Pedestrian counting

How To Do Your Own Pedestrian Count

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Dr. Robert Schneider, UC Berkeley SafeTREC Pedestrians Count! 2012Los Angeles, CA

Why Collect Pedestrian Volume Data?

Answer Practical Questions

Will more people walk? Can we make it safer?

Why are there higher pedestrian volumes in some locations and lower

volumes in others?

How many people use non-motorized facilities after they are constructed?

Where do pedestrian crashes occur? Where is the greatest risk?

Institutional Purposes

Use Data for Pedestrian Planning

Institutionalize Pedestrian & Bicycle Data

X-Street Traffic Volume

Mainline Traffic Volume

A multimodal transportation system requires collecting data for all modes of transportation

We need these data fields!

Institutionalize Pedestrian & Bicycle Data

X-Street Traffic Volume

Mainline Traffic Volume

X-Street Pedestrian Volume

Mainline Pedestrian Volume

A multimodal transportation system requires collecting data for all modes of transportation

Establish baseline for pedestrian & bicycle safety, infrastructure, volumes, etc.

Types of Pedestrian & Bicycle Data

Safety (Crashes, injuries,

behaviors)

User Characteristics

(Age, gender)

Exposure/ Volume

(Counts, mode share)

Infrastructure (Facility coverage

& quality)

Public Opinion

(Satisfaction, desires)

Two Main Sources of Volume Data

Surveys Pedestrian & Bicycle Counts

Volume Data Collection Methods


behaviors)


(Age, gender)

Exposure/ Volume


Surveys Counts


& quality)

Public Opinion


Types of Pedestrian & Bicycle Surveys


behaviors)


(Age, gender)


& quality)

Public Opinion


Exposure/ Volume


Surveys Counts

Household (Phone, mail,

internet) Intercept

Survey Data

National Household Travel Survey (NHTS) US Census/American Community Survey Regional household travel surveys Intercept surveys

9.3%

8.5%

7.2%

5.5%

8.7%

11.0%

0.7% 0.8% 0.7% 0.9% 0.8% 1.0%

0%

5%

10%

15%

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25%

30%

1977 1983 1990 1995 2001 2009

United States Pedestrian & Bicycle Mode Share (1977-2009 NHTS)

Walk

Bicycle

Note: National Household Travel Survey added question to prompt for forgotten walking trips in 2001

U.S. Shopping Trip Mode Share (2009)

(Home-Based Shopping Trips) Source: Federal Highway Administration, National Household Travel Survey, 2009.

Hayward

Pedestrian 8% Bicycle

1% Transit 2%

Automobile 89%

Types of Pedestrian & Bicycle Counts


behaviors)


(Age, gender)


& quality)

Public Opinion


Exposure/ Volume


Surveys Counts

Household (Phone, mail,

internet) Intercept

Manual (Intersection,

segment)

Automated (Loops, infrared)

Pedestrian and Bicycle Counting Tips

Manual counts Automated count technologies

Overall advice: Count with a purpose

IdenBfy possible uses of count data before starBng Possible purposes: Track trends in walking & bicycling over Bme Evaluate crash risk at specic locaBons Show the eect of specic projects/programs on use or safety (before and aNer studies)

Demonstrate that there are many people walking and bicycling

Develop pedestrian or bicycle volume models

Several Dierent Ways to Count

Intersec)on Where two roadways cross

Screenline or Segment Along sidewalk/roadway segment NaBonal DocumentaBon Project

Mid-block Crossing in the middle of the block, away from the intersecBon

Google EarthTele 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

Manual Counts

Tip 1: Train the Data Collectors

Why do we need training?

...Its just counBng people walking and bicycling!

We Need Consistent, Reliable Counts

Accuracy is most important. Counts will be used by transportaBon & planning agencies, advocates, researchers.

Coun)ng is easy. Coun)ng accurately & consistently is the challenge.

Data collectors get beZer with experience.

QuesBons in Data Collectors Minds Eliminate them.

Who is a pedestrian? Baby in Dads 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)

Pedestrian IntersecBon Count Form

National Documentation Project Screenline Count Form

Tip 3: IdenBfy locaBons 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 intersecBons

Greater mix of pedestrians & bicyclists requires more aZenBon/more data collectors

Tip 4: PrioriBze data items so that most important informaBon is collected

EssenBal

Important

OpBonal

Possible Data Priority Ranking

1) Count of pedestrians 2) Count of bicyclists 3) Gender 4) Helmet Use 5) Pedestrian Crossing DirecBon 6) Bicyclist Turning Movement

Other ConsideraBons

Where should you count?

