wrong-way bike riding) can help indicate areas with deficiencies where improvements may be needed. 2.5.2 Example Application San Francisco Municipal Transportation Agency The San Francisco Municipal Transportation Agency (SFMTA) has collected annual citywide bicycle counts since 2006. SFMTA started following the NBPD’s guidelines for collecting manual

18 Guidebook on Pedestrian and Bicycle Volume Data Collection counts in 2011 in order to improve accuracy and create a comparable data set. In addition, SFMTA is installing permanent automated bicycle counters at key locations in the bicycle net- work. The automatic counters collect volume data over a 24-hour period to provide a more complete picture of ridership patterns. SFMTA uses the data to evaluate the usage of the bicycle network and to “help identify locations where additional infrastructure improvements may be needed” (SFMTA 2011). At some locations, SFMTA collects data manually on the rate of wrong-way and sidewalk riding. These data identify locations where facilities may be inadequate or unsafe, so that SFMTA can improve conditions in these areas. Although 94% of riders were observed obeying the law, 6% of cyclists were observed riding on the sidewalk and/or in the wrong direction. Figure 2-6 graphs locations with the highest rates of improper riding. As seen in the figure, the report con- cludes that where higher rates of improper riding occur, bicyclists are concerned for their safety due to higher speeds, more car lanes, and fewer bicyclist facilities. Source: San Francisco Municipal Transportation Agency (2011). Figure 2-6. Use of manual counts to evaluate unsafe bicyclist behaviors.

Non-Motorized Count Data Applications 19 2.6 Multimodal Model Development 2.6.1 Potential Applications Although many jurisdictions have developed vehicle travel demand models to forecast future motorized vehicle volumes, relatively few areas have undertaken comparable efforts to assess pedestrian and bicycle demand. Multimodal travel demand modeling is an emerging field which has the potential to estimate pedestrian and bicycle demand over a large transportation network. Non-motorized counts are a key element in calibrating a multimodal model. Once developed and calibrated, such a model could be used to • Estimate multimodal demand over a large network with limited new data collection, • Estimate the influence of infrastructure changes (i.e., the addition of a new bike facility) on travel behaviors, and • Project future multimodal demand. Source: Alta Planning + Design (2010). Figure 2-7. Example output from a pedestrian model calibrated with count data.

20 Guidebook on Pedestrian and Bicycle Volume Data Collection 2.6.2 Example Application City of Berkeley, California The City of Berkeley maintains a pedestrian demand model. The model was developed based on an assessment of “spatial accessibility,” urban form, land use, and pedestrian observations. Pedestrian count volumes from 64 locations throughout the city were used to assess the model’s significance and validity. The comparison showed that the model forecasts approach 70% accu- racy compared to the observed counts. Most pedestrian movement in the city is explained by average daily traffic, distance from the Central Business District (CBD), and the relative acces- sibility of a junction. Although the model can predict intersection volumes, pedestrian volume estimates were assigned to street segments, as shown in Figure 2-7. As stated in Berkeley’s Pedestrian Master Plan, the model can be used to identify key areas of pedestrian activity and to “prioritize improvement options to target opportunities where streets are being used the most” (Alta Planning + Design 2010).