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Estimating Pedestrian Volumes
Robert J. SchneiderSafe Transportation Education & Research Center (SafeTREC)—January
2010
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Overview
• Why are pedestrian volumes important?• Alameda County pedestrian counting
methods• Extrapolating short counts to weeks and
years• Estimating volumes from a model
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
Pedestrian Crash Analysis
Mainline Roadway
Intersecting Roadway
Reported Pedestrian
Crashes (1996-2005)
Mission Boulevard
Torrano Avenue 5
Davis Street Pierce Avenue 4Foothill Boulevard D Street 1Mission Boulevard
Jefferson Street 5
University Avenue Bonar Street 7International Boulevard 107th Avenue 2San Pablo Avenue Harrison Street 2East 14th Street
Hasperian Boulevard 1
International Boulevard 46th Avenue 3
Solano AvenueMasonic Avenue 2
Broadway 12th Street 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 60,796 607,964 5 82.24
Davis Street Pierce Avenue 1,570 81,619 816,187 4 49.01Foothill Boulevard D Street 632 32,862 328,624 1 30.43Mission Boulevard
Jefferson Street 5,236 272,246 2,722,464 5 18.37
University Avenue Bonar Street 11,175 581,113 5,811,127 7 12.05International Boulevard 107th Avenue 3,985 207,243 2,072,429 2 9.65San Pablo Avenue Harrison Street 4,930 256,357 2,563,572 2 7.80East 14th Street
Hasperian Boulevard 3,777 196,410 1,964,102 1 5.09
International Boulevard 46th Avenue 12,303 639,752 6,397,522 3 4.69
Solano AvenueMasonic Avenue 22,203 1,154,559 11,545,589 2 1.73
Broadway 12th Street 112,896 5,870,590 58,705,898 5 0.85
2) Alameda County Pedestrian Counting Methods
• Manual Counts– Field data collectors & count sheets– Short time periods
• Automated Counts– Sensor technology– Continuous counts
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
Example: Broadway & 2nd Street
Google Earth—Tele Atlas 2008
Pedestrian Screenline/Segment Counts
Example: Broadway & 2nd Street
Google Earth—Tele Atlas 2008
Pedestrian Midblock Crossing Counts
Example: Broadway & 2nd Street
Google Earth—Tele Atlas 2008
Pedestrian Intersection Crossing Counts
Example: Broadway & 2nd Street
Google Earth—Tele Atlas 2008
Pedestrian Intersection Crossing Counts
Intersection Count Form (Pedestrians)
Example: Broadway & 2nd Street
Google Earth—Tele Atlas 2008
Bicyclist Intersection Turning Counts
Right
Straight
Left
Intersection Count Form (Bicyclists)
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
Automated Counters
www.eco-compteur.com
Percent of Weekly Volume by Hour (Composite of 13 Automated Count Sites)
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Raw Counter Data
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
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
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“Typical” Pedestrian Activity Pattern vs. Employment Centers
“Typical” Pedestrian Activity Pattern vs. Employment Centers
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“Typical” Pedestrian Activity Pattern vs. Employment Centers
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Composite of 13 Locations12 to 2 p.m., Wednesday=
2.16% of total weekly volume
Employment Centers12 to 2 p.m., Wednesday =
2.63% of total weekly volume
Land Use Adjustment Factors
Land Use Category DefinitionWeekday12-2 p.m.
Weekday2-4 p.m.
Weekday3-5 p.m.
Weekday4-6 p.m.
Saturday9-11 a.m.
Saturday12-2 p.m.
Saturday3-5 p.m.
