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Sweat the Details!Tips for Pedestrian and Bicycle Counting
Bob Schneider, UC Berkeley Safe Transportation Research & Education Center ProWalk/ProBike Conference 2010
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Bicy
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ycle
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outh
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cycl
e la
ne
Telegraph Ave. Southbound Bicycle Loop Counts--Typical Week (Mar., Jun., Sep. 2009)
March
June
September
M T W Th F Sa Su
Pedestrian and Bicycle Counting Tips
• Manual counts• Automated counts• Pedestrian and bicycle count applications
Overall advice: Count with a purpose
• Identify possible uses of count data before starting• Possible purposes:
– Track trends in walking & bicycling over time– Evaluate crash risk at specific locations– Show the effect of specific projects/programs on use or
safety (before and after studies)– Demonstrate that there are many people walking and
bicycling– Develop pedestrian or bicycle volume models
Manual Counts
Tip 1: Train the Data Collectors
“Why do we need training?
...It’s just counting people walking and bicycling!”
We Need Consistent, Reliable Counts
• Accuracy is most important• Counts will be used by transportation & planning
agencies, advocates, researchers• Counting is easy. • Counting accurately & consistently is the
challenge.• Data collectors get better with experience.
Several Different Ways to Count
• “Intersection” − Where two roadways cross
• “Screenline” or “Segment”− Along sidewalk/roadway segment− National Documentation Project
• “Mid-block”− Crossing in the middle of the block, away from the intersection
Google Earth—Tele Atlas 2008
Example
Google Earth—Tele Atlas 2008
Pedestrian Midblock Crossing Counts
Example
Google Earth—Tele Atlas 2008
Pedestrian Segment/Screenline Counts
Example
Example
Google Earth—Tele Atlas 2008
Pedestrian Intersection Crossing Counts
Example
Google Earth—Tele Atlas 2008
Pedestrian Intersection Crossing Counts
Google Earth—Tele Atlas 2008
Right
Straight
Left
Bicyclist Intersection Turning Counts
Example
Google Earth—Tele Atlas 2008
Right
Straight
Left
Bicyclist Intersection Turning Counts
Example
Questions in Data Collectors’ MindsEliminate them.
• Who is a pedestrian?– Baby in Dad’s arms? Skateboarder? Person walking a
bike?
• Who is a bicyclist?– Moped rider? Person walking a bike?
• When does a pedestrian get counted?– Jaywalking? Turning right around the corner?
• When does a bicyclist get counted?– Riding on sidewalk? Turning right around the corner?
Tip 2: Choose a Good Count Form(or recording device)
National Documentation Project Screenline Count Form
Pedestrian Intersection Count Form
Pedestrian Intersection Count Form (“Maddox”)
Informal Experiment
Group Using Original Group Using “Maddox”
Bicycle Intersection Count Form
Google Earth—Tele Atlas 2008
Right
Straight
Left
Bicyclist Intersection Turning Counts
Bicycle Turning Counts (Complex)
Google Earth—Tele Atlas 2008
Approaching from Leg C
Bicyclist Intersection Counts
Bicycle Approach Counts (Simple)
Google Earth—Tele Atlas 2008
Approaching from Leg C
Bicyclist Intersection Counts
Approaching from Leg A
Approaching from Leg D
Approaching from Leg B
Bicycle Approach Counts (Simple)
Tip 3: Identify locations that need more than one data collector in advance
When do you need more than one data collector?
• Rule of thumb: 400-500 pedestrians per hour is upper limit of single data collector for intersections
• Greater mix of pedestrians & bicyclists requires more attention/more data collectors
Tip 4: Prioritize data items so that most important information is collected
Essential
Important
Optional
Possible Data Priority Ranking
1) Count of pedestrians2) Count of bicyclists3) Gender4) Helmet Use5) Pedestrian Crossing Direction6) Bicyclist Turning Movement
Automated Counts
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Bicy
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ycle
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Telegraph Ave. Southbound Bicycle Loop Counts , Feb. to Nov. 2009
February March April May June July August September October Nov
Tip 1: Understand the type of data that the automated counter will provide
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Perc
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“Typical” Alameda County Pedestrian Activity Pattern (13 sites)
In-Pavement Loop Detectors
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Telegraph Ave. Southbound Bicycle Loop Counts , Feb. to Nov. 2009
February March April May June July August September October Nov
UC-Berkeley Summer Break
Rain
Rain
Bike To Work Day
Bicycle Lane Volume Pattern (Alameda County Site)
Tip 2: Review raw data and correct anomalies
Tip 3: Understand and correct for undercounting
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Man
ual C
ount
(15
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ute
peri
od)
Automated Count (15-minute period)
Automated Counts vs. Manual Counts (15-minute periods)
Manual = Automated
LineValidation counts taken in Alameda County and San Francisco, CA. Included locations with different sidewalk widths, temperature, precipitation.
y = 0.393x1.2672
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ual C
ount
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Automated Count (15-minute period)
Automated Count to Manual Count Conversion Function
For Automated Counts > 49:
ConversionFunction
Manual = Automated
Line
For Automated Counts < 49:y = 1.1x
Undercounting is likely to depend on the width and design of the sidewalk in addition to the volume of pedestrians. However, this is an early attempt to develop a general conversion function.
Tip 4: Use data to develop adjustment(extrapolation) factors
• Time of day, day of week, season of year
• Land use• Weather
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“Typical” Alameda County Pedestrian Activity Pattern (13 sites)
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“Typical” Alameda County Pedestrian Activity Pattern (13 sites)
2-hour count period
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“Typical” Pedestrian Activity Pattern vs. Employment Centers
“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
“Typical” Pedestrian Activity Pattern vs. Employment Centers
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
(Example: Alameda County, CA)
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
(Example: Alameda County, CA)
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: Alameda County, CA)
Seasonal Adjustment Factors
Each month has a different proportion of the total annual pedestrian or bicycle volume
(Example: National Documentation Project)
Applications of Count Data
• Analyze crash risk– Prioritize locations for safety treatments– Improve roadway designs & CRFs
• Develop predictive volume models• Track progress over time
Analyze Crash Risk
Alameda County 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
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
Alameda County Pedestrian Risk Analysis
Alameda County Pedestrian Volume ModelEstimated 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 = 0.900Root Mean Squared Error = 5310Independent variables significant at 93% confidence interval
Pilot Pedestrian Volume Model Application
Source: New York City DOT, 2010
New York City Bicycle Counts (1980-2009)
Source: City of Seattle
Seattle Bicycle Counts
Source:
Community withPedestrian Counts?
?
Questions & Answers