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National Bicycle & Pedestrian Documentation Project Alta Planning + Design with Institute of Transportation Engineers (ITE) Charlie Denney, Alta Planning + Design 1 Outline What is the National Bicycle Pedestrian Documentation Project (NBPD)? Technology for Your Count Extrapolating Counts 2 What is NBPD? A pro bono effort by Alta Planning + Design with support from ITE Annual bicycle and pedestrian count and survey effort Fulfill need for in-depth analysis of factors why people walk and bike Objectives Consistent data collection Open data access Shared research 3 Need for NBPD Lack of consistent data Non-motorized modes lack of funding Analysis for other modes are based on marginal data yet receive substantive funding 4 Accomplishments Counts from over 500 locations in 60 communities Adjust counts done almost any period on multi-use paths and pedestrian districts to an annual figure Related projects include Washington State Bicycle and Pedestrian Documentation Project (http://wsdot.wa.gov/bike/Count.htm) Arlington County, VA Seamless Travel in San Diego County 4 Federally funded Non-Motorized Transportation Pilot Projects New website: www.bikepeddocumentation.org 5 NBPD Count Dates and Times 2013 Official National Count/Survey Days Tuesday, September 10 through Thursday, September 12 Saturday, September 14 through Sunday, September 15 Recommended Times Weekday, 5-7 PM Saturday, 12 noon – 2PM Secondary Times Weekday, 7 AM to 7 PM Saturday, 7 AM to 7 PM Choosing a Location Historic count location Existing or proposed facility High collision area Smart growth, mix of land uses Transit access Bottleneck or pinch areas Stakeholder recommendations Example Forms Manual vs. Automatic Counts Count Effort Budget Duration of Count Effort Manual count person hours vs. cost of count machines Quarterly, bi-annual, yearly Year long Type of data Volume Behavior, i.e., helmet use, wrong-way riding Gender 9 Automatic Count Technologies Passive Infrared Detects change in thermal contrast Active Infrared Detects obstruction in beam Ultrasonic Emits ultrasonic wave and listens for echo Doppler Radar Emits radio wave and listens for change in frequency Video Imaging Analyzes pixel changes or Data is played by and analyzed by a person Piezometric Senses pressure on tube or underground sensor In-Pavement Magnetic Loop Sense change in magnetic field as metal passes over 10 Error Factors and Adjustments Error Factors All automatic count technologies have an error factor Error rates vary by technology Adjustments Typical calibration involves a comparison of manual and automatic counts From comparison a correction factor can be derived. 11 Technology for your Count Considerations Who are you counting? Technology cost Bicycles? Pedestrians? Both? Do you need to differentiate between bicyclists and pedestrians? What is your budget? Staff time cost What is your budget? 12 Data Access and Analysis Data can be used for: Demand projections Exposure analysis Estimate of benefits Trip generation Overall trends in activity Facility operation and design Land use and design 13 Monthly Variation: East/Midwest Multi-Use Paths: Monthly Variations in Use Monthly Use (% of Annual Total Use) 18.0% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Jan Feb Mar Apr Indianapolis (30 locations) May Jun Jul Aug Monon Trail (4 locations) Sep Oct Rhode Island Nov Dec Average 14 Daily Variation Multi-Use Paths: Daily Variations in Use % of Weekly Use 25% 20% 15% 10% 5% 0% Mon Tue Indianapolis (30 locations) Wed Thur Terry Hershey Park Trail (Houston) Fri Sat Outer Banks Sun Average 15 Weekday Hourly Variation Multi-Use Paths: Hourly Variations in Weekday Use 25% THershey Prk Trail (May-Oct) Monon Trail (Oct) Outerbanks Licking County (July) 15% Manhattan Bronx 10% Queens Brooklyn Staten Is 5% Average 8 9P M 7 6 5 4 3 2 1 no on 11 10 9 8 7 0% 6A M % of Daily Use 20% Starting Hour 16 Arlington, VA Started with manual counts Experimented with automated counters Full program with 28 automated counters Sharing with Washington, DC and NPS 17 Charles River Basin 18 Caltrans/San Diego Caltrans/TSC 2.5 year study 40 historic locations 40 new locations 80 total count locations AM weekday peak (all) Midday weekend peak (all) PM weekday peak (20 selected) 19 Analysis: Key Observed Patterns Significant hourly variation weekday/weekend variation monthly variation No generalized ‘peak’ period More variability in recreational travel patterns (vs. utilitarian travel) Accept variation as part of normal estimating process 20 Extrapolating Counts Standard technique used in travel demand modeling Utilize automatic 24 hour counts to determine hourly, daily, monthly and annual rates (adjustment factors) Extrapolate manual hourly counts to daily, monthly or annual estimates Extrapolating Counts We recommend a combination of automatic counts (annual) manual counts (hourly) Extrapolation Example Hourly Adjustment Factors Detailed instructions available online 4-5 PM Tuesday count Daily to weekly trips 100 bicycles Adjustment factor = .07 100/.07 = 1,429 1429 daily trips on Tuesday Adjustment factor = .13 1429/.13 = 10,992 Weekly to monthly 4.33 weeks per month 10,992 * 4.33 = 47,595 Other Uses - Estimating Demand Utilize count data to identify factors correlated with biking and walking Our research found Employment Density R = .976 Multi-use trail within ¼ mile R = .879 Conclusions/Next Steps Regional differences in seasonal patterns Unlike vehicle use patterns Estimate models will need regional factors Need for more 365-day count machines Climate Visitors To count pedestrians and on-street bicyclists Need for standardized counting methodology to yield improved data 25 Thank You More information: Alta Planning + Design www.altaplanning.com www.bikepeddocumentation.org Jennifer Donlon [email protected] Charlie Denney [email protected] Institute of Transportation Engineers http://www.ite.org/councils/Ped_Bike/trips.asp 26