Employment Center >=2,000 jobs within 0.25 miles (402 m)4 0.83 0.97 0.99 0.99 1.16 1.00 1.07
Residential Area>=500 jobs within 0.25 miles (402 m)4 & no
commercial retail properties within 0.1 miles (161 m)5 1.37 0.96 0.90 0.98 0.86 1.14 1.12
Neighborhood Commercial Area
>=10 commercial retail properties within 0.1 miles
(161 m)5 0.92 1.00 1.00 0.97 1.04 0.77 0.78
Near Multi-Use Trail
>=0.5 centerline miles of multi-use trails within 0.25
miles (402 m)6 1.63 0.79 0.72 0.91 0.69 1.31 1.07
Near School>=1 elementary, middle, or high school within 0.25
miles (402 m)5 0.94 0.77 0.82 1.07 1.20 1.23 1.37
Count Times when Adjustment Factors were Applied
Counts taken at locations with specific types of land uses were multiplied by these factors to match counts taken at “typical” Alameda County Locations
Weather Adjustment Factors
Counts taken under certain weather conditions were multiplied by these factors to match counts taken during “typical” Alameda County weather conditions
Weather Condition Definition
Warm>=80 degrees Fahrenheit (27 degrees Celsius) during
first count hour7
Cool<=50 degrees Fahrenheit (10 degrees Celsius) during
first count hour7
Cloudy<= 0.6 of the expected solar radiation (Langleys per
day) during first count hour7,8
Rain>=0.01 inch (0.254 mm) of precipitation during either
count hour7
Saturday9 a.m.-5 p.m.
Count Times when Adjustment Factors were Applied
1.07
Weekday12-6 p.m.
1.10
1.11
1.27
1.12
1.06
1.11
1.34
1
Effect of Rain on Pedestrian Volumes: Weekend vs. Weekday
-61%
2%
-24%
1%
-70%
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-50%
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-20%
-10%
0%
10% Rain
No rain
Sunday 12PM
Weekday 8AM
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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
Land Use Category Definition
Employment Center >=2,000 jobs within 0.25 miles (402 m)4
Residential Area>=500 jobs within 0.25 miles (402 m)4 & no
commercial retail properties within 0.1 miles (161 m)5
Neighborhood Commercial Area
>=10 commercial retail properties within 0.1 miles
(161 m)5
Near Multi-Use Trail
>=0.5 centerline miles of multi-use trails within 0.25
miles (402 m)6
Near School>=1 elementary, middle, or high school within 0.25
miles (402 m)5 0.93
Count Times when Adjustment Factors were Applied
All Time Periods
0.98
0.97
0.98
0.91
Example: Estimate the Annual Pedestrian Volume at an
Intersection• Telegraph Ave. & 27th St. (Oakland)• 2-hour pedestrian count (Tue., 5/26, 2-4
p.m.)• 65 degrees, cloudy• Total crossings of all legs = 212
Example: Estimate the Annual Pedestrian Volume at an
Intersection• Telegraph Ave. & 27th 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
Example: Estimate the Annual Pedestrian Volume at an
Intersection• Telegraph Ave. & 27th 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
Example: Estimate the Annual Pedestrian Volume at an
Intersection• Telegraph Ave. & 27th 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
Example: Estimate the Annual Pedestrian Volume at an
Intersection• Telegraph Ave. & 27th 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
Example: Estimate the Annual Pedestrian Volume at an
Intersection• Telegraph Ave. & 27th 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
Example: Estimate the Annual Pedestrian Volume at an
Intersection• Telegraph Ave. & 27th 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
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
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 R2=0.897Independent variables significant at 95% confidence level
Model Spreadsheet
Model Spreadsheet
ID # Mainline Roadway Intersecting Roadway City
Total population within 1/2-mile
radius3
Total employment within 1/4-mile radius
Total number of commercial properties within 1/4-mile radius
Presence of regional transit station within 1/10 mile (Yes = 1, No = 0)
Estimated Pedestrian Crossings
in a Typical Week5,6,7
1 Telegraph Avenue 16th Street Oakland 10720 16440 86 0 495042 Telegraph Avenue 27th Street Oakland 9780 3100 66 0 174493 Telegraph Avenue 51st Street Oakland 8970 970 41 0 95734 Telegraph Avenue 59th Street Oakland 10270 610 27 0 8613
Model OutputIntersection Identification Model Inputs 4
Pedestrian Intersection Crossing Volume ModelPilot Model--January 20091,2
Developed by Robert Schneider, Lindsay Arnold, and David RaglandUniversity of California-Berkeley Traffi c Safety Center
Pilot Pedestrian Volume Model
Application
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.
