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Estimating Pedestrian Volumes Robert J. Schneider Safe Transportation Education & Research Center (SafeTREC)—January 2010.

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Presentation on theme: "Estimating Pedestrian Volumes Robert J. Schneider Safe Transportation Education & Research Center (SafeTREC)—January 2010."— Presentation transcript:

1 Estimating Pedestrian Volumes Robert J. Schneider Safe Transportation Education & Research Center (SafeTREC)—January 2010

2 Overview Why are pedestrian volumes important? Alameda County pedestrian counting methods Extrapolating short counts to weeks and years Estimating volumes from a model

3 1) Why are Pedestrian Volumes Important? Track pedestrian volume over time Quantify exposure to calculate pedestrian crash risk See where & when pedestrian activity occurs City of Portland, OR

4 Pedestrian Crash Analysis Mainline Roadway Intersecting Roadway Reported Pedestrian Crashes (1996-2005) Mission Boulevard Torrano Avenue5 Davis StreetPierce Avenue4 Foothill BoulevardD Street1 Mission Boulevard Jefferson Street5 University AvenueBonar Street7 International Boulevard107th Avenue2 San Pablo AvenueHarrison Street2 East 14th Street Hasperian Boulevard1 International Boulevard46th Avenue3 Solano Avenue Masonic Avenue2 Broadway12 th Street5

5 Pedestrian Risk Analysis Mainline Roadway Intersecting Roadway Estimated Total Weekly Pedestrian Crossings Annual Pedestrian Volume Estimate Ten-Year Pedestrian Volume Estimate Reported Pedestrian Crashes (1996-2005) Pedestrian Risk (Crashes per 10,000,000 crossings) Mission Boulevard Torrano Avenue 1,169 60,796 607,964582.24 Davis StreetPierce Avenue 1,570 81,619 816,187449.01 Foothill BoulevardD Street 632 32,862 328,624130.43 Mission Boulevard Jefferson Street 5,236 272,246 2,722,464518.37 University AvenueBonar Street 11,175 581,113 5,811,127712.05 International Boulevard107th Avenue 3,985 207,243 2,072,42929.65 San Pablo AvenueHarrison Street 4,930 256,357 2,563,57227.80 East 14th Street Hasperian Boulevard 3,777 196,410 1,964,10215.09 International Boulevard46th Avenue 12,303 639,752 6,397,52234.69 Solano Avenue Masonic Avenue 22,203 1,154,559 11,545,58921.73 Broadway12 th Street112,8965,870,59058,705,89850.85

6 2) Alameda County Pedestrian Counting Methods Manual Counts – Field data collectors & count sheets – Short time periods Automated Counts – Sensor technology – Continuous counts

7 Alameda County, CA Key Partner = ACTIA Population = 1.46 million Land area = 738 square miles Largest City = Oakland (401,000) San Francisco Oakland Alameda County 40 miles

8 Example: Broadway & 2 nd Street Google Earth—Tele Atlas 2008 Pedestrian Screenline/Segment Counts

9 Example: Broadway & 2 nd Street Google Earth—Tele Atlas 2008 Pedestrian Midblock Crossing Counts

10 Example: Broadway & 2 nd Street Google Earth—Tele Atlas 2008 Pedestrian Intersection Crossing Counts

11 Example: Broadway & 2 nd Street Google Earth—Tele Atlas 2008 Pedestrian Intersection Crossing Counts

12 Intersection Count Form (Pedestrians)

13 Example: Broadway & 2 nd Street Google Earth—Tele Atlas 2008 Bicyclist Intersection Turning Counts Right Straight Left

14 Intersection Count Form (Bicyclists)

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16 Manual Count Resource Needs Site selection (strategic analysis) $200-$300 per 2-hour pedestrian & bicycle count – Data collector training & management – Travel to site – Count period – Most sites = 1 data collector (except high volume) Data entry & cleaning (15-20 min. per 2-hour count) Cost reduction – Use existing count methods and forms – Use interns & volunteers (cautiously!) – Repeat at regular intervals

17 Automated Counters www.eco-compteur.com

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19 Percent of Weekly Volume by Hour (Composite of 13 Automated Count Sites)

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21 Raw Counter Data

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24 Automated Count Resource Needs $2,000-$2,500 per EcoCounter Infrared Sensor Budget time for permission & travel to location 10-15 min. to install (follow instructions) 5-10 min. to download data in field (need software) Data cleaning & analysis – Search data for anomalies – Summarize data in meaningful way – Repeat at regular intervals Alternatives: Other sensors, video

25 3) Extrapolating Short Pedestrian Counts Calculated extrapolation factors from continuous pedestrian counts – Time of day, day of week, season of year – Land use – Weather Identified “peak” pedestrian activity Derived from 13 locations in Alameda County

26 “Typical” Pedestrian Activity Pattern vs. Employment Centers

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29 Land Use Adjustment Factors Counts taken at locations with specific types of land uses were multiplied by these factors to match counts taken at “typical” Alameda County Locations

