their ability to make evidence-based choices among investment alternatives ... The authors, the University of Minnesota, and the U.S. Government do not ..... Bicycles and pedestrians have been counted in a variety of ways since 2007 in ... pedestrian traffic, including scaling factors for adjusting hourly counts to daily counts,.
Understanding the Use of Non-Motorized Transportation Facilities
Final Report
Prepared by: Greg Lindsey Kristopher Hoff Steve Hankey Xize Wang Humphrey School of Public Affairs University of Minnesota
CTS 12-24
Technical Report Documentation Page 1. Report No.
2.
3. Recipients Accession No.
CTS 12-24 4. Title and Subtitle
5. Report Date
Understanding the Use of Non-Motorized Transportation Facilities
July 2012
7. Author(s)
8. Performing Organization Report No.
6.
Greg Lindsey, Kristopher Hoff, Steve Hankey, and Xize Wang 9. Performing Organization Name and Address
10. Project/Task/Work Unit No.
Humphrey School of Public Affairs University of Minnesota 301 19th Avenue South Minneapolis, Minnesota 55455
CTS Project #2010058
12. Sponsoring Organization Name and Address
13. Type of Report and Period Covered
Intelligent Transportation Systems Institute University of Minnesota 200 Transportation and Safety Building 511 Washington Ave. SE Minneapolis, Minnesota 55455
Final Report
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15. Supplementary Notes
http://www.its.umn.edu/Publications/ResearchReports/ 16. Abstract (Limit: 250 words)
Traffic counts and models for describing use of non-motorized facilities such as sidewalks, bike lanes, and trails are generally unavailable. Because transportation officials lack the data and tools needed to estimate use of facilities, their ability to make evidence-based choices among investment alternatives is limited. This report describes and assesses manual and automated methods of counting non-motorized traffic; summarizes counts of cyclists and pedestrians in Minneapolis, Minnesota; develops scaling factors to describe temporal patterns in non-motorized traffic volumes; validates models for estimating traffic using ordinary least squares and negative binomial regressions; and estimates bicycle and pedestrian traffic volumes for every street in Minneapolis. Research shows that automated counters are sufficiently accurate for most purposes. Automated counter error rates vary as a function of type of technology and traffic mode and volume. Across all locations, mean pedestrian traffic (51/hour) exceeded mean bicycle traffic (38/hour) by 35 percent. One-hour counts were highly correlated with 12-hour "daily" counts. Significant correlates of non-motorized traffic vary by mode and include weather (temperature, precipitation), neighborhood socio-demographics (household income, education), built environment characteristics (land use mix), and street (or bicycle facility) type. When controlling for these factors, bicycle traffic, but not pedestrian traffic, increased over time and was higher on streets with bicycle facilities than without (and highest on off-street facilities). These new models can be used to estimate non-motorized traffic where counts are unavailable and to estimate changes associated with infrastructure improvements. 17. Document Analysis/Descriptors
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Traffic surveillance, Traffic volume, Traffic models, Traffic counts, Bicycle counts, Pedestrian counts, Trail counts
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Understanding the Use of Non-Motorized Transportation Facilities Final Report
Prepared by:
Greg Lindsey Kristopher Hoff Steve Hankey Xize Wang Humphrey School of Public Affairs University of Minnesota
July 2012
Published by:
Intelligent Transportation Systems Institute Center for Transportation Studies University of Minnesota 200 Transportation and Safety Building 511 Washington Ave. S.E. Minneapolis, Minnesota 55455
The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. This report does not necessarily reflect the official views or policies of the University of Minnesota. The authors, the University of Minnesota, and the U.S. Government do not endorse products or manufacturers. Any trade or manufacturers’ names that may appear herein do so solely because they are considered essential to this report.
Acknowledgments The research team wishes to acknowledge those who made this research possible. The study was funded by the Intelligent Transportation Systems (ITS) Institute, a program of the University of Minnesota’s Center for Transportation Studies (CTS). Additional financial support was provided by the United States Department of Transportation Research and Innovative Technologies Administration (RITA). The Hubert H. Humphrey School of Public Affairs also provided support and in-kind services. Special thanks are due to Tony Hull, Transit for Livable Communities; Shaun Murphy, Minneapolis Department of Public Works; Jennifer Dill, Portland State University; and Jennifer Ringold, Minneapolis Department of Parks and Recreation. Each of them provided useful comments on elements of this research. We also thank students from the Hubert H. Humphrey School of Public Affairs who contributed to this research: Alex Anderson, Jason Borah, Andrew Senn, Brad Utecht, and Zhiyi Xu.