Thank you
• Lindsay Arnold & David Ragland (SafeTREC)
• Alameda County Transportation Improvement Authority
• California Department of Transportation
• Volunteer counters & SafeTREC students
Questions?
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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eDaily Pedestrian Volume on Broadway in Downtown Oakland
Daily Pedestrian Volume (Feb. 16-22, 2009) Typical Daily Pedestrian Volume
Rained 0.59" Rained 0.96"No Rain
Rained 1.65"
No Rain
No Rain
No Rain
Rain totals are for Feb. 16-22 April 2008 to April 2009
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 35and 57 percent lower than the average volume for the same hour of the weekover the entire year. The effect of rain may be greater on weekends becausemore trips may be discretionary.
Cloud cover (5 to 24 percent reduction)Pedestrian volumes collected when it was cloudy tended to be between 5 and24 percent lower than typical volumes during the same hour of the week overthe entire year. The effect of clouds may be greater on weekends due todiscretionary trips.
Warm temperatures (slight reduction)Pedestrian counts taken between 12 p.m. and 1 p.m. on Saturdays showedthat each additional degree Fahrenheit was associated with one percent lowerpedestrian volume. Two weekday models showed that pedestrian volumes
maybe 5 to 8 percent lower than average when the temperature is above 80degrees Fahrenheit (27 Celsius).
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
degreesFahrenheit (10 Celsius) were associated with lower pedestrian
volumes.
High winds (slight reduction)The weekday mid-day model showed that higher winds were
associated withlower pedestrian volumes.
*Results are from Alameda County, CA (very mild climate)
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
Variation in Pedestrian Volumes
• 5 Control Intersections
ID #
2008 Weekly Pedestrian Volume
based on Counts
2009 Weekly Pedestrian Volume
based on CountsAbsolute Difference
(2009 - 2008) Percent Difference1
50 315 310 -5 1.6%2650 15691 16113 422 2.7%9179 8342 7429 -913 12.3%9436 105297 88118 -17179 19.5%
499 5186 3448 -1738 50.4%1) Percent difference is calculated using the smaller number as the base value. If the model value is greater than the actual value, the percent difference is calculated as (2009 - 2008)/2008. If the actual value is greater than the model value, the percent difference is calculated as (2008 - 2009)/2009.
2009 Observed Volumes vs. Pilot Model Predictions
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Weekly Pedestrian Volume "Observed" in 2009
Trendline forObserved vs.
Predicted Data
Line Representing Perfect Prediction (Observ. = Pred.)
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
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 R2=0.900Independent variables significant at 90% confidence level
Which Intersection Features are Associated with Pedestrian Risk?
Pedestrian Crossings (+) While intersections with more pedestriancrossings 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”
effectwith mainline motor vehicle volume, but
needto explore the influence of congestion andspeed.
(Expected Effect*: 100% more pedestrian crossings, 49% more crashes)
(Expected Effect*: 100% more mainline AADT, >100% more crashes)
Which Intersection Features are Associated with Pedestrian Risk?
Number of Right-Turn-Only Lanes (+) Intersections with more right-turn-only lanesmay have longer crossing distances and
morecomplex interactions between drivers andpedestrians.
Number of Driveway Crossings (+) Intersections with more non-residentialdriveway crossings within 50 ft. may havemore conflict points; drivers may focus onentering or exiting motor vehicle lanes.
Medians (-) Mainline and cross-street legs with medianshave a refuge that allows pedestrians tocross one direction of traffic at a time, whichmay 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)
Which Intersection Features are Associated with Pedestrian Risk?
Number of Commercial Properties (+) Intersections with more commercialproperties within 0.1 miles may have
moredrivers looking at signs and for parking;
morepedestrians may cross between cars.
Percentage of Residents Under 18 (+) A greater percentage of young
pedestrianswithin 0.25 miles may indicate that more
ofthe people crossing are less experienced
andhave higher risk crossing busy streets.
(Expected Effect*: 100% more pedestrian crossings, 49% more crashes)
(Expected Effect*: 100% more mainline AADT, >100% more crashes)
Recommended