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31 Weather Adjustment Factors Counts taken under certain weather conditions were multiplied by these factors to match counts taken during “typical” Alameda County weather conditions

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33 Seasonal Adjustment Factors Counts taken during the spring were multiplied by these factors to match counts taken in Alameda County during a typical time of the year

34 Example: Estimate the Annual Pedestrian Volume at an Intersection Telegraph Ave. & 27 th St. (Oakland) 2-hour pedestrian count (Tue., 5/26, 2-4 p.m.) 65 degrees, cloudy Total crossings of all legs = 212

35 Example: Estimate the Annual Pedestrian Volume at an Intersection Telegraph Ave. & 27 th St. (Oakland) 2-hour pedestrian count (Tue., 5/26, 2-4 p.m.) 65 degrees, cloudy Total crossings of all legs = 212 – Extrapolate to typical week: Multiply by 42.54

36 Example: Estimate the Annual Pedestrian Volume at an Intersection Telegraph Ave. & 27 th St. (Oakland) 2-hour pedestrian count (Tue., 5/26, 2-4 p.m.) 65 degrees, cloudy Total crossings of all legs = 212 – Extrapolate to typical week: Multiply by 42.54 – Extrapolate to typical year: Multiply by 52.18

37 Example: Estimate the Annual Pedestrian Volume at an Intersection Telegraph Ave. & 27 th St. (Oakland) 2-hour pedestrian count (Tue., 5/26, 2-4 p.m.) 65 degrees, cloudy Total crossings of all legs = 212 – Extrapolate to typical week: Multiply by 42.54 – Extrapolate to typical year: Multiply by 52.18 – Account for spring count: Multiply by 0.981

38 Example: Estimate the Annual Pedestrian Volume at an Intersection Telegraph Ave. & 27 th St. (Oakland) 2-hour pedestrian count (Tue., 5/26, 2-4 p.m.) 65 degrees, cloudy Total crossings of all legs = 212 – Extrapolate to typical week: Multiply by 42.54 – Extrapolate to typical year: Multiply by 52.18 – Account for spring count: Multiply by 0.981 – Account for employment & commercial retail land uses: Multiply by 0.97 and by 1.002

39 Example: Estimate the Annual Pedestrian Volume at an Intersection Telegraph Ave. & 27 th St. (Oakland) 2-hour pedestrian count (Tue., 5/26, 2-4 p.m.) 65 degrees, cloudy Total crossings of all legs = 212 – Extrapolate to typical week: Multiply by 42.54 – Extrapolate to typical year: Multiply by 52.18 – Account for spring count: Multiply by 0.981 – Account for employment & commercial retail land uses: Multiply by 0.97 and by 1.002 – Account for cloudy weather: Multiply by 1.06

40 Example: Estimate the Annual Pedestrian Volume at an Intersection Telegraph Ave. & 27 th St. (Oakland) 2-hour pedestrian count (Tue., 5/26, 2-4 p.m.) 65 degrees, cloudy Total crossings of all legs = 212 – Extrapolate to typical week: Multiply by 42.54 – Extrapolate to typical year: Multiply by 52.18 – Account for spring count: Multiply by 0.981 – Account for employment & commercial retail land uses: Multiply by 0.97 and by 1.002 – Account for cloudy weather: Multiply by 1.06 – Estimated annual pedestrian crossings ~ 475,000

41 4) Estimating Pedestrian Volumes from Statistical Models Developed model from counts at 50 intersections in Alameda County Identified factors associated with higher vols. – Total population within 0.5 mi – Total employment within 0.25 mi – Number of commercial retail properties within 0.25 mi – Presence of regional rail station within 0.1 mi Created simple spreadsheet for applying model

42 Pilot Model Formula Estimated Weekly Pedestrian Crossings = 0.928* Total population within 0.5-miles of the intersection + 2.19 *Total employment within 0.25-miles of the intersection + 98.4 *Number of commercial properties within 0.25-miles of the intersection +54,600 * Number of regional transit stations within 0.10-miles of the intersection - 4910 (Constant) Adjusted R 2 =0.897 Independent variables significant at 95% confidence level

43 Model Spreadsheet

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45 Pilot Pedestrian Volume Model Application

46 Considerations for Applying the Model Designed for estimating volumes at neighborhood, corridor, and community levels. Actual pedestrian counts should be used for site-level safety, design, and engineering analyses.