Table of Contents Chapter 1 Introduction................................................................................................................. 1 Chapter 2 Use of Non-Motorized Transportation Facilities: A Selected Review ................... 3 2.1 Measuring Use of Non-Motorized Traffic Facilities ............................................................ 3 2.2 Patterns in Non-Motorized Traffic ....................................................................................... 4 2.3 Models for Estimating Non-Motorized Traffic .................................................................... 4 Chapter 3 Approach and Methods Used in Research ............................................................... 7 3.1 Measurements of Non-Motorized Traffic in Minneapolis .................................................... 7 3.1.1 Field Observations of Bicycle and Pedestrian Traffic................................................... 8 3.1.2 Magnetic Loop Detector Counts of Greenway Bicycle Traffic ...................................... 8 3.1.3 Infrared Counts of Mixed-Mode Trail Traffic ............................................................. 11 3.2 Temporal and Spatial Patterns in Non-Motorized Traffic .................................................. 13 3.2.1 Variations in Hourly Traffic ........................................................................................ 13 3.2.2 Variations in Daily Traffic ........................................................................................... 13 3.2.3 Monthly and Seasonal Variations in Traffic ................................................................ 14 3.2.4 Spatial Patterns in Non-Motorized Traffic .................................................................. 14 3.3 Models of Non-Motorized Traffic ...................................................................................... 14 3.3.1 Models of Pedestrian Traffic ....................................................................................... 14 3.3.2 Models of Bicycle Traffic ............................................................................................. 15 3.3.3 Models of Multi-Mode Trail Traffic............................................................................. 15 Chapter 4 Traffic Volumes on Non-Motorized Infrastructure in Minneapolis .................... 17 4.1 Bicycle Volumes ................................................................................................................. 17 4.1.1 Manual Counts by Bicycle Facility Type ..................................................................... 17 4.1.2 Magnetic Loop Detector Trail Counts ......................................................................... 18 4.2 Pedestrian Volumes ............................................................................................................ 19
4.3 Mixed-Mode Trail Volumes ............................................................................................... 21 Chapter 5 Temporal and Spatial Patterns in Non-Motorized Traffic in Minneapolis......... 23 5.1 Temporal Variations in Bicycle Traffic .............................................................................. 24 5.1.1 Hourly Variations in Bicycle Traffic............................................................................ 24 5.1.2 Daily Variations in Bicycle Traffic .............................................................................. 25 5.1.3 Monthly Variations in Bicycle Traffic.......................................................................... 26 5.2 Temporal Variations in Pedestrian Traffic ......................................................................... 27 5.2.1 Hourly Variations in Pedestrian Traffic ...................................................................... 27 5.2.2 Daily and Monthly Variations in Pedestrian Traffic ................................................... 29 5.3 Temporal Variations in Mixed-Mode Trail Traffic ............................................................ 29 5.3.1 Hourly Variations in Mixed-Mode Trail Traffic .......................................................... 29 5.3.2 Daily Variations in Multi-Mode Trail Traffic .............................................................. 30 5.3.3 Monthly Variations in Mixed-Mode Trail Traffic ........................................................ 31 Chapter 6 Models of Bicycle, Pedestrian, and Mixed-Mode Trail Traffic ............................ 33 6.1 Models of Pedestrian and Bicycle Traffic .......................................................................... 33 6.2 Models of Mixed-Mode Trail Traffic ................................................................................. 39 Chapter 7 Findings and Implications for Transportation Planning and Management ....... 45 7.1 Findings about Measurement and Use of Non-Motorized Facilities .................................. 45 7.2 Implications for Counting Pedestrians and Cyclists ........................................................... 48 7.3 Implications for Modeling Non-Motorized Traffic ............................................................ 48 7.4 Implications for Transportation Planning and Management .............................................. 50 References .................................................................................................................................... 51 Appendix A: Normalization of Manual Pedestrian and Bicycle Count Data Appendix B: Daily Traffic Patterns for Mixed-Mode Locations
List of Tables Table 3.1. Deployment of magnetic loop detectors in Minneapolis. .............................................. 9 Table 3.2. Calibration equations for DPW magnetic loop detectors. ........................................... 10 Table 3.3. Deployment of active infrared trail counters in Minneapolis. ..................................... 11 Table 3.4. Calibration equations for Trailmaster ™ active infrared monitors.............................. 13 Table 4.1. Count location breakout by bicycle infrastructure....................................................... 17 Table 4.2. 12-hour estimated bicycle counts (6:30am – 6:30pm)................................................. 18 Table 4.3. Summary of bicycle counts by street functional class. ................................................ 18 Table 4.4. Summary of estimated 12-hour pedestrian counts by functional class. ....................... 20 Table 4.5. Summary pedestrian counts by presence of a bus line. ............................................... 20 Table 4.6. Summary pedestrian counts by functional class and presence of a bus line................ 21 Table 5.1. Bicycle scaling factors for the manual count data to 12-hour counts (6:30-18:30). .... 24 Table 5.2. Pedestrian scaling factors for the manual count data to 12-hour counts (6:30-18:30). 28 Table 6.1. Independent variables used for regression of manual count data. ............................... 34 Table 6.2. Regression results for 12-hour bicycle and pedestrian counts..................................... 35 Table 6.3. Variables selected for model 1 building and expected signs. ...................................... 40 Table 6.4. Estimation results of the models. ................................................................................. 41
