- Email: [email protected]
Measuring Bicycling Within the Community
CHAPTE R 3
Measuring Bicycling Within the Community Introduction In recent years, active transportation (AT; i.e., walking and bicycling) has become recognized more frequently as a fundamental mode of transportation. In support of this recognition, domestic and foreign agencies alike have placed a strong emphasis on the promotion of AT and bicycling. In 2010, the United States Department of Transportation issued a policy statement calling for the collection of more data on AT modes.1 Likewise, to promote AT, research initiatives, funding, educational/awareness campaigns, intervention, policy statements, government mandates, and updates to urban planning and community design have been spearheaded. Keeping up with these trends, within the last decade, techniques and technologies to measure and monitor AT have advanced tremendously. Although the majority of this book focuses pointedly on bicycling behavior, it is important to note the major literature gaps on measuring bicycling behavior separate from AT. As depicted in Fig. 3.1, there has been a major upswing in research surrounding AT. Still, the bulk of this literature merges bicycling with other forms of AT making it difficult to isolate bikespecific analyses. With that in mind, this chapter is intended to discuss some of the traditional and modern techniques to measure AT both narrowly and broadly. In most cases, these measurement tactics can be applied to bicycling behavior specifically. This chapter is broken down into subjective measurement techniques and objective measurement techniques. As you read through the chapter you will come to understand that both techniques have their associated advantages and disadvantages. These should be weighed carefully when deciding what measurement technique to implement for your use. Keep in mind that a combination of techniques may be the most appropriate for you depending on the goals of your measurement.
Objective Objective techniques include two overarching categories: short-term (i.e., manual and automatic), and permanent. These objective techniques are useful for collecting information to (1) examine trends in how biking changes over time, (2) to learn more about where people bike, and (3) a combination of the two. How you collect data is dependent upon the question you are trying to answer. That is, if examining the temporal component, care should be taken to collect continuous results. If examining the geographical component, more emphasis Bicycling for Transportation http://dx.doi.org/10.1016/B978-0-12-812642-4.00003-9
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Copyright © 2018 Elsevier Inc. All rights reserved.
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Figure 3.1: Research Publications in Active Transportation (1996–2016).
should be placed on the location of your results collection. Be aware of the type of question you may be trying to answer when reading in more detail on each objective technique below.
Placing Counters Traffic counting is the process of counting the physical number of cyclists who ride through a particular area and has been proven to be a useful technique for measuring bicycle behavior. The Federal Highway Administration (FHWA) developed a traffic monitoring guide (TMG) flowchart summarizing the type of traffic counter that can be used for collecting AT data.2 This flowchart is displayed in Fig. 3.2 and reflects the technologies that are possible to use to take bicycle and pedestrian counts and the relative costs associated with each. This chapter briefly touches on a few of the techniques. The FHWA’s TMG provides useful recommendations on placing traffic counters. Specifically, the TMG indicates that counting sites should be chosen to evaluate changes in behavior over time, before and after a construction project, and/or to understand the general bike traffic across an area. The number of sites is completely dependent on cost and the goals of the particular jurisdiction placing the counters. Specifically, for permanent counters, the CDOT recommends seven permanent counters per factor group for each location.3 The National Bicycle and Pedestrian Documentation Project (NBPDP) recommends collecting two hour counts,4 while Nordback et al. recommend a seven day count,5 and the Swedish National Road and Transport Research Institute recommends a minimum of two weeks.6 Given that the recommendations vary so broadly, it is crucial that the needs of the user and their associated jurisdiction are taken into account when making decisions on the length of the count. For additional resources on where, how many, and for how long counters should be placed, please refer to Box 3.1.
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Bike Counter in Downtown Boulder, CO.
Short Term Standardized Protocols The NBPDP is conceivably the most well-known method for AT data collection in the United States. This project was created by Alta Planning + Design and the Institute of Transportation
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Figure 3.2: Flowchart for Selecting Count Equipment for Measuring Active Transportation. Federal Highway Administration. Traffic monitoring guide. 2012.
BOX 3.1 Select Traffic Monitoring Organizations and Resources 1. Organizations • The Federal Highway Administration, www.fhwa.dot.gov • The Colorado Department of Transportation, www.codot.gov • The North Central Texas Council of Governments, www.nctcog.org • The Finland Transport Agency, www.liikennevirasto.fi/web/en • The Swedish National Road and Transport Research Institute (VTI), www.vti.se/en • The Delaware Valley Regional Planning Commission (DVRPC), www.dvrpc.org 2. Resources • The Colorado Mile Markers: Recommendations for Measuring Active Transportation7 • The Strategic Plan for Nonmotorized Traffic Monitoring in Colorado8 • The Minnesota Bicycle and Pedestrian Counting Initiative: Methodologies for Nonmotorized Traffic Monitoring9
Measuring Bicycling Within the Community 49 Engineers (ITE) Pedestrian and Bicycle Council and was the first project to standardize a manual counting protocol to allow for comparison across jurisdictions. The goals of the project are to (1) establish a consistent national bicycle and pedestrian count and survey methodology, (2) establish a national database of AT count information generated, and (3) use the data to analyze relationships between demographics, climate, land use, and AT activity.4 Directions for completion of the count process instruct the user to complete counts for two hours in 15 minute increments counting bicyclists on riding in the street, on the sidewalk, and through intersections. Instructions indicate that the number of people on the bicycle should be counted rather than the number of bicycles. Additional to the count form, the NBPDP includes a standard bicycle survey that allows the user to ask questions of bicyclists relative to: home zip code, trip purpose, frequency of riding, riding season, and total trip length, among others.10 This survey is presented in Box 3.2. Developers of this project created adjustment factors that convert these manual counts to a figure comparable to the average annual daily traffic figure used in vehicular traffic analyses. The NBPDP, however, still has limitations. First, manually counting is time intensive and requires access to a large staff and/or many volunteers. Second, the results collected by this study (and bike behavior itself) are vulnerable to seasonal changes and weather variations.