47 Thank you Lindsay Arnold & David Ragland (SafeTREC) Alameda County Transportation Improvement Authority California Department of Transportation Volunteer counters & SafeTREC students

48 Questions?

49 EcoCounter Validation Counts Prior Studies: – Shawn Turner, et al. (2007), "Testing and Evaluation of Pedestrian Sensors", http://swutc.tamu.edu/publications/technicalreports/167762- 1.pdf – Ryan Greene-Roesel, et al. (2008), “Effectiveness of a Commercially Available Automated Pedestrian Counting Device in Urban Environments: Comparison with Manual Counts”, http://www.tsc.berkeley.edu/news/08-0503session240ryanposter.pdf High and low pedestrian volumes Different sidewalk widths Different weather conditions

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51 How Does Weather Affect Pedestrian Volume?* (Linear model from Attaset, Schneider, Arnold, & Ragland, 2009) Rain (35 to 57 percent reduction) Pedestrian counts during hours with measurable rain tended to be between 35 and 57 percent lower than the average volume for the same hour of the week over the entire year. The effect of rain may be greater on weekends because more trips may be discretionary. Cloud cover (5 to 24 percent reduction) Pedestrian volumes collected when it was cloudy tended to be between 5 and 24 percent lower than typical volumes during the same hour of the week over the entire year. The effect of clouds may be greater on weekends due to discretionary trips. Warm temperatures (slight reduction) Pedestrian counts taken between 12 p.m. and 1 p.m. on Saturdays showed that each additional degree Fahrenheit was associated with one percent lower pedestrian volume. Two weekday models showed that pedestrian volumes may be 5 to 8 percent lower than average when the temperature is above 80 degrees Fahrenheit (27 Celsius).

52 How Does Weather Affect Pedestrian Volume?* (Linear model from Attaset, Schneider, Arnold, & Ragland, 2009) Cool temperatures (slight reduction) The weekday afternoon model showed that temperatures below 50 degrees Fahrenheit (10 Celsius) were associated with lower pedestrian volumes. High winds (slight reduction) The weekday mid-day model showed that higher winds were associated with lower pedestrian volumes. *Results are from Alameda County, CA (very mild climate)

53 Pilot Pedestrian Volume Model Testing Found that some counts were close to predicted values, but others were more than 50% off Proposed alternative model specifications based on validation counts Other pedestrian volume models – Cameron (1976)—Manhattan – Benham & Patel (1977)—Milwaukee CBD – Desyllas, et al. (2003)—Central London – Raford & Ragland (2004, 2005)— Oakland, CA; Boston, MA – Pulugurtha & Repaka (2008)—Charlotte – Clifton, et al. (2008)—Maryland Cities – Liu & Griswold (2009)—San Francisco

54 Variation in Pedestrian Volumes 5 Control Intersections

55 2009 Observed Volumes vs. Pilot Model Predictions

56 Possible Model Formulations 1) New Alameda County Model – Correlation between employment & vehicle ownership – Distance to closest school 2) Model with Total Population Squared – Correlation between employment & vehicle ownership – Five factors significant at 99% confidence level – RMSE = 4,470; RMSPE = 7,480 3) Revised Pilot Model – Four key factors are significant in most models estimated

57 Revised Pilot Model Formula Estimated Weekly Pedestrian Crossings = 0.987* Total population within 0.5-miles of the intersection + 2.19 *Total employment within 0.25-miles of the intersection + 71.1 *Number of commercial properties within 0.25-miles of the intersection +49,300 * Number of regional transit stations within 0.10-miles of the intersection - 4850 (Constant) Adjusted R 2 =0.900 Independent variables significant at 90% confidence level

58 Which Intersection Features are Associated with Pedestrian Risk? Pedestrian Crossings (+) While intersections with more pedestrian crossings have more pedestrian crashes, there may be a “safety in numbers” effect (i.e., lower crash risk per crossing). Motor Vehicle Volume (+) There may be a “danger in numbers” effect with mainline motor vehicle volume, but need to explore the influence of congestion and speed. (Expected Effect*: 100% more pedestrian crossings, 49% more crashes) (Expected Effect*: 100% more mainline AADT, >100% more crashes)

59 Which Intersection Features are Associated with Pedestrian Risk? Number of Right-Turn-Only Lanes (+) Intersections with more right-turn-only lanes may have longer crossing distances and more complex interactions between drivers and pedestrians. Number of Driveway Crossings (+) Intersections with more non-residential driveway crossings within 50 ft. may have more conflict points; drivers may focus on entering or exiting motor vehicle lanes. Medians (-) Mainline and cross-street legs with medians have a refuge that allows pedestrians to cross one direction of traffic at a time, which may make crossing safer. (Expected Effect*: 1 more right-turn-only lane, 53% more crashes) (Expected Effect*: 1 more driveway crossing, 33% more crashes) (Expected Effect*: Medians on mainline roadway crossings, 75% fewer crashes)

60 Which Intersection Features are Associated with Pedestrian Risk? Number of Commercial Properties (+) Intersections with more commercial properties within 0.1 miles may have more drivers looking at signs and for parking; more pedestrians may cross between cars. Percentage of Residents Under 18 (+) A greater percentage of young pedestrians within 0.25 miles may indicate that more of the people crossing are less experienced and have higher risk crossing busy streets. (Expected Effect*: 100% more pedestrian crossings, 49% more crashes) (Expected Effect*: 100% more mainline AADT, >100% more crashes)


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