List of Figures Figure 3.1. Locations of manual and continuous counts of non-motorized traffic. ........................ 7 Figure 3.2. Scatter plot of manual counts vs. magnetic loop detector counts............................... 10 Figure 3.3. Scatter plot of manual counts vs. TrailMaster TM infrared counts. ............................. 12 Figure 4.1. Adjusted average (monthly) daily traffic for each loop detector location.................. 19 Figure 4.2. 30-day moving average of daily multi-mode counts on off-street trails from active infrared counters. .......................................................................................................................... 22 Figure 5.1. Hourly bicycle (manual and loop detector) and pedestrian (manual only) count patterns. All 30-minute counts are normalized to a percentage of the 12-hour count (6:30-18:30). ....................................................................................................................................................... 23 Figure 5.2. Bicycle 12-hour count predictions (n=43) from peak hour measures for hourly (dashed lines) and bi-hourly (solid lines) counts. ......................................................................... 25 Figure 5.3. Weekend:Weekday ratios for daily bicycle counts on the Midtown Greenway (note: Hennepin Ave detector was broken from January 2009 – October 2009). ................................... 26 Figure 5.4. Monthly scaling factors for each loop detector on the Midtown Greenway as compared to December of each (note: Hennepin Ave detector was broken from January 2009 – October 2009). .............................................................................................................................. 27 Figure 5.5. Pedestrian 12-hour count predictions (n=43) from peak hour measures for hourly (dashed lines) and bi-hourly (solid lines) counts. ......................................................................... 28 Figure 5.6. Average hourly non-motorized traffic volumes by month for one location with an infrared counter – Lake Nokomis. ................................................................................................ 29 Figure 5.7. Percent of daily non-motorized traffic volumes by month for one location with an infrared counter – Lake Nokomis. ................................................................................................ 30 Figure 5.8. Weekend:Weekday average adjusted daily count ratio. ............................................. 31 Figure 5.9. Average daily traffic volumes. Light colors represent inner trails (pedestrian) and dark colors represent outer trails (bicycle) for each location. ....................................................... 31 Figure 6.1. Estimated non-motorized traffic for street and trail segments in Minneapolis, MN. . 37
Figure 6.2. Comparison of model predictions (model data: 2007-2009) to actual counts (year2010) for pedestrians and bicycles. Upper left: Bicycle ordinary least squares; Upper right: Pedestrian ordinary least squares; Lower left: Bicycle negative binomial; Lower right: Pedestrian negative binomial. ......................................................................................................................... 38 Figure 6.3. Predicted and actual trail traffic, April 24-30, 2011. .................................................. 44
Executive Summary Background and Research Objective: The general aim of the research described in this report is to increase understanding of the use of non-motorized transportation facilities in Minneapolis. Planners and transportation officials currently lack the data and tools necessary to make evidence-based choices about investment alternatives in non-motorized infrastructure. This research builds a foundation for this type of work by comparing automated counting technologies, validating various data sources, and developing first-generation models that will aid in the development of tools for practitioners. Data and Methods: Technology for measuring non-motorized traffic has evolved and advanced but generally is not widely deployed. There are two distinct methods used to monitor nonmotorized traffic: (1) short (1- or 2-hour) manual counts, and (2) continuous measurements using automated instruments. In Minneapolis both of these types of data are currently being collected. The City of Minneapolis Department of Public Works, Transit for Livable Communities, and University of Minnesota each run programs that collect both short, manual counts (on-street and urban trails) and continuous measurement (mostly urban trails). In general, the research presented here occurred in mainly four steps: •
collection and validation of existing counts of bicycle and pedestrian traffic in Minneapolis;
•
acquisition and deployment of infrared counters for measuring mixed-mode trail traffic, including development of protocols for collection and validation of data;
•
analyses of counts by mode and facility type;
•
development of regression models for determining correlates of non-motorized traffic and estimating daily bicycle, pedestrian, and mixed-mode trail traffic.
Results and Key Findings: This research has resulted in several key findings about the use of non-motorized infrastructure and the relationships of neighborhood characteristics to nonmotorized traffic volumes. More data and research is needed to confirm and extend our findings; however, the following conclusions represent a significant contribution to the understanding of non-motorized traffic volumes and patterns. •
Bicycles and pedestrians have been counted in a variety of ways since 2007 in Minneapolis. The most common type of count is the short (2-hour) peak-hour manual count. These counts are supplemented by a smaller number of continuous counts by various technologies.
•
All traffic counts regardless of approach or technology used, including manual counts, include errors and therefore are estimates. In this report we describe correction equations for the various types of technology and discuss implications of error in manual counts.
•
Bicycle and pedestrian traffic volumes vary significantly by location, infrastructure type, day of week (i.e., weekday vs. weekend), and month of year or season.
•
Each type of non-motorized traffic (i.e., bicycle, pedestrian, and mixed-mode traffic) follows similar but distinct daily temporal patterns. These temporal patterns appear to be sufficiently stable to permit development of scaling factors that reflect the relationships between hourly and daily counts, weekday and weekend counts, and monthly counts.
•
Analyses of both bicycle and pedestrian flows taken with manual counts indicate that peak hour traffic volumes correlate strongly with 12-hour traffic volumes.
•
While more data would be helpful for non-motorized traffic modeling, we are able to demonstrate that with limited data first-generation models can effectively relate traffic volumes to certain neighborhood and infrastructure characteristics.
Conclusions and policy implications: We have demonstrated that with limited data there are a series of analyses and models that can be developed to aid planners in planning for nonmotorized travel and for undertaking tasks such as allocating funding for non-motorized investments. Increasing the amount of available data would greatly improve the analyses and models presented here.
Chapter 1 Introduction Although the federal, state, and local governments are investing hundreds of millions of dollars in non-motorized transportation facilities, traffic counts and other basic information about use of these facilities generally are unavailable for purposes of planning and management. Because officials lack both the data and tools needed to estimate use of facilities, their ability to make evidence-based choices among investment alternatives and to optimize management of infrastructure and transportation systems is limited. Officials need both information about volumes and patterns of traffic on non-motorized transportation infrastructure and new tools for estimating use for facilities where data are unavailable. The general aim of the research described in this report is to increase understanding of the use of non-motorized transportation facilities. Using the City of Minneapolis as a study site, this report describes: •
The collection, validation, and analyses, of counts of bicycles and pedestrians on different types of non-motorized infrastructure using different methods, including manual field observations, magnetic loop detectors, and infrared counters;
•
Standardized measures and scaling factors for describing patterns in bicycle and pedestrian traffic, including scaling factors for adjusting hourly counts to daily counts, weekend/weekday traffic ratios, and monthly adjustment factors; and
•
The specification and estimation of models for identifying correlates of daily nonmotorized traffic volumes and for estimating bicycle and pedestrian traffic where counts have not been taken.