Environmental Audits In addition to collecting traffic counts, it may also be useful to collect supplementary information about the physical environment to gain a better understanding of your counts. This knowledge can inform future policy development, program planning, and community design. A number of environmental audit tools exist that measure both the built environment and the social environment for physical activity, including biking. The Wisconsin Assessment of the Social and Built Environment (WASABE) is one such tool.11 The WASABE focuses on the assessment five major domains. First, the WASABE examines neighborhood characteristics, which include aspects of the physical/built environment (i.e., aesthetics, presence of amenities like shade, street art, signage, etc.). Second, the transportation environment is assessed. This is potentially the most important domain for measuring bicycling behavior and focuses on features of the AT-supportive built environment including traffic volume, street type, presence of sidewalks and bike lanes, and the presence of public transit. Third, factors that relate to accessibility and availability of nearby facilities are examined for the destinations/land use domain. Fourth, the social environment is surveyed for factors concerning neighborhood social capital, the presence of a protective social community, crime safety, etc. Finally, connectivity is examined for features relating to the directness of travel routes, and the presence of sidewalks/bike lanes/crosswalks. Each of these five domains is a crucial component when completing a comprehensive measurement of
50 Chapter 3 BOX 3.2 Adapted National Bicycle and Pedestrian Documentation Project Standard Bicycle Survey10
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cycling activity within a jurisdiction. However, as the WASABE is relatively new, limitations include a poor understanding of the predictive validity and intrarater reliability of this tool.11
Manual Counting With Technology Many paper-based protocols, including the NBPDP, can incorporate technology using smartphones or tablets. However, methodologies exist that were specifically designed to be used with technology. For example, smartphone apps like CounterPoint are intended to replace manual counters. The Global Positioning System (GPS) enabled-app allows the user to track the number of people going via multiple forms of transportation, including biking.12 The app functions similar to that of manual counting in that when traffic passes in front of the researcher or planner they can easily select the rider’s mode of transportation. The user can count traffic at a predesignated location deemed a “counterpoint” or select their own location to count. The app also provides the option of selecting features including: time you will be counting, gender, adult cyclist versus passenger, child cyclist versus passenger, planned events, weather, road construction, closures, etc. A screenshot of one of the pages on the app is presented below. Undoubtedly there exist other apps with similar features, making it easier to track and measure cyclists. However, being that CounterPoint and similar apps are collecting data over the short-term they are subject to variations in traffic flow including weather, time of day, and day of week. Thus, depending on the goal of your project these short-term counters may be inappropriate for your needs.
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Screenshot of CounterPoint App. CounterPoint
Portable Counters To combat the issues associated with manual counting (i.e., traffic flow variations due to weather, time of day, and day of week), automatic portable counters provide a good alternative. Pneumatic tubes and infrared sensors are commonly used portable techniques for counting bicyclists. These options provide a balance of flexibility to move them around and relative low cost for the amount of data collected. Pneumatic tubes may even provide the flexibility of counting bicyclists and motorists simultaneously.13 Accuracy of these counters varies, most often dependent on the environmental condition they are counting in and the device make, model, and software. On the whole, these automatic counters tend to provide comparable counts to manual counting with a slight tendency toward undercounting.13 For example, pneumatic tubes may not count lightweight riders (e.g., children) and may accidentally count motorized scooters and certain cars. Infrared sensors produce more problems in pedestrian counting, however, still work best on restricted width paths.14,15
Geographic Information Systems Geographic Information Systems (GIS) have been used for years to estimate travel routes and allow for the examination of traffic volumes, street connectivity, and the presence of
Measuring Bicycling Within the Community 53 bicycle and pedestrian infrastructure. Before GIS technology, mapping routes was conducted largely via manual mapping and self-reports of the environment. However, GIS often uses a “shortest street network” analysis that does not always reflect the actual route. GIS also does not consider route choice and thus may not be as useful as other methods for measuring bicycling.16
Global Positioning Systems GPS devices have become more popular for tracking AT behavior as they have become more familiar, discreet, and relatively inexpensive. GPS monitoring, like GIS, is objective and unobtrusive, but also offers a relatively accurate, passive, and low cost method to assess actual movement patterns of cyclists throughout their environment. This is of particular importance for measuring AT, including biking, because it allows for measurement of long-term trips and the recording of natural travel data.17 Accuracy for walking trips specifically was shown to be higher when trips were longer than 2 min and further than 30 m distance.18 GPS devices estimate a rider’s location on earth by triangulating their position using a minimum of four satellites to estimate position and elevation. Thus, GPS is limited to assessing movement in outdoor areas with a clear line of sight to the sky. Signal blockage by major buildings, mountains, trees, and so on vastly limits the accuracy of the receivers causing potential limitations in the use of GPS in assessing bicycling.19 This may cause the route to appear to bounce around the actual route reducing the spatial accuracy in the type of units required for measuring AT. Limitations aside, GPS can be very useful for measuring AT given the combination of recording actual routes and the specific times during which the behavior occurred. GPS is also great for showing routes taken along the trip, accounting for route choice unlike GIS. This information is often missing from other measurement techniques; thus, GPS is valuable for measuring travel volumes on a specific route and understanding behavioral aspects of route choice.13 As technology continues to advance, the quality of GPS devices has only improved making the use of GPS for measuring AT more accessible. GPS-enabled smartphones in particular are a promising new method that can play a major role in this measurement process. Moreover, there exist GPS apps for tracking pedestrian and bicycle trips. CycleTracks is a smartphone app developed by San Francisco County Transportation Authority to track AT trips.20 A case study of CycleTracks concluded that the app was a viable and inexpensive method of gathering data but requires ample data preprocessing.21,22
Bluetooth Static Bluetooth monitoring has been used to count vehicles for years and has recently been adopted to count pedestrians23 and could also be used to count cyclists. Bluetooth devices
54 Chapter 3 have unique identifying code that allow for monitoring travel and route using multiple monitoring points/checkpoints. This is great for locations with big crowds where other more traditional counters may fail to function properly.13 A potential limitation for this method is that not everyone has a Bluetooth-enabled device and thus margins of errors may be high. However, Bluetooth-enabled smartphones are making this much easier.
Webcams Using video-based sensors like webcams has recently been used to capture activity and estimate AT. This technique is both unobtrusive and relatively inexpensive. For example, Hipp et al. evaluated images from public webcams using crowdsourcing via Amazon Mechanical Turk.24 All areas may not have webcams and thus may not always be an option. Moreover, this technique requires visibility (e.g., lighting) and thus can only be used for counting daylight hours or very well-lit locations at night.
Crowdsourcing Given that spatial data related to biking is often limited, crowdsourced data can be useful for measuring rates of biking. This can primarily be used to be understand bike data as it relates to demographics, geography, physical infrastructure, personal travel experiences, and community priorities to name a few. Benefits of crowdsourcing include improved response rates, community engagement, minimization of time and location constraints, and integration into spatial analysis. Furthermore, crowdsourcing may be less expensive than other measurement methods. Crowdsourcing can be accomplished using tools like Strava25 and CycleTracks,20 as well as many others. Strava is a smartphone app and website originally developed to connect riders from around the world. The app functions in such a way that when the user is active, Strava uses his/her phone or GPS device to track his/her activities and then shares it with their friends. The app also provides user with performance metrics like speed, pace, distance, and performance compared to past attempts.25 The information from these apps can be harnessed to evaluate a cyclist’s routes, time spent riding, and attitudes about their ride. All of this information combined is very valuable to researchers and community employees interested measuring cycling.