Alameda County Example

Most are in Countywide Ped & Bike Plans In neighborhoods with a range of incomes 18 locations within -mile of a school 6 locations within -mile of BART Range of traffic volumes About of intersections have: Median islands Less than four lanes on mainline approaches No traffic signals

When are good time periods for counting?

Tuesday, Wednesday, and Thursday 4 p.m. to 6 p.m. 5 p.m. to 7 p.m. (National Documentation Project) 2 p.m. to 4 p.m. near schools

Saturday 9 a.m. to 11 a.m. 12 p.m. to 2 p.m. 3 p.m. to 5 p.m.

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 AcBvity PaZern (13 sites)

Tip 2: Review raw data and correct anomalies

Tip 3: Understand and correct for undercounBng

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Automated Count (15-minute period)

Automated Counts vs. Manual Counts (15-minute periods)

Manual = Automated

LineValida&on counts taken in Alameda County and San Francisco, CA. Included loca&ons with dierent sidewalk widths, temperature, precipita&on.

y = 0.393x1.2672

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Automated Count to Manual Count Conversion Function

For Automated Counts > 49:

ConversionFunction

Manual = Automated

Line

For Automated Counts < 49:y = 1.1x

Undercoun&ng is likely to depend on the width and design of the sidewalk in addi&on to the volume of pedestrians. However, this is an early aCempt to develop a general conversion func&on.

Tip 4: Use data to develop adjustment (extrapolaBon) factors

Time of day, day of week, season of year

Land use Weather

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2-hour count period

Challenges to Collecting Pedestrian & Bicycle Data

Limited funding and staff time Concerns about unwanted results Limited agency capacity for data

collection, in general Difficulty with data collection

departments

2-Hour Pedestrian and Bicyclist Counts Mainline Roadway Intersec)ng Roadway Weekday

Ped Count Saturday Ped Count

Weekday Bike Count

Saturday Bike Count

Mission Boulevard Torrano Avenue 16 28 3 8

Davis Street Pierce Avenue 28 33 2 29

Foothill Boulevard D Street 20 4 2 1

Mission Boulevard Jeerson Street 171 27 3 12

University Avenue Bonar Street 229 225 40 25 InternaBonal Boulevard 107th Avenue 89 69 14 11

San Pablo Avenue Harrison Street 99 114 38 43

East 14th Street Hesperian Boulevard 78 69 6 34 InternaBonal Boulevard 46th Avenue 287 286 53 63

Solano Avenue Masonic Avenue 514 397 150 127

Broadway 12th Street 3577 1374 63 47

Pedestrian Crash Analysis

Mainline Roadway

Intersec)ng Roadway

Reported Pedestrian

Crashes (1996-2005)

Mission Boulevard

Torrano Avenue 5

Davis Street Pierce Avenue 4 Foothill Boulevard D Street 1 Mission Boulevard

Jeerson Street 5

University Avenue Bonar Street 7 InternaBonal Boulevard 107th Avenue 2 San Pablo Avenue Harrison Street 2 East 14th Street

Hasperian Boulevard 1

InternaBonal Boulevard 46th Avenue 3

Solano Avenue Masonic Avenue 2

Broadway 12th Street 5

Pedestrian RISK Analysis

Mainline Roadway

Intersec)ng Roadway

Es)mated Total Weekly

Pedestrian Crossings

Annual Pedestrian

Volume Es)mate

Ten-Year Pedestrian

Volume Es)mate

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 49.01 Foothill Boulevard D Street 632 32,862 328,624 1 30.43 Mission Boulevard

Jeerson Street 5,236 272,246 2,722,464 5 18.37

University Avenue Bonar Street 11,175 581,113 5,811,127 7 12.05 InternaBonal Boulevard 107th Avenue 3,985 207,243 2,072,429 2 9.65 San Pablo Avenue Harrison Street 4,930 256,357 2,563,572 2 7.80 East 14th Street

Hasperian Boulevard 3,777 196,410 1,964,102 1 5.09

InternaBonal Boulevard 46th Avenue 12,303 639,752 6,397,522 3 4.69

Solano Avenue Masonic 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

Example: 2012 Benchmarking Report

Communities Seek to Increase Walking & Bicycling

USDOT 1994: Increase mode share from 7.9% to 15.8% 35 states have published goals to increase walking 35 states have published goals to increase bicycling 36 of 51 largest cities have goals to increase walking 47 of 51 largest cities have goals to increase bicycling 26 states and more than 250 local & regional agencies

have established Complete Streets policies

City of Portland, OR. Bicycle Counts Report 2011.