This report also discusses the implications of this research for the planning and management of transportation systems. Chapter 2 is a brief literature review that summarizes previous research related to use of nonmotorized infrastructure. Chapter 3 presents methods used in the research. Chapters 4 and 5, respectively, describe traffic counts on different types of facilities and patterns in non-motorized traffic, including counts and patterns for bicycles, pedestrians, and mixed-mode trail traffic. Chapter 6 presents models that can be used to estimate daily bicycle, pedestrian, and mixedmode traffic on different types of facilities. Chapter 7 discusses the results, identifies opportunities for additional research, and presents recommendations for operations and management of infrastructure for bicycles and pedestrians.
1
2
Chapter 2 Use of Non-Motorized Transportation Facilities: A Selected Review Systematic research on use of facilities for non-motorized transportation has been conducted since at least the 1970s, and understanding of patterns of use and factors that affect use of these facilities has increased. However, transportation planners and managers still lack the data and tools they need to support analytic, evidence-based decisions for many, if not most practical applications such as optimizing the efficiency of investments in new facilities or in operations and maintenance of existing facilities. This chapter summarizes research that reflects the general understanding of use of non-motorized facilities, including information about bicycle and pedestrian traffic counts and volumes, temporal patterns in traffic, and models for estimating traffic volumes. 2.1 Measuring Use of Non-Motorized Traffic Facilities Technology for measuring traffic has evolved and advanced, but generally is not widely deployed. There are two distinct methods used to monitor non-motorized traffic: (1) short (1- or 2-hour) manual counts and (2) continuous measurements using automated instruments. These methods are described briefly below. We also point readers to excellent reviews of these technologies for further details.1-5 Manual Counts: Manual count campaigns typically occur annually for short time periods (i.e., 1or 2-hours). An advantage of manual counts is that a large number of locations can be observed albeit for relatively short time periods. These data enable researchers to investigate spatial patterns and variability of non-motorized traffic within an urban area. They also allows for comparison of different types of facilities and their relationship to neighborhood design, location, etc. A key limitation of manual counts is the lack of information about long-term temporal variation. Since these counts are typically for short periods it is difficult to infer differences in daily or seasonal patterns between locations or facility types. Bicycle Loop Detectors: Inductive loop detectors are commonly used to count vehicles as well as for traffic signal control. Loop detectors work using the properties of induction to generate an electric current when a metal object passes over the unit. Loop detectors can be used to identify bicycles instead of vehicles by slightly altering the placement and design of each unit. For example, the City of Minneapolis currently uses loop detectors on the Midtown Greenway to estimate bicycle use. DPW’s loop detectors provide counts in 15 minute increments and only need to be visited approximately once every three months to download data. However, since each loop detector’s placement and installation may vary slightly correction factors are typically not uniform over each unit. Additionally, it is not possible to count pedestrians or non-metal bicycles using loop detection. Infrared Counters: There are two types of infrared counters: active infrared and passive infrared. Active infrared counters emit a pulse signal across an area of interest to a receiver. Each time this beam is broken a count event is registered. Passive infrared detect heat signatures of a passerby to register a count event. Since active infrared requires both a transmitter and a receiver they are typically used on urban trail systems while passive infrared can be deployed in other areas (e.g., sidewalks). Infrared counters provide continuous measurements of non-motorized traffic (both pedestrian and cyclists) and error rates are typically uniform across different units and locations. However, memory capacity is typically small and it is necessary to download data more 3
frequently than for other instruments (i.e., loop detectors). Since the infrared units are typically exposed above ground they are also slightly more prone to vandalism. Video and Computer Imaging: Use of video streams and computer imaging are increasingly replacing the use of loop detectors for continuous measurement of vehicles. Improvements in the software associated with this process has allowed for distinction between modes (i.e., vehicles, bicycle, pedestrians). However, equipment and labor costs associated with data processing are typically much larger than for other instruments. 