Automated Video Image Processing An alternative to evaluating images from webcams involves analyzing stored video. A computer is able to process video automatically by isolating the moving portion of the image and provide traffic counts. A technique called computer vision allows for real-time processing of video and has been used to perform pedestrian counts.26,27 This technique may easily be extended to bicycle counts28 and to measure AT at complex intersections (e.g., roundabouts).29
Measuring Bicycling Within the Community 55 Automated Video Image Processing has the same limitations as other video methods including webcams (i.e., lighting). Accuracy for this technique for conducting pedestrian counts is about 15% off from other automated methods.13
Bikeshare Systems Chapter 6 discusses in detail how bike shares can be used to promote and encourage safe riding. That said, shared bicycle systems are expanding rapidly across the United States and thus provide a great opportunity for measuring cycling. Bicyclists tend to only keep a bike rented from a bike share for short-term use.13 As part of a bikeshare system, bicyclists start and end at docking station. Each bike has a unique ID number, which is associated with the information of the individual who rented the bike. This data can be harnessed to get some idea of the route that was traveled, the length of time that they rode, and to conduct volume analyses. With these data, it is possible to make observations on how usage varies across times and days. As an example, Pittsburgh collects data on the number of users that rent a bike from a particular bikeshare hub within their city. Below is a heat map of Pittsburgh bikeshare users in mid-September 2015. Using such information, researchers have examined London’s bikeshare system and determined that peak weekday rides occur in the early morning. Alternatively, researchers have noted that in Washington D.C.’s bikeshare system, peek weekday riding occurs in the afternoon—these results were similar to all results for the weekend.30
Heat Map of Pittsburgh’s bikeshare Rentals, September 7 Through September 8, 2015. Healthy Ride
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Permanent Count techniques like manual count surveys, counting apps, and automatic counters are of great use for short-term analyses; however, it is time-intensive, labor-intensive, and expensive to obtain long-term continuous counts using such techniques. The short-term nature of these count methods make it impossible to obtain long-term changes across weather types, seasons, years, and decades. As an alternative, it is possible to install permanent counters to track changes over long periods of time. These permanent counters afford the opportunity to make generalizations on who is using the facilities, the major trip purposes of the facility are (i.e., are cyclists using the street/trail/path mainly for commuting, leisure, etc.), and estimation of miles traveled.13 The continuous data provided by permanent counters can provide information to develop adjustment factors for seasonal and weather variations like rain, snow, and temperature. The most commonly used permanent counter is the induction loop. This technology is commonly used for detecting traffic at traffic signals, and can be configured in such a way that senses metals in a bicycle that passes over the detector. Induction loops have been shown to accurately differentiate bicycles from motor vehicles; however, depending on the path these detectors may undercount or overcount.31
Subjective Subjective techniques including national surveillance, targeted surveying, multimodal surveying, interception, and bikeshare surveying have the potential to provide the similar information to objective techniques. However, this information may be tainted by various types of bias. Participants may be unwilling to provide personal details and/or inaccurately recall information. The data is also subject to social desirability bias, by which individuals select the response they believe they should select. Despite potential biases, subjective techniques can help dive a little deeper into the reasons why someone is biking. This knowledge is invaluable as it can be crucial in informing future interventions, campaigns, and updates to the bike-supportive built environment.
National Surveillance Two major national surveys within the United States provide information on the mode in which Americans travel: the American Community Survey (ACS) and the National Household Transportation Survey (NHTS). Both of these surveys examine mode of transportation to work and isolate bicycling as a unique category rather than observing AT as a whole. These population-based surveys are arguably the most comprehensive method of quantifying mode of transportation for a large number of people across an entire geographic area. Other countries conduct national surveillance as well that, in some ways, overlap with the ACS and NHTS.
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American Community Survey The ACS is a nationwide survey conducted by the US Census Bureau.32 Data is provided in 1-, 3-, and 5-year estimates. The majority of the survey asks questions relative to income, disability, employment, and housing characteristics; however, the ACS also contains the question: “How did this person usually get to work last week?”. This question allows the participant to select from a variety of options, including biking, the method with which they feel they traveled most of their distance of their commute. The ACS is the broadest and most accessible data on bicycling commuting in the United States and is a great resource for garnering a general sense of bicycling trends across the country. In combination with other tools, like the Census Transportation Planning Package, the ACS is particularly useful for understanding bicycling to work in tandem with demographics data.13 While the ACS can provide a good overview of bicycling in the United States, it is not without limitations for analyzing bicycling. The first problem for analyzing bicycling data lies in the phrasing of the question. As mentioned above, the ACS only asks participants about their usual form of transportation for travel to work. This vastly limits the number of participants as it does not include results from individuals who (a) do not work, and/or (b) work from home, and (c) are disabled and unable to walk or bicycle. Second, the question restricts the participant to selecting their usual form of transportation to work. This prevents the inclusion of participants who may bike to work occasionally but principally use another mode of transportation or use mixed modes of transportation. Third, while recent versions of the ACS include this question it was only added to the survey in 2000. From 1960 through 2000 this question was only included in the decennial census. This detail should be accounted for when comparing historical data to present and future data.
National Household Transportation Survey In comparison to the ACS, the NHTS provides a relatively small sample of bicycling data. Starting in 2001, the NHTS began when the American Travel Survey was combined with the Nationwide Personal Transportation Survey. The NHTS is conducted every 5–7 years and was most recently conducted in 2016. Similar to the ACS, the NHTS asks a question on the mode with which the participant usually travels to work. Additionally, the NHTS is more comprehensive for bike-related transportation delving deeper into the frequency with which someone bikes, an individual’s abettors/barriers to bicycling, and any improvements that the respondent believes can be made to their bike-supportive environment. See Box 3.3 for a comprehensive list of NHTS bike-related questions. The primary limitation of the NHTS is that it represents a relatively small sample of US cyclists (about 150,000 households) making generalization to smaller localities difficult. However, when combined with local add-ons for sampling with a jurisdiction, the NHTS can provide fairly accurate results within a particular jurisdiction.
58 Chapter 3 BOX 3.3 National Household Travel Survey Bicycle-Related Questions33
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Surveys in Other Countries As mentioned, the United States is (understandably) not the only country that uses survey techniques to evaluate the frequency with which people ride in their country. Canada also measures commuting to work (e.g., mode of transportation, travel time), similar to the ACS, via the National Household Survey.34 The United Kingdom conducts a decennial census that was most recently conducted in 2011.35 Within this census, mode of travel to work is examined. Additionally, Australia utilizes the Household Income and Labour Dynamics in Australia (HILDA) survey.36 This longitudinal survey measures commuting to work. Many other countries use analogous methods to examine national rates of cycling within their country.
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Targeted Surveys National surveillance techniques like the ACS and NHTS provide great information at a large scale on bicycle travel. However, more targeted surveys are often more appropriate when wanting to take a deeper look at a particular demographic in a specific location.
Pedestrian and Bicyclist Travel Survey As the name suggests, the Pedestrian and Bicyclist Travel Survey (PABS) was developed to help quantify the amount of walking and bicycling that happens at a jurisdictional level. The PABS is a random-sample, mail-out questionnaire that collects data about AT behaviors and is meant to address the deficiencies of national surveys in measuring AT.37 This survey has validated and checked for statistical reliability, and thus, should produce comparable results across jurisdictions.