Source: City of Seattle, Bicycle Count Report, 2009

Seattle Bicycle Counts

Source: New York City DOT

San Francisco

Source: City of San Francisco MTA, 2011 Bicycle Count Report, December 2011

Communities with Pedestrian Volume Trend Data?

Alameda County Pedestrian Count Trends

Source: Alameda County Transportation Commission, Manual Pedestrian and Bicycle Count Report for Alameda County, 2002 to 2010, June 2011

Weekday 4-6 p.m. counts at selected locations

Alameda County Pedestrian Count Trends

Source: Alameda County Transportation Commission, Manual Pedestrian and Bicycle Count Report for Alameda County, 2002 to 2010, June 2011

Weekday 3-4 p.m. counts at selected locations near schools

Minneapolis Pedestrian Count Trends

Source: City of Minneapolis Pedestrian & Bicycle Count Report, 2011

Minneapolis EsBmated Daily Total Bicycle Volumes


Minneapolis EsBmated Daily Total Pedestrian Volumes


Minneapolis Screenline Count Mode Shares


Intersection Counting Exercise

E. Caesar Chavez Avenue & N. Vignes Street

Questions & Discussion

Dr. Robert J. Schneider UC Berkeley Safe TransportaBon Research & EducaBon Center

[email protected]

Preliminary Pedestrian Crash Analysis

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Typical Pedestrian AcBvity PaZern 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 locaBons with specic types of land uses were mulBplied by these factors to match counts taken at typical Alameda County LocaBons

(Example: Alameda County, CA)

Weather Adjustment Factors

Counts taken under certain weather condiBons were mulBplied by these factors to match counts taken during typical Alameda County weather condiBons

Weather Condition Definition

Warm>=80 degrees Fahrenheit (27 degrees Celsius) during first count hour7

Cool

Seasonal Adjustment Factors

Counts taken during the spring were mulBplied by these factors to match counts taken in Alameda County during a typical Bme of the year

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


>=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


>=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

Weather Condition Definition

Warm>=80 degrees Fahrenheit (27 degrees Celsius) during first count hour7

Cool=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


>=10 commercial retail properties within 0.1 miles (161 m)5


>=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

Saturday9 a.m.-5 p.m.

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)1

Weather Adjustment Factors (Counts taken under certain weather conditions were multiplied by these factors to match counts taken during typical Alameda County weather conditions)2

Seasonal Adjustment Factors (Counts taken from April through June were multiplied by these factors to match counts taken in Alameda County during a typical time of the year)3

0.93




All Time Periods

1.07

Weekday12-6 p.m.

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1.27

1.12

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1.34

1) Land use adjustment factors based on hourly automated sensor counts taken at 13 locations in Alameda County between April 2008 and June 2009.2) Weather adjustment factors based on hourly automated sensor counts taken at 13 locations in Alameda County between April 2008 and June 2009.3) Employment center, residential area, neighborhood commercial area, and multi-use trail seasonal adjustment factors based on hourly automated sensor counts taken at 13 locations in Alameda County from April 2008 to June 2009. School seasonal adjustment factor based on hourly automated sensor counts taken at 3 locations in Alameda County from May 2009 to June 2009.4) Source = Traffic Analysis Zones from San Francisco Bay Area Metropolitan Transportation Commission, 20055) Source = Land Use Parcels from Alameda County Tax Assessor's Office, 20076) Source = Bay Area Multi-Use Trail Centerlines from San Francisco Bay Area Metropolitan Transportation Commission, 20077) Source = California Irrigation Management Information System, 2008-2009 (Mills College, Union City, and Pleasanton weather stations).8) Solar radiation measurements from the previous 4 to 10 years at each of the three Alameda County weather stations were used to calculate the expected solar radiationmeasurement for every hour of the year. The weather condition was determined to be "cloudy" if the ratio of the current measurement was

Seasonal Adjustment Factors

Each month has a dierent proporBon of the total annual pedestrian or bicycle volume

(Example: NaBonal DocumentaBon Project)

Documents

Pedestrian counting