2.2 Patterns in Non-Motorized Traffic Similar to automobile traffic there seem to be discernible temporal and spatial patterns in nonmotorized travel behavior. However, these patterns seem to differ between modes (e.g., auto vs. cycling vs. walking). Temporal patterns vary on three time scales: (1) Hourly (i.e., within a day), (2) weekday vs. weekend (i.e., between days), and (3) seasonally (i.e., by month). For example, traffic patterns are different on weekends vs. weekdays; however, patterns among all weekdays (or all weekend days) are fairly consistent. Furthermore, volumes vary seasonally which is heavily dependent on weather. There may also be regional differences in patterns of non-motorized traffic. Jones6 illustrated that the patterns of trail use documented in Indianapolis vary from those in other regions of the nation and concluded that “unlike vehicle use patterns, there appear to be significant regional differences in seasonal patterns” and that, for non-motorized facilities, analysts may need to “accept variation as part of the normal estimating process.” A review by Pucher et al.7 highlights this point for bicycle infrastructure interventions stating that there is “considerable variation in estimated impacts, both by type of intervention and by study design, location, and timing”. In Minnesota, MnDOT maintains monitoring stations at about 32,000 locations for automobiles but currently has no program to monitor bicycles and pedestrians. Given the temporal and spatial variability in non-motorized traffic demonstrated in these exploratory studies and the level of investment in non-motorized infrastructure it would be beneficial to collect data that could maximize the effectiveness of infrastructure investment. 2.3 Models for Estimating Non-Motorized Traffic Researchers have worked on methods of estimating non-motorized traffic volumes for at least forty years. Two early examples from the 1970s are: (1) use of aerial photography to count pedestrians and develop regression models to estimate pedestrian traffic as a function of built environment variables8 and (2) estimating pedestrian traffic per hour for blocks in Milwaukee, Wisconsin as a function of land use and other variables.9 More than 20 years after these exploratory studies, Hunter and Huang10 completed a comprehensive review of reports on the use of bicycle lanes and off-street trails and found wide variation in the level of detail and quality. Although the scope of studies remains insufficient, researchers have added new insights in several key areas thought to impact non-motorized travel: Built environment characteristics,11-13 infrastructure design characteristics,14,15 neighborhood socio-economics,16 and weather.17 Furthermore, researchers have made incremental steps towards developing traditional traffic models (e.g., gravity models) for non-motorized travel by developing impedance functions for cycling and walking,18 modeling mode share near bicycle facilities,19 and building route-choice models.20 For example, researchers explained more than eighty percent of observed variation in traffic at 30 locations on five multiuse trails in Indianapolis, Indiana by modeling daily counts as 4
a function of weather, day of week (and month of year), neighborhood socio-demographics, neighborhood form, and trail characteristics.14,21,22 They also demonstrated tradeoffs in quality of traffic forecasts associated with differences in the areal units used to calculate covariates (e.g., census tracts vs. network-defined “pedestrian access zones”).21
5
6
Chapter 3 Approach and Methods Used in Research 3.1 Measurements of Non-Motorized Traffic in Minneapolis In addition to the preceding literature review, the general approach to this research included: •
collection and validation of existing counts of bicycle and pedestrian traffic in Minneapolis;
•
acquisition and deployment of infrared counters for measuring mixed-mode trail traffic, including development of protocols for collection and validation of data;
•
analyses of counts by mode and facility type;
•
development of regression models for determining correlates of non-motorized traffic and estimating daily bicycle, pedestrian, and mixed-mode trail traffic.
The collection and analyses of traffic counts were undertaken in collaboration with professional staff in the Bicycle Program in the Traffic and Parking Services Division of the City of Minneapolis Department of Public Works (DPW); a nonprofit organization, Transit for Livable Communities (TLC); the Minneapolis Parks and Recreation Board (MPRB); and students in capstone and workshop courses at the Humphrey School of Public Affairs. The count locations are shown in Figure 3.1.
Figure 3.1. Locations of manual and continuous counts of non-motorized traffic. 7
3.1.1 Field Observations of Bicycle and Pedestrian Traffic The DPW and TLC have cooperated in manual field counts of bicycle and pedestrian traffic at more than 240 locations in the City of Minneapolis since 2007. Counts are taken by employees and volunteers annually in September or October and occasionally at different times throughout the year. DPW and TLC use a non-random, purposive sampling approach: locations are selected because their characteristics are of special interest. For example, locations may be included in the sample because they are believed to have high volumes, have been the locations of accidents, or are the locations for proposed infrastructure improvements. Most counts are two-hour, peak hour counts, although some 12-hour counts have been taken. Because the location and timing of observations are not random, the results cannot be generalized more broadly to the rest of the city or to locations outside the city. Protocols for the DPW and TLC counts, which generally are consistent with those used by the National Bicycle and Pedestrian Documentation Project23, are described in reports by DPW24 and TLC.25 DPW and TLC have reported annual results and trends in volumes at locations over time24,25, but due to staffing and other limitations, the organizations have not maintained an integrated, multiyear dataset or analyzed the data to identify systematic variation of traffic volumes in relation to facility type or location characteristics. DPW and TLC provided copies of hundreds of spreadsheets containing the results of counts at different locations to the research team, and students in a capstone class developed an integrated dataset for all bicycle and pedestrian counts taken by DPW and TLC between 2007 and 2009. This data set included 458 counts of bicycle traffic and 458 counts of pedestrian traffic at 240 different locations.26 Using geographic information systems (GIS), researchers next identified characteristics of the sample