Multimodal Surveying A major limitation of many bicycle surveys is that they assume (a) individuals only commute via one mode of travel across all days and/or (b) individuals only commute via one mode of travel within a single trip. “Last-mile” connections (e.g., carpooling to a park-and-ride, walking to the train, biking from the bus stop, etc.) are great examples of multimodal trip information missed by traditional surveys.13 Clifton and Muhs reviewed the literature of approaches to examining multimodal travel behavior and provided suggestions on how to combat the issue.38 They recommend that walking trips over 150 ft be included in survey, an improvement in computer-assisted telephone interviews, and the use of more advanced technologies (e.g., GPS) to collect travel data. Further, surveys should be developed that focus on more than single modes.
Intercept Surveying A direct method of obtaining information on bicycling is through the use of an intercept survey. This method intercepts cyclists while on a specific route and then a brief interview or survey is conducted. As discussed in the beginning of this chapter, the Standard Bicycle Survey is a one-page survey provided by the NBPDP that may be useful for such interviews.10 If interviewing, the conversation should remain brief so as not to inconvenience the rider. Survey alternatives may include a mail-back or internet-based survey that the rider can respond to after their trip is completed. A limitation of this type of survey is that you can only interview people who are riding their bikes at that point in time and thus your results may not be generalizable.
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Bikeshare Surveying Additional to collecting data from bikeshare systems as discussed above, surveys of bikeshare systems may provide detailed information on the demographics of bike users as well as more detailed travel behavior information (e.g., impact of environmental factors, seasonal changes, etc.).
Summary It is important to understand that any survey method has its benefits and pitfalls dependent on the situation at hand. Surveys should always be tailored around the target population. Further, generalizing the results obtained from these targeted surveys should be done with caution. National surveillance like the NHTS and ACS capture broader trends and across the country that can be compared to previous nationally conducted surveys. However, while these surveys are broad their sample size is relatively limited, therefore, the results may not translate well to certain populations and jurisdictions. Targeted surveys provide an alternative to national surveillance. Options include, but are not limited to, the PABS, intercept surveys of cyclists on a particular route, and bikeshare surveys. Conducting surveys can be expensive, time consuming, and inconvenient and are consequently not necessarily the perfect solution. However, they do help eliminate the multimodal travel gaps seen in national surveys and may be more appropriate at a local level. Still, surveys are subjective and subject to various biases that, to an extent, can be avoided using more objective measures.
Case study 3.1 Evaluating green shared lanes—Minneapolis, Minnesota As mentioned in the chapter, it is sometimes very necessary for communities to utilize multiple evaluation techniques to measure cycling within their community. Minneapolis, Minnesota wanted to evaluate the impact of the share bus-bike-right turn lane on Hennepin Avenue between Washington Avenue and 12th street was enhanced to include-green shared lane markings. To conduct this analysis, they identified seven specific research questions examined and parameters used to evaluate each particular research question. As you will see, in their evaluation process they made sure to use a variety of techniques that aligned with the research question at hand. Each are presented below. The bullets underneath each research question indicates the measurement technique used for evaluation. 1. Are bicyclists using the green lane and where are they riding? ○ Bicyclist riding position using video analysis
62 Chapter 3 2. Are motor vehicles and buses driving in the green lane and where are they driving? ○ Motor vehicle and bus driving position using video analysis 3. Are motorists and buses given a safe passing distance (>3 ft) when overtaking bicyclists? ○ Road user driving or riding position using video analysis 4. Does driving or riding behavior vary by lane width? ○ Road user driving or riding position using video analysis 5. Have bicyclist volumes on Hennepin Avenue changed with the addition of green lanes? ○ Daily bicyclist volumes using manual bicyclist counts 6. Has the safety of bicyclists improved with the addition of the green lanes? ○ Bicyclist-motorist crash rates using MN DPS accident reports 7. How do road users comprehend and perceive the green lanes? Does this differ from actual driving or riding behavior? ○ Survey of downtown travelers
As is seen here, in Minneapolis, Minnesota various analysis techniques were used including video analysis, manual bicyclist counts, examination of accident reports, and a survey of travelers. This provided the Minneapolis government with a much more comprehensive look at cycling within their community and how it changed on Hennepin Avenue.
References 1. LaHood R. United States Department of Transportation policy statement on bicycle and pedestrian accommodation regulations and recommendations. Signed on March 2010;11. 2. Federal Highway Administration. Traffic monitoring guide. 2012. 3. Nordback K, Marshall WE, Janson BN. Development of estimation methodology for bicycle and pedestrian volumes based on existing counts. 4. Birk M, Jones MG, Cheng AM. National bicycle and pedestrian documentation project, In: Paper presented at Transportation Research Board 85th Annual Meeting; 2006. 5. Nordback K, Marshall W, Janson B, Stolz E. Estimating annual average daily bicyclists: error and accuracy. Transp Res Rec 2013;2339:90–7. 6. Niska A, Nilsson A, Varedian M, Eriksson J, Söderström L. Evaluating pedestrian and cycle traffic. Development of a harmonised method for monitoring the municipal proportion of pedestrian and cycle traffic through travel surveys and cycle counts. Linkoping, Sweden: VTI; 2012. 7. Krizek K, Forsyth A. The Colorado mile markers: recommendations for measuring active transportation. Kaiser Permanente; 2012. 8. Turner S, Qu T, Lasley P. Strategic plan for non-motorized traffic monitoring in Colorado. Texas Transportation Institute; 2012.
