Session #7 - Pedestrian & Bicycle Counting Tips - Schneider

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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Telegraph Ave. Southbound Bicycle Loop Counts--Typical Week (Mar., Jun., Sep. 2009)

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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


Pedestrian Midblock Crossing Counts

Example


Pedestrian Segment/Screenline Counts

Example

Example


Pedestrian Intersection Crossing Counts


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


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Bicyclist Intersection Turning Counts

Bicycle Turning Counts (Complex)


Approaching from Leg C

Bicyclist Intersection Counts

Bicycle Approach Counts (Simple)


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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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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“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

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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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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.

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Automated Count to Manual Count Conversion Function

For Automated Counts > 49:

ConversionFunction

Manual = Automated

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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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2-hour count period

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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

(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.


1.07

Weekday12-6 p.m.

1.10

1.11

1.27

1.12

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1.11

1.34


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


All Time Periods

0.98

0.97

0.98

0.91


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

Session #7 - Pedestrian & Bicycle Counting Tips - Schneider

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Transcript of Session #7 - Pedestrian & Bicycle Counting Tips - Schneider