locations including street functional classification, presence of a bus line, presence of a bicycle facility, and various land use and socio-demographic characteristics. The team then stratified the sample by street functional classification and other characteristics to determine whether traffic appeared to vary systematically with particular characteristics. To illustrate the potential for error in manual field observations, a student team in a workshop class collaborating with this project conducted field counts of traffic at 2 locations and calculated inter-observer differences in hourly traffic volumes. The mean difference in hourly traffic counts taken by observers simultaneously at the same location was 1.4%.27 Although no error rates are reported for the DPW/TLC counts, this error rate is believed to be an approximation of the magnitude of uncertainty that may be associated with all the manual field counts described in this report. 3.1.2 Magnetic Loop Detector Counts of Greenway Bicycle Traffic The DPW maintains magnetic loop detectors to count bicycles at three locations on the Midtown Greenway, a 5.5 mile, multi-use urban trail on an historic rail line that that runs east-west across Minneapolis. The detectors, which are embedded in the asphalt trail, count changes in the electromagnetic field over a coil of wire that occurs when a bicycle with metal rims travels over the wire. Each change, or disturbance, is recorded as an event or a single use. The detectors are designed to provide both a count of total use and counts of bicycles riding in each direction. The detectors provide continuous 24-hour counts for the entire year (see Table 3.1 for a summary of available data). 8
Table 3.1. Deployment of magnetic loop detectors in Minneapolis. Location Hennepin Cedar West River Parkway Total
Days deployed 1,402 1,402 1,402 4,206
Usable days 852 1,218 1,243 3,313
Percentage 61% 87% 89% 79%
Following installation, the DPW developed procedures for collecting and analyzing data obtained from the loop detectors but did not validate or calibrate the counts. Estimates of bicycle traffic potentially may be inaccurate because of detector malfunction or because of travel behavior of cyclists. For example, total counts of bicycle traffic may be low because bicyclists may ride on gravel shoulders adjacent to the trail and not be detected. Directional counts may be inaccurate because cyclists may ride in oncoming lanes. Because pedestrians also use the trail, the bicycle counts are an underestimate of total trail traffic. Because of resource shortages and conflicting priorities, DPW has not produced estimates of total traffic on the trail. Bicycle traffic volumes from the detectors are available for the trail near its intersections with Hennepin Avenue, Cedar Avenue, and the West River Parkway (Figure 3.1; Table 3.1). The DPW has maintained these detectors since at least 2007 and published reports that summarize daily and monthly bicycle volumes except when counters have malfunctioned.24 The magnitude of error or uncertainty in these counts has not been estimated or reported by DPW. The DPW provided copies of spreadsheets with bicycle counts to the research team which reanalyzed them. As part of the re-analysis, researchers conducted field observations to validate counts and, if needed, procedures for calibration.27 The general approach to validation involved counting bicyclists on the trail at the location of each detector and then comparing hourly totals from the detector with the hourly observation totals. These investigations included 84 hours of manual field counts at the Hennepin location, 8 at the Cedar location, and 50 hours at the West River Parkway location. The estimates of bicycle traffic at each location made with field observations varied from the estimates from the detectors at each location and were inconsistent. The counts from the detector at the Hennepin location were consistently high, overestimating hourly bicycle traffic volumes by an average of 27 percent, while the counts at the West River Parkway location were consistently low, underestimating hourly traffic by an average of 7 percent (counts at the Cedar location overestimated by 5%).27,28 At each of the three locations, the magnitude of difference between the field directional counts and the detector directional counts was much greater than the difference for the total counts. The reasons for the differences in detector performance, including factors that contribute to higher or lower counts, are unknown. The magnitude of error associated with the directional counts is sufficiently high that use of the directional estimates is not advised. Figure 3.2 presents scatter plots and ordinary least squares (OLS) regression lines for the observed and detector hourly counts at each location. Table 3.2 includes calibration equations estimated using OLS regression that can be used to adjust hourly counts from the detectors to account for the observed, systematic error associated in the detector counts. Calibration of hourly 9
counts using equations estimated with OLS can result in estimates of negative traffic on days with low traffic volumes. For practical applications, estimates of negative traffic can be changed to zero. Alternatively, new correction equations can be estimated using different functional programs that constrain values to be at least zero or minimize the number of estimates with values below zero. 800
Hennepin, n=84 W River Pkwy, n=50
700
Manual Hourly Bicycle Count
Cedar, n=8 Linear (Hennepin, n=84)
600
Linear (W River Pkwy, n=50) Linear (Cedar, n=8)
500 400
y = 0.7273x - 10.434 R² = 0.9806
300 200
y = 0.9448x + 0.0431 R² = 0.9992
100
y = 1.072x - 6.2274 R² = 0.9493
0 0
100
200
300
400
500
600
700
800
Magnetic Loop Detector Hourly Count
Figure 3.2. Scatter plot of manual counts vs. magnetic loop detector counts.
Table 3.2. Calibration equations for DPW magnetic loop detectors.