Measuring Bicycling Within the Community 63 9. Lindsey G. The Minnesota bicycle and pedestrian counting initiative: methodologies for non-motorized traffic monitoring. Minnesota Department of Transportation; 2013. 10. National Bicycle and Pedestrian Documentation Project. http://bikepeddocumentation.org/. 11. Malecki KC, Engelman CD, Peppard PE, et al. The Wisconsin Assessment of the Social and Built Environment (WASABE): a multi-dimensional objective audit instrument for examining neighborhood effects on health. BMC Public Health 2014;14(1):1165. 12. CounterPoint App. www.counterpointapp.org. 13. Griffin G, Nordback K, Götschi T, Stolz E, Kothuri S. Monitoring bicyclist and pedestrian travel and behavior: current research and practice. Washington, DC: Transportation Research Board; 2014. 14. Greene-Roesel R, Diogenes MC, Ragland DR, Lindau LA. Effectiveness of a commercially available automated pedestrian counting device in urban environments: comparison with manual counts. Safe Transportation Research & Education Center; 2008. 15. Turner S, Lasley P. Quality counts for pedestrians and bicyclists: quality assurance procedures for nonmotorized traffic count data. Transp Res Rec 2013;2339:57–67. 16. Duncan MJ, Mummery WK. GIS or GPS? A comparison of two methods for assessing route taken during active transport. AJPM 2007;33(1):51–3. 17. Bricka S, Zmud J, Wolf J, Freedman J. Household travel surveys with GPS: an experiment. Transp Res Rec 2009;2105:51–6. 18. Cho G-H, Rodriguez DA, Evenson KR. Identifying walking trips using GPS data. MSSE 2011;43(2): 365–72. 19. Wolf J, Hallmark S, Oliveira M, Guensler R, Sarasua W. Accuracy issues with route choice data collection by using global positioning system. Transp Res Rec 1999;1660:66–74. 20. San Francisco County Transportation Authority. CycleTracks. Available from: http://www.sfcta.org/modelingand-travel-forecasting/cycletracks-iphone-and-android. 21. Hudson JG, Duthie JC, Rathod YK, Larsen KA, Meyer JL. Using smartphones to collect bicycle travel data in Texas. College Station: University Transportation Center for Mobility; 2012. 22. Broach J, Dill J, Gliebe J. Where do cyclists ride? A route choice model developed with revealed preference GPS data. Transport Res A-Pol 2012;46(10):1730–40. 23. Malinovskiy Y, Saunier N, Wang Y. Analysis of pedestrian travel with static bluetooth sensors. Transp Res Rec 2012;2299:137–49. 24. Hipp J, Adlakha D, Eyler AA, Chang B, Pless R. Emerging technologies: webcams and crowd-sourcing to identify active transportation. Brown School Faculty Publications; 2013 Paper 3.. 25. Strava. www.strava.com. 26. Svensson Å, Laureshyn A, Jonsson T, Ardö H, Persson A. . Collection of micro-level safety and efficiency indicators with automated video analysis; 2011Paper presented at 3rd International Conference on Road Safety and Simulation, Indianapolis, IN. . 27. Charreyron S, Jackson S, Miranda-Moreno LF. . Towards a flexible system for pedestrian data collection using Microsoft Kinect motion sensing device; 2013Paper presented at 92nd Annual Meeting of the Transportation Research Board, Washington, DC. . 28. Zaki M, Sayed T, Cheung A. Computer vision techniques for the automated collection of cyclist data. Transp Res Rec 2013;2387:10–9. 29. Zangenehpour S, Miranda-Moreno LF, Saunier N. . Automated classification in traffic video at intersections with heavy pedestrian and bicycle traffic; 2014Paper presented at Transportation Research Board 93rd Annual Meeting. . 30. Austwick MZ, O’Brien O, Strano E, Viana M. The structure of spatial networks and communities in bicycle sharing systems. PloS One 2013;8(9):e74685. 31. Nordback K, Piatkowski DP, Janson BN, Marshall WE, Krizek KJ, Main DS. Using inductive loops to count bicycles in mixed traffic. ITE J 2011;. 32. United States Census Bureau. American Community Survey information guide. 2010. 33. U.S. Department of Transportation, Federal Highway Administration. 2009 National household travel survey; 2009. http://nhts.ornl.gov/.
64 Chapter 3 34. Statistics Canada. National household survey; 2016. Available from: http://www12.statcan.gc.ca/nhsenm/2011/as-sa/99-012-x/99-012-x2011003_1-eng.cfm. 35. Office for National Statistics. 2011 census analysis—method of travel to England and Wales Report. 2013. 36. Melbourne Institute. Household income and labour dynamics in Australia; 2017. Available from zhttp://melbourneinstitute.unimelb.edu.au/hilda. 37. Forsyth A, Agrawal A, Krizek K. Simple, inexpensive approach to sampling for pedestrian and bicycle surveys: approach developed in pedestrian and bicycling survey. Transp Res Rec 2012;2299:22–30. 38. Clifton K, Muhs C. Capturing and representing multimodal trips in travel surveys: review of the practice. Transp Res Rec 2012;2285:74–83.
Measuring Bicycling Within the Community Introduction In recent years, active transportation (AT; i.e., walking and bicycling) has become recognized more frequently as a fundamental mode of transportation. In support of this recognition, domestic and foreign agencies alike have placed a strong emphasis on the promotion of AT and bicycling. In 2010, the United States Department of Transportation issued a policy statement calling for the collection of more data on AT modes.1 Likewise, to promote AT, research initiatives, funding, educational/awareness campaigns, intervention, policy statements, government mandates, and updates to urban planning and community design have been spearheaded. Keeping up with these trends, within the last decade, techniques and technologies to measure and monitor AT have advanced tremendously. Although the majority of this book focuses pointedly on bicycling behavior, it is important to note the major literature gaps on measuring bicycling behavior separate from AT. As depicted in Fig. 3.1, there has been a major upswing in research surrounding AT. Still, the bulk of this literature merges bicycling with other forms of AT making it difficult to isolate bikespecific analyses. With that in mind, this chapter is intended to discuss some of the traditional and modern techniques to measure AT both narrowly and broadly. In most cases, these measurement tactics can be applied to bicycling behavior specifically. This chapter is broken down into subjective measurement techniques and objective measurement techniques. As you read through the chapter you will come to understand that both techniques have their associated advantages and disadvantages. These should be weighed carefully when deciding what measurement technique to implement for your use. Keep in mind that a combination of techniques may be the most appropriate for you depending on the goals of your measurement.
Objective Objective techniques include two overarching categories: short-term (i.e., manual and automatic), and permanent. These objective techniques are useful for collecting information to (1) examine trends in how biking changes over time, (2) to learn more about where people bike, and (3) a combination of the two. How you collect data is dependent upon the question you are trying to answer. That is, if examining the temporal component, care should be taken to collect continuous results. If examining the geographical component, more emphasis Bicycling for Transportation http://dx.doi.org/10.1016/B978-0-12-812642-4.00003-9
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Copyright © 2018 Elsevier Inc. All rights reserved.
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Figure 3.1: Research Publications in Active Transportation (1996–2016).
should be placed on the location of your results collection. Be aware of the type of question you may be trying to answer when reading in more detail on each objective technique below.
Placing Counters Traffic counting is the process of counting the physical number of cyclists who ride through a particular area and has been proven to be a useful technique for measuring bicycle behavior. The Federal Highway Administration (FHWA) developed a traffic monitoring guide (TMG) flowchart summarizing the type of traffic counter that can be used for collecting AT data.2 This flowchart is displayed in Fig. 3.2 and reflects the technologies that are possible to use to take bicycle and pedestrian counts and the relative costs associated with each. This chapter briefly touches on a few of the techniques. The FHWA’s TMG provides useful recommendations on placing traffic counters. Specifically, the TMG indicates that counting sites should be chosen to evaluate changes in behavior over time, before and after a construction project, and/or to understand the general bike traffic across an area. The number of sites is completely dependent on cost and the goals of the particular jurisdiction placing the counters. Specifically, for permanent counters, the CDOT recommends seven permanent counters per factor group for each location.3 The National Bicycle and Pedestrian Documentation Project (NBPDP) recommends collecting two hour counts,4 while Nordback et al. recommend a seven day count,5 and the Swedish National Road and Transport Research Institute recommends a minimum of two weeks.6 Given that the recommendations vary so broadly, it is crucial that the needs of the user and their associated jurisdiction are taken into account when making decisions on the length of the count. For additional resources on where, how many, and for how long counters should be placed, please refer to Box 3.1.
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Bike Counter in Downtown Boulder, CO.
Short Term Standardized Protocols The NBPDP is conceivably the most well-known method for AT data collection in the United States. This project was created by Alta Planning + Design and the Institute of Transportation
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Figure 3.2: Flowchart for Selecting Count Equipment for Measuring Active Transportation. Federal Highway Administration. Traffic monitoring guide. 2012.