Location Hennepin Cedar W. River Pkwy
Hours 84 8
Mean hourly manual count 212 73
Mean hourly loop detector count 266 69
Correction equation y=0.727x-10.43 y=0.945x-0.04
R-square 0.981 0.999
50
119
121
y=1.072x-6.23
0.949
Mean % Error 45.0% 7.5% 15.2%
A challenge in interpretation and use of the counts from the magnetic loop detectors is the inconsistency in error, especially the fact that the same type of counters with the same settings are producing estimates of traffic volumes that are both higher and lower than observed volumes. Although the calibration equations can be applied to all historic counts at individual locations to account for error known to exist now, it is unknown how long the errors have persisted or whether the magnitude and direction of error have changed over time. While use of calibration equations to adjust historic counts seems essential given the magnitude of observed errors, the time period for which these calibration equations actually is appropriate is unknown. Periodic 10
validation and calibration is essential for maintaining the most accurate estimates of traffic volumes from the magnetic loop detectors. The research team used the calibration equations in this report to update all DPW traffic counts.28 3.1.3 Infrared Counts of Mixed-Mode Trail Traffic The research team obtained and deployed eight battery-operated Trailmaster ™ active infrared monitors at the six locations determined in consultation with DPW and MPRB (Figure 3.1). These locations include the three locations on the Midtown Greenway with magnetic loop detectors, adjacent locations on the separate bicycle and pedestrian trails around Lakes Calhoun and Nokomis, and a location on a multiuse trail in Wirth Park. Table 3.3 lists the infrared monitor locations, the dates of installation and operation, and the percent of total possible days for which counts are available. Missing counts may be due to monitor malfunction, loss of power, vandalism, or human error in data retrieval. Table 3.3. Deployment of active infrared trail counters in Minneapolis. Location Midtown Greenway: Hennepin Midtown Greenway: Cedar Midtown Greenway: W River Parkway Lake Calhoun Lake Nokomis Theodore Wirth Parkway Total
Days deployed 558 542 416 386 386 385 2,673
Usable days 519 466 379 344 351 354 2,413
Percentage 93% 86% 91% 89% 91% 92% 90%
The Trailmaster ™ monitors consist of a transmitter and a receiver installed on opposite sides of the trail on posts approximately 48 inches high. The transmitter emits a stream of pulses across the trail to the receiver. The receiver records the time when the beam is interrupted by a trail user (i.e., when a predetermined number of pulses are not received). Each time stamp is an “event” or a single use. The monitors can hold 8,000 to 16,000 events, depending on the model. The monitors cannot distinguish between traffic modes (i.e., between bicyclists and pedestrians), and the counts are measures of all or mixed-mode traffic. The monitors also cannot distinguish direction of travel. Trailmaster ™ monitors systematically undercount trail traffic because they record only a single event when users pass simultaneously. This type of measurement error can occur, for example, when users traveling in opposite directions pass at the same time or when bicyclists or pedestrians travel by two or more abreast. Other potential sources of error include missed observations from users passing so quickly they do not break the beam long enough to record an events, extra counts when users move back and forth across the beam multiple times, extreme weather events such as heavy, wet snowfalls that interrupt the beam, and purposeful blocking of the beam by users. To obtain estimates of traffic volumes, the Trailmaster ™ data are downloaded in with a data collector, exported to an Excel © spreadsheet, and totaled by hour, adjusted to correct for the systematic undercount, and then aggregated by day.
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To validate the traffic counts from the monitors and develop calibration equations, the team conducted field observations at the Hennepin, Cedar, and West River Parkway locations. Figure 3.3 present scatterplots and ordinary least squares (OLS) regression lines for the observed and monitor hourly counts at the three locations. The direction and magnitude of the monitor errors are consistent across locations, indicating that a single calibration equation can be used for all monitors. Table 3.4 includes calibration equations for each location plus a general equation estimated from pooled data from all locations. 600
Hennepin Ave, n=84 W River Pkwy, n=41
500
Cedar Ave, n=5
Manual Hourly Count
1:1 Hypothetical 400
300
200
y = 0.0002x2 + 1.0655x - 1.2937 R² = 0.9958
100
0 0
100
200
300
400
500
600
TrailMaster Hourly Count
Figure 3.3. Scatter plot of manual counts vs. TrailMaster TM infrared counts.
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Table 3.4. Calibration equations for Trailmaster ™ active infrared monitors.
Location
Hours
Hennepin Cedar W. River Pkwy Composite
84 5
Mean hourly manual count 235 86
41
114
130
191
Mean hourly TMI count 264 88
Correction equation
R-square
Mean % Error
y=1.1939x-16.703 y=1.0857x-4.9726
0.9941 0.9994
9.8% 11.0%
125
y=1.0995x-0.3899
0.9953
11.6%
213
y=0.0002x2+1.0655x-1.2937
0.9958
10.2%
The calibration equations in Table 3.4 are for hourly mixed-mode trail traffic (i.e., cyclists and pedestrians combined). Different equations have not been estimated for the monitors installed along Lakes Calhoun and Nokomis where there are separate trails for bicyclists and pedestrians. It is likely that the rate of error in the counts provided by the Trailmaster ™ infrared monitors is a function of mode (i.e., the error rates likely are different for bicyclists and pedestrians). Additional field research is underway to develop calibration equations for these locations. 3.2 Temporal and Spatial Patterns in Non-Motorized Traffic A principal objective of this research is to develop standardized measures and scaling factors that transportation planners and managers can use to describe patterns in bicycle and pedestrian traffic. Measures of hourly, peak hour, daily, and monthly traffic all are commonly used by analysts. In addition, estimates of traffic by facility type and other spatial factors often are useful. 3.2.1 Variations in Hourly Traffic Variations in hourly traffic are described by: •
corrected hourly and daily counts, including peak-hour counts;
•
corrected hourly counts as a percentage of daily traffic, where daily traffic is measured as 12-hour volumes or 24-hour volumes, for manual and automated counts, respectively;
•
hourly scaling factors, computed as the value one divided by the proportion of daily traffic that occurs, on average, within the hour.
The hourly scaling factors are used to estimate daily traffic volumes from hourly volumes for modeling and other purposes. 3.2.2 Variations in Daily Traffic Variations in daily traffic are described using the ratio of mean weekend daily traffic to mean weekday daily traffic. The weekend-weekday traffic ratios are computed for different months of the year. For any given location, a weekend-weekday ratio greater than one indicates that recreational rather than utilitarian trips likely accounts for most traffic. Weekend-weekday ratios can be used to estimate traffic volumes when observations for a particular day may be missing or when approximations of aggregate traffic volumes are needed.
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3.2.3 Monthly and Seasonal Variations in Traffic Monthly traffic counts are useful for illustrating the seasonal variation in non-motorized traffic associated with weather and other factors. Where the data record is sufficient, variations in monthly traffic for each location are described by: •
estimates of total and mean daily traffic;
•
the percentage of total annual traffic;
•
monthly scaling factors, computed as the value one divided by the average percentage of total annual traffic within the month.