BOX 3.1 Select Traffic Monitoring Organizations and Resources 1. Organizations • The Federal Highway Administration, www.fhwa.dot.gov • The Colorado Department of Transportation, www.codot.gov • The North Central Texas Council of Governments, www.nctcog.org • The Finland Transport Agency, www.liikennevirasto.fi/web/en • The Swedish National Road and Transport Research Institute (VTI), www.vti.se/en • The Delaware Valley Regional Planning Commission (DVRPC), www.dvrpc.org 2. Resources • The Colorado Mile Markers: Recommendations for Measuring Active Transportation7 • The Strategic Plan for Nonmotorized Traffic Monitoring in Colorado8 • The Minnesota Bicycle and Pedestrian Counting Initiative: Methodologies for Nonmotorized Traffic Monitoring9
Measuring Bicycling Within the Community 49 Engineers (ITE) Pedestrian and Bicycle Council and was the first project to standardize a manual counting protocol to allow for comparison across jurisdictions. The goals of the project are to (1) establish a consistent national bicycle and pedestrian count and survey methodology, (2) establish a national database of AT count information generated, and (3) use the data to analyze relationships between demographics, climate, land use, and AT activity.4 Directions for completion of the count process instruct the user to complete counts for two hours in 15 minute increments counting bicyclists on riding in the street, on the sidewalk, and through intersections. Instructions indicate that the number of people on the bicycle should be counted rather than the number of bicycles. Additional to the count form, the NBPDP includes a standard bicycle survey that allows the user to ask questions of bicyclists relative to: home zip code, trip purpose, frequency of riding, riding season, and total trip length, among others.10 This survey is presented in Box 3.2. Developers of this project created adjustment factors that convert these manual counts to a figure comparable to the average annual daily traffic figure used in vehicular traffic analyses. The NBPDP, however, still has limitations. First, manually counting is time intensive and requires access to a large staff and/or many volunteers. Second, the results collected by this study (and bike behavior itself) are vulnerable to seasonal changes and weather variations.
Environmental Audits In addition to collecting traffic counts, it may also be useful to collect supplementary information about the physical environment to gain a better understanding of your counts. This knowledge can inform future policy development, program planning, and community design. A number of environmental audit tools exist that measure both the built environment and the social environment for physical activity, including biking. The Wisconsin Assessment of the Social and Built Environment (WASABE) is one such tool.11 The WASABE focuses on the assessment five major domains. First, the WASABE examines neighborhood characteristics, which include aspects of the physical/built environment (i.e., aesthetics, presence of amenities like shade, street art, signage, etc.). Second, the transportation environment is assessed. This is potentially the most important domain for measuring bicycling behavior and focuses on features of the AT-supportive built environment including traffic volume, street type, presence of sidewalks and bike lanes, and the presence of public transit. Third, factors that relate to accessibility and availability of nearby facilities are examined for the destinations/land use domain. Fourth, the social environment is surveyed for factors concerning neighborhood social capital, the presence of a protective social community, crime safety, etc. Finally, connectivity is examined for features relating to the directness of travel routes, and the presence of sidewalks/bike lanes/crosswalks. Each of these five domains is a crucial component when completing a comprehensive measurement of
50 Chapter 3 BOX 3.2 Adapted National Bicycle and Pedestrian Documentation Project Standard Bicycle Survey10
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cycling activity within a jurisdiction. However, as the WASABE is relatively new, limitations include a poor understanding of the predictive validity and intrarater reliability of this tool.11
Manual Counting With Technology Many paper-based protocols, including the NBPDP, can incorporate technology using smartphones or tablets. However, methodologies exist that were specifically designed to be used with technology. For example, smartphone apps like CounterPoint are intended to replace manual counters. The Global Positioning System (GPS) enabled-app allows the user to track the number of people going via multiple forms of transportation, including biking.12 The app functions similar to that of manual counting in that when traffic passes in front of the researcher or planner they can easily select the rider’s mode of transportation. The user can count traffic at a predesignated location deemed a “counterpoint” or select their own location to count. The app also provides the option of selecting features including: time you will be counting, gender, adult cyclist versus passenger, child cyclist versus passenger, planned events, weather, road construction, closures, etc. A screenshot of one of the pages on the app is presented below. Undoubtedly there exist other apps with similar features, making it easier to track and measure cyclists. However, being that CounterPoint and similar apps are collecting data over the short-term they are subject to variations in traffic flow including weather, time of day, and day of week. Thus, depending on the goal of your project these short-term counters may be inappropriate for your needs.
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Screenshot of CounterPoint App. CounterPoint
Portable Counters To combat the issues associated with manual counting (i.e., traffic flow variations due to weather, time of day, and day of week), automatic portable counters provide a good alternative. Pneumatic tubes and infrared sensors are commonly used portable techniques for counting bicyclists. These options provide a balance of flexibility to move them around and relative low cost for the amount of data collected. Pneumatic tubes may even provide the flexibility of counting bicyclists and motorists simultaneously.13 Accuracy of these counters varies, most often dependent on the environmental condition they are counting in and the device make, model, and software. On the whole, these automatic counters tend to provide comparable counts to manual counting with a slight tendency toward undercounting.13 For example, pneumatic tubes may not count lightweight riders (e.g., children) and may accidentally count motorized scooters and certain cars. Infrared sensors produce more problems in pedestrian counting, however, still work best on restricted width paths.14,15
Geographic Information Systems Geographic Information Systems (GIS) have been used for years to estimate travel routes and allow for the examination of traffic volumes, street connectivity, and the presence of
Measuring Bicycling Within the Community 53 bicycle and pedestrian infrastructure. Before GIS technology, mapping routes was conducted largely via manual mapping and self-reports of the environment. However, GIS often uses a “shortest street network” analysis that does not always reflect the actual route. GIS also does not consider route choice and thus may not be as useful as other methods for measuring bicycling.16
Global Positioning Systems GPS devices have become more popular for tracking AT behavior as they have become more familiar, discreet, and relatively inexpensive. GPS monitoring, like GIS, is objective and unobtrusive, but also offers a relatively accurate, passive, and low cost method to assess actual movement patterns of cyclists throughout their environment. This is of particular importance for measuring AT, including biking, because it allows for measurement of long-term trips and the recording of natural travel data.17 Accuracy for walking trips specifically was shown to be higher when trips were longer than 2 min and further than 30 m distance.18 GPS devices estimate a rider’s location on earth by triangulating their position using a minimum of four satellites to estimate position and elevation. Thus, GPS is limited to assessing movement in outdoor areas with a clear line of sight to the sky. Signal blockage by major buildings, mountains, trees, and so on vastly limits the accuracy of the receivers causing potential limitations in the use of GPS in assessing bicycling.19 This may cause the route to appear to bounce around the actual route reducing the spatial accuracy in the type of units required for measuring AT. Limitations aside, GPS can be very useful for measuring AT given the combination of recording actual routes and the specific times during which the behavior occurred. GPS is also great for showing routes taken along the trip, accounting for route choice unlike GIS. This information is often missing from other measurement techniques; thus, GPS is valuable for measuring travel volumes on a specific route and understanding behavioral aspects of route choice.13 As technology continues to advance, the quality of GPS devices has only improved making the use of GPS for measuring AT more accessible. GPS-enabled smartphones in particular are a promising new method that can play a major role in this measurement process. Moreover, there exist GPS apps for tracking pedestrian and bicycle trips. CycleTracks is a smartphone app developed by San Francisco County Transportation Authority to track AT trips.20 A case study of CycleTracks concluded that the app was a viable and inexpensive method of gathering data but requires ample data preprocessing.21,22
Bluetooth Static Bluetooth monitoring has been used to count vehicles for years and has recently been adopted to count pedestrians23 and could also be used to count cyclists. Bluetooth devices
54 Chapter 3 have unique identifying code that allow for monitoring travel and route using multiple monitoring points/checkpoints. This is great for locations with big crowds where other more traditional counters may fail to function properly.13 A potential limitation for this method is that not everyone has a Bluetooth-enabled device and thus margins of errors may be high. However, Bluetooth-enabled smartphones are making this much easier.
Webcams Using video-based sensors like webcams has recently been used to capture activity and estimate AT. This technique is both unobtrusive and relatively inexpensive. For example, Hipp et al. evaluated images from public webcams using crowdsourcing via Amazon Mechanical Turk.24 All areas may not have webcams and thus may not always be an option. Moreover, this technique requires visibility (e.g., lighting) and thus can only be used for counting daylight hours or very well-lit locations at night.
Crowdsourcing Given that spatial data related to biking is often limited, crowdsourced data can be useful for measuring rates of biking. This can primarily be used to be understand bike data as it relates to demographics, geography, physical infrastructure, personal travel experiences, and community priorities to name a few. Benefits of crowdsourcing include improved response rates, community engagement, minimization of time and location constraints, and integration into spatial analysis. Furthermore, crowdsourcing may be less expensive than other measurement methods. Crowdsourcing can be accomplished using tools like Strava25 and CycleTracks,20 as well as many others. Strava is a smartphone app and website originally developed to connect riders from around the world. The app functions in such a way that when the user is active, Strava uses his/her phone or GPS device to track his/her activities and then shares it with their friends. The app also provides user with performance metrics like speed, pace, distance, and performance compared to past attempts.25 The information from these apps can be harnessed to evaluate a cyclist’s routes, time spent riding, and attitudes about their ride. All of this information combined is very valuable to researchers and community employees interested measuring cycling.
Automated Video Image Processing An alternative to evaluating images from webcams involves analyzing stored video. A computer is able to process video automatically by isolating the moving portion of the image and provide traffic counts. A technique called computer vision allows for real-time processing of video and has been used to perform pedestrian counts.26,27 This technique may easily be extended to bicycle counts28 and to measure AT at complex intersections (e.g., roundabouts).29
Measuring Bicycling Within the Community 55 Automated Video Image Processing has the same limitations as other video methods including webcams (i.e., lighting). Accuracy for this technique for conducting pedestrian counts is about 15% off from other automated methods.13
Bikeshare Systems Chapter 6 discusses in detail how bike shares can be used to promote and encourage safe riding. That said, shared bicycle systems are expanding rapidly across the United States and thus provide a great opportunity for measuring cycling. Bicyclists tend to only keep a bike rented from a bike share for short-term use.13 As part of a bikeshare system, bicyclists start and end at docking station. Each bike has a unique ID number, which is associated with the information of the individual who rented the bike. This data can be harnessed to get some idea of the route that was traveled, the length of time that they rode, and to conduct volume analyses. With these data, it is possible to make observations on how usage varies across times and days. As an example, Pittsburgh collects data on the number of users that rent a bike from a particular bikeshare hub within their city. Below is a heat map of Pittsburgh bikeshare users in mid-September 2015. Using such information, researchers have examined London’s bikeshare system and determined that peak weekday rides occur in the early morning. Alternatively, researchers have noted that in Washington D.C.’s bikeshare system, peek weekday riding occurs in the afternoon—these results were similar to all results for the weekend.30
Heat Map of Pittsburgh’s bikeshare Rentals, September 7 Through September 8, 2015. Healthy Ride
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Permanent Count techniques like manual count surveys, counting apps, and automatic counters are of great use for short-term analyses; however, it is time-intensive, labor-intensive, and expensive to obtain long-term continuous counts using such techniques. The short-term nature of these count methods make it impossible to obtain long-term changes across weather types, seasons, years, and decades. As an alternative, it is possible to install permanent counters to track changes over long periods of time. These permanent counters afford the opportunity to make generalizations on who is using the facilities, the major trip purposes of the facility are (i.e., are cyclists using the street/trail/path mainly for commuting, leisure, etc.), and estimation of miles traveled.13 The continuous data provided by permanent counters can provide information to develop adjustment factors for seasonal and weather variations like rain, snow, and temperature. The most commonly used permanent counter is the induction loop. This technology is commonly used for detecting traffic at traffic signals, and can be configured in such a way that senses metals in a bicycle that passes over the detector. Induction loops have been shown to accurately differentiate bicycles from motor vehicles; however, depending on the path these detectors may undercount or overcount.31
Subjective Subjective techniques including national surveillance, targeted surveying, multimodal surveying, interception, and bikeshare surveying have the potential to provide the similar information to objective techniques. However, this information may be tainted by various types of bias. Participants may be unwilling to provide personal details and/or inaccurately recall information. The data is also subject to social desirability bias, by which individuals select the response they believe they should select. Despite potential biases, subjective techniques can help dive a little deeper into the reasons why someone is biking. This knowledge is invaluable as it can be crucial in informing future interventions, campaigns, and updates to the bike-supportive built environment.
National Surveillance Two major national surveys within the United States provide information on the mode in which Americans travel: the American Community Survey (ACS) and the National Household Transportation Survey (NHTS). Both of these surveys examine mode of transportation to work and isolate bicycling as a unique category rather than observing AT as a whole. These population-based surveys are arguably the most comprehensive method of quantifying mode of transportation for a large number of people across an entire geographic area. Other countries conduct national surveillance as well that, in some ways, overlap with the ACS and NHTS.
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American Community Survey The ACS is a nationwide survey conducted by the US Census Bureau.32 Data is provided in 1-, 3-, and 5-year estimates. The majority of the survey asks questions relative to income, disability, employment, and housing characteristics; however, the ACS also contains the question: “How did this person usually get to work last week?”. This question allows the participant to select from a variety of options, including biking, the method with which they feel they traveled most of their distance of their commute. The ACS is the broadest and most accessible data on bicycling commuting in the United States and is a great resource for garnering a general sense of bicycling trends across the country. In combination with other tools, like the Census Transportation Planning Package, the ACS is particularly useful for understanding bicycling to work in tandem with demographics data.13 While the ACS can provide a good overview of bicycling in the United States, it is not without limitations for analyzing bicycling. The first problem for analyzing bicycling data lies in the phrasing of the question. As mentioned above, the ACS only asks participants about their usual form of transportation for travel to work. This vastly limits the number of participants as it does not include results from individuals who (a) do not work, and/or (b) work from home, and (c) are disabled and unable to walk or bicycle. Second, the question restricts the participant to selecting their usual form of transportation to work. This prevents the inclusion of participants who may bike to work occasionally but principally use another mode of transportation or use mixed modes of transportation. Third, while recent versions of the ACS include this question it was only added to the survey in 2000. From 1960 through 2000 this question was only included in the decennial census. This detail should be accounted for when comparing historical data to present and future data.