3.2.4 Spatial Patterns in Non-Motorized Traffic Transportation engineers classify streets by function and design volumes. Functional classifications used in the City of Minneapolis include principal arterial, A-minor arterial, Bminor arterial, collector, and local streets. Some of these streets include facilities such as painted bike lanes and others do not. In addition, there are off-street or separate facilities for pedestrians and cyclists, including sidewalks and trails like the Midtown Greenway. Other spatial characteristics, such as whether bus lines operate on a street, also may be associated with volumes of non-motorized transportation. To illustrate spatial patterns in volumes of nonmotorized traffic, counts are stratified by street functional class, by the presence of a bus line, and by the presence of bicycle facilities on the street. 3.3 Models of Non-Motorized Traffic A principal objective of this research is to specify and estimate models that can be used to determine correlates of non-motorized traffic and to estimate traffic where at locations where counts have not been taken. Separate models are developed for bicycle traffic, pedestrian traffic, and mixed-mode trail traffic. In each of the models, daily traffic counts are the dependent variable. The general approach to modeling involves regressing the daily traffic counts on a vector of independent variables, including daily weather (e.g., temperature, precipitation, humidity); infrastructure type, including street classification and presence of bicycle facilities); presence of a bus line; dimensions of urban form (e.g., land use mix and population density); and neighborhood socio-demographics (e.g., household income, race, education attainment). GIS is used to identify these characteristics for each sample location. Regression models are estimated using both OLS and negative binomial regression. A brief description of the models for each mode is given below. See Chapter 6 for a full discussion of the model inputs and results. 3.3.1 Models of Pedestrian Traffic Models of pedestrian traffic are developed from the DPW/TLC field observations using a twostep process: 1. all counts are converted to 12-hour daily counts using scaling factors, and 2. the estimated 12-hour daily pedestrian counts are regressed on the independent variables.
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Counts from 240 locations taken between 2007 and 2009 are included the dataset used to estimate the model. 3.3.2 Models of Bicycle Traffic Models of bicycle traffic also are developed from the DPW/TLC field observations using a twostep process: 1. all counts are converted to 12-hour daily counts using scaling factors, and 2. the estimated 12-hour daily bicycle counts are regressed on the independent variables. Counts from 240 locations taken between 2007 and 2009 are included the dataset used to estimate the model. 3.3.3 Models of Multi-Mode Trail Traffic Models of mixed-mode trail traffic are developed from the Trailmaster ™ 24-hour traffic counts. Only days with a full 24-hour count are included in the dataset used to estimate the models; days with partial counts were censored from the dataset. This process included mainly three steps: 1. all counts are corrected (on an hourly basis) using the correction equations described above. 2. all hourly counts are then aggregated to daily, 24-hour counts. 3. the aggregated daily counts are regressed on the independent variables. Data from all eight locations where Trailmaster ™ monitors have been installed are included in the models; the number of daily counts available for each site varies.
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Chapter 4 Traffic Volumes on Non-Motorized Infrastructure in Minneapolis This chapter presents basic descriptive statistics by mode for each of the datasets described in Chapter 3. All values are adjusted as described previously and were joined to independent variables using a geographic information system (GIS). 4.1 Bicycle Volumes 4.1.1 Manual Counts by Bicycle Facility Type The count locations by street functional class and bicycle infrastructure type are shown in Table 4.1. The sample includes observations from each type of street both with and without bicycle facilities, except for principal arterials, where no bicycle facilities exist and bicycle and pedestrian traffic is prohibited or discouraged. In general, the number of samples from each type of street is comparable, and the numbers of observations are sufficient for analysis. One-third of all count locations occurred on A-minor roads of which two-thirds did not have any bicycle infrastructure. Fifteen percent of count locations were on B-minor streets, 18 percent were on local roads, and 20 percent were on major collectors. There were 29 trail locations that did not have a corresponding road. These included off-street bicycle paths as well as pedestrian and bicycle bridges. Table 4.1. Count location breakout by bicycle infrastructure. Bike Lane None On-Street Bike Lane Off-Street Shared lane Total
Count Principal Locations Arterial 164 3
AMinor 66
BMinor 20
Local Collector
Trail
36
39
0
39
0
11
16
5
7
0
33 4 240
0 0 3
2 0 79
0 0 36
0 3 44
2 1 49
29 0 29
Estimated 12-hour bicycle counts by bicycle facility type are given in Table 4.2. The estimated 12-hour counts are based on the 2-hour field observations by DPW and TLC. Each count was scaled to a 12-hour count based on scaling factors described in Chapter 5. These data show that bicycle traffic volumes are highest on off-street trails followed by on-street bike lanes and shared lanes. The differences of mean bicycle traffic volumes between streets with bicycle facilities and streets without bicycle facilities are all statistically significant. Table 4.3 shows estimated 12hour bicycle counts by street type stratified by the presence of a bicycle facility. Bicycle traffic volumes were higher for streets with bicycle facilities regardless of street type, however, the effect was most pronounced for minor arterials.
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Table 4.2. 12-hour estimated bicycle counts (6:30am – 6:30pm). Twelve Hour Estimated Counts Count Maximum Mean Median Minimum Average Hourly
OffStreet
On-Street Bike Lane
Shared Lane
None
All
100 6,701 837* 770 20 70
81 3,138 566* 301 41 47
5 964 450* 395 71 38
272 3,394 362 220 0 30
458 6,701 502 269 0 42
*Statistically significant from no bike infrastructure mean (p