National Household Transportation Survey In comparison to the ACS, the NHTS provides a relatively small sample of bicycling data. Starting in 2001, the NHTS began when the American Travel Survey was combined with the Nationwide Personal Transportation Survey. The NHTS is conducted every 5–7 years and was most recently conducted in 2016. Similar to the ACS, the NHTS asks a question on the mode with which the participant usually travels to work. Additionally, the NHTS is more comprehensive for bike-related transportation delving deeper into the frequency with which someone bikes, an individual’s abettors/barriers to bicycling, and any improvements that the respondent believes can be made to their bike-supportive environment. See Box 3.3 for a comprehensive list of NHTS bike-related questions. The primary limitation of the NHTS is that it represents a relatively small sample of US cyclists (about 150,000 households) making generalization to smaller localities difficult. However, when combined with local add-ons for sampling with a jurisdiction, the NHTS can provide fairly accurate results within a particular jurisdiction.
58 Chapter 3 BOX 3.3 National Household Travel Survey Bicycle-Related Questions33
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Surveys in Other Countries As mentioned, the United States is (understandably) not the only country that uses survey techniques to evaluate the frequency with which people ride in their country. Canada also measures commuting to work (e.g., mode of transportation, travel time), similar to the ACS, via the National Household Survey.34 The United Kingdom conducts a decennial census that was most recently conducted in 2011.35 Within this census, mode of travel to work is examined. Additionally, Australia utilizes the Household Income and Labour Dynamics in Australia (HILDA) survey.36 This longitudinal survey measures commuting to work. Many other countries use analogous methods to examine national rates of cycling within their country.
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Targeted Surveys National surveillance techniques like the ACS and NHTS provide great information at a large scale on bicycle travel. However, more targeted surveys are often more appropriate when wanting to take a deeper look at a particular demographic in a specific location.
Pedestrian and Bicyclist Travel Survey As the name suggests, the Pedestrian and Bicyclist Travel Survey (PABS) was developed to help quantify the amount of walking and bicycling that happens at a jurisdictional level. The PABS is a random-sample, mail-out questionnaire that collects data about AT behaviors and is meant to address the deficiencies of national surveys in measuring AT.37 This survey has validated and checked for statistical reliability, and thus, should produce comparable results across jurisdictions.
Multimodal Surveying A major limitation of many bicycle surveys is that they assume (a) individuals only commute via one mode of travel across all days and/or (b) individuals only commute via one mode of travel within a single trip. “Last-mile” connections (e.g., carpooling to a park-and-ride, walking to the train, biking from the bus stop, etc.) are great examples of multimodal trip information missed by traditional surveys.13 Clifton and Muhs reviewed the literature of approaches to examining multimodal travel behavior and provided suggestions on how to combat the issue.38 They recommend that walking trips over 150 ft be included in survey, an improvement in computer-assisted telephone interviews, and the use of more advanced technologies (e.g., GPS) to collect travel data. Further, surveys should be developed that focus on more than single modes.
Intercept Surveying A direct method of obtaining information on bicycling is through the use of an intercept survey. This method intercepts cyclists while on a specific route and then a brief interview or survey is conducted. As discussed in the beginning of this chapter, the Standard Bicycle Survey is a one-page survey provided by the NBPDP that may be useful for such interviews.10 If interviewing, the conversation should remain brief so as not to inconvenience the rider. Survey alternatives may include a mail-back or internet-based survey that the rider can respond to after their trip is completed. A limitation of this type of survey is that you can only interview people who are riding their bikes at that point in time and thus your results may not be generalizable.
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Bikeshare Surveying Additional to collecting data from bikeshare systems as discussed above, surveys of bikeshare systems may provide detailed information on the demographics of bike users as well as more detailed travel behavior information (e.g., impact of environmental factors, seasonal changes, etc.).
Summary It is important to understand that any survey method has its benefits and pitfalls dependent on the situation at hand. Surveys should always be tailored around the target population. Further, generalizing the results obtained from these targeted surveys should be done with caution. National surveillance like the NHTS and ACS capture broader trends and across the country that can be compared to previous nationally conducted surveys. However, while these surveys are broad their sample size is relatively limited, therefore, the results may not translate well to certain populations and jurisdictions. Targeted surveys provide an alternative to national surveillance. Options include, but are not limited to, the PABS, intercept surveys of cyclists on a particular route, and bikeshare surveys. Conducting surveys can be expensive, time consuming, and inconvenient and are consequently not necessarily the perfect solution. However, they do help eliminate the multimodal travel gaps seen in national surveys and may be more appropriate at a local level. Still, surveys are subjective and subject to various biases that, to an extent, can be avoided using more objective measures.
Case study 3.1 Evaluating green shared lanes—Minneapolis, Minnesota As mentioned in the chapter, it is sometimes very necessary for communities to utilize multiple evaluation techniques to measure cycling within their community. Minneapolis, Minnesota wanted to evaluate the impact of the share bus-bike-right turn lane on Hennepin Avenue between Washington Avenue and 12th street was enhanced to include-green shared lane markings. To conduct this analysis, they identified seven specific research questions examined and parameters used to evaluate each particular research question. As you will see, in their evaluation process they made sure to use a variety of techniques that aligned with the research question at hand. Each are presented below. The bullets underneath each research question indicates the measurement technique used for evaluation. 1. Are bicyclists using the green lane and where are they riding? ○ Bicyclist riding position using video analysis
62 Chapter 3 2. Are motor vehicles and buses driving in the green lane and where are they driving? ○ Motor vehicle and bus driving position using video analysis 3. Are motorists and buses given a safe passing distance (>3 ft) when overtaking bicyclists? ○ Road user driving or riding position using video analysis 4. Does driving or riding behavior vary by lane width? ○ Road user driving or riding position using video analysis 5. Have bicyclist volumes on Hennepin Avenue changed with the addition of green lanes? ○ Daily bicyclist volumes using manual bicyclist counts 6. Has the safety of bicyclists improved with the addition of the green lanes? ○ Bicyclist-motorist crash rates using MN DPS accident reports 7. How do road users comprehend and perceive the green lanes? Does this differ from actual driving or riding behavior? ○ Survey of downtown travelers
As is seen here, in Minneapolis, Minnesota various analysis techniques were used including video analysis, manual bicyclist counts, examination of accident reports, and a survey of travelers. This provided the Minneapolis government with a much more comprehensive look at cycling within their community and how it changed on Hennepin Avenue.
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