Taking Pedestrian and Bicycle Counting Programs to the Next Level

MOVING THINKINGFORWARD

Taking Pedestrian and Bicycle Counting Programs to the Next Level Lessons from NCHRP 07-19

Robert Schneider, UW Milwaukee

RJ Eldridge, Toole Design Group

Conor Semler, Kittelson & Associates

ProWalk/ProBike/ProPlace

Pittsburgh, PA

September 2014 Source: Kittelson & Associates, Inc.

Presentation Overview

Introduction

State of the Practice

Count Applications

Testing Approach and Findings

Guidebook Overview

Questions and Discussion

2

Source: Toole Design Group

NCHRP 7-19 Research Team

Kittelson & Associates, Inc.

– Principal Investigator: Paul Ryus

University of Wisconsin-Milwaukee

UC Berkeley, SafeTREC

Toole Design Group

McGill University

Quality Counts, LLC

3

Project Purpose

Address lack of pedestrian and bicycle volume data

Assess data collection technologies and methods

Develop guidance for practitioners

4

Source: Bob Schneider, UW-Milwaukee

Related Work

National Bicycle and Pedestrian Documentation Project

FHWA Traffic Monitoring Guide (TMG)

– 2013 edition includes chapter on non-motorized traffic

– NCHRP research complements FHWA guide

5

State of the Practice

Early Findings

Multimodal Count Applications

6

Source: Bob Schneider, UW-Milwaukee


NCHRP 7-19 Survey (N = 269)

10

20

36

3

11

69

2

35

39

20

0 10 20 30 40 50 60 70 80

Other

University

STATE DOT

U.S. federal agency

U.S. County

U.S. city

Transit agency

Non-profit/advocacy

MPO

Consulting firm

SURVEY RESPONSES

NCHRP 7-19 Survey Findings

• Pedestrian and bicycle counts are becoming routine for cities, MPOs, and State DOTs.

• Manual counts are the most prevalent data collection method

• Most programs lack formal or dedicated funding source and rely heavily on volunteers

• Organizations serving larger communities are more likely to conduct pedestrian and/or bicycle counts.

• Some integration with motor vehicle count databases.

Current Methods of Bicycle Counting

NCHRP 7-19 Survey Findings

There is no standard approach for count programs

Practitioners are looking for more guidance

– Choosing devices

– Selecting locations

– Count intervals and duration

– Temporal/seasonal adjustments

Pedestrian & Bicycle Count Applications

Measuring facility usage

Evaluating before-and-after data

Monitoring travel patterns

Safety analysis

Project prioritization

Multimodal modeling

11

Measuring Facility Usage

Transportation system monitoring program

Typically requires collecting counts at set locations and regular intervals

Critical for tracking progress and measuring success

12

Change in Walking and Bicycling Activity at

Washington State Count Sites, 2009–2012

Source: Washington State DOT (2012)

Evaluating Before-and-After Volumes

Measure volumes before and after facility is opened

Forecast usage of planned facilities

13

Before-and-After Bicycle Facility Usage – buffered bicycle lanes on Pennsylvania Ave

Source: Kittelson &

Associates, Portland State University, and Toole Design

Group (2012)

Monitoring Travel Patterns

Developing extrapolation factors

– Extrapolate short-duration counts over longer time periods

– Control for effect of land use, weather, demographics, etc.

Evaluating user behavior patterns

– Identify factors that influence walking/biking

– Controllable (land use) and uncontrollable (weather) factors

14

Safety Analysis

Quantifying exposure

– Variety of methods proposed to quantify exposure

– One method compares pedestrian-vehicle collissions to average annual pedestrian volumes

Identifying before-and-after safety effects

15

Mainline Roadway

Intersecting Roadway

Reported Pedestrian

Crashes (1996-2005)

Mission Boulevard

Torrano Avenue 5

Davis Street Pierce Avenue 4

Foothill Boulevard D Street 1

Mission Boulevard

Jefferson Street 5

University Avenue Bonar Street 7

International Boulevard 107th Avenue 2

San Pablo Avenue Harrison Street 2

East 14th Street

Hasperian Boulevard 1

International Boulevard 46th Avenue 3

Solano Avenue Masonic Avenue 2

Broadway 12th Street 5

Alameda County Pedestrian Crash Analysis

Mainline 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 49.01

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

Mission 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 12.05 International 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

International 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


Mainline 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

Broadway 12th Street 112,896 5,870,590 58,705,898 5 0.85


Project Prioritization

Identify high-priority locations for improvements

Identify factors that influence walking/biking and prioritize accordingly

Measure improper user behaviors (i.e., wrong-way bike riding) to identify areas needing improvement

19

Multimodal Model Development

Multimodal travel demand modeling is an emerging field

Potential to estimate demand over a large area and forecast influence of infrastructure changes

20

Source: City of Berkeley, CA

Pedestrian Master Plan

We need these data fields!

Institutionalize Pedestrian & Bicycle Data

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

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

X-Street Traffic Volume

Mainline Traffic Volume

X-Street Pedestrian

Volume

Mainline Pedestrian

Volume

Challenges to Bicycle and Pedestrian Counting

Where to count?

When (how long/how frequently) to count?

What to count?

How to count?

Site/Mode challenges

Who should be counting?

ACH SLIDE ADDITION


Arlington County’s automated bicycle and pedestrian count program

• First counter Custis Trail: Fall 2009

• Now 30 automatic counter stations

• Automatic uploads to central server

• Web based “dashboard”

• Plans for real-time “totem” counter display

Case Study: Arlington County, VA

Statistics and graphics courtesy of David Patton Bicycle & Pedestrian Planner, Arlington County Division of Transportation

ACH SLIDE ADDITION

NCHRP 7-19 Research Approach

Focus on testing and evaluating commercially available automated technologies

Assess type of technology as opposed to a specific product

Cover a range of facility types, mix of traffic, and geographic locations

Evaluate accuracy through the use of manual count video data reduction

24

Types of Counts

Motor Vehicle Data Collection

Inductive Loops

Pneumatic Tubes

Manual Count Boards

Video cameras

ITS integration

In-vehicle sensors (toll tags)

Photo Credit: Federal Highway Administration

ACH SLIDE ADDITION

Auto versus Multimodal Counts

Key differences:

– Differences in demand variability

– Ease of detection

– Experience with counting technology

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0

2,000

4,000

6,000

8,000

10,000

12,000

0 6 12 18 24

Ho

url

y V

olu

me

(ve

h/h

)

Hour Starting

Monday Tuesday Wednesday Thursday Friday Annual Weekday Average

Motor Vehicle Data Collection Constrained; somewhat predictable

ACH SLIDE ADDITION


Bicycle Data Collection Constrained environments easy to monitor

Complex environments harder to define

Detection

ACH SLIDE ADDITION


Pedestrian Data Collection Constrained environments easy to monitor

Detection

People tend to make their own path

ACH SLIDE ADDITION


Motor vehicle data collection

• Widely collected

• Easy to track vehicle movements

• Predictable patterns and routes

• Years of trend data to analyze

Bicycle and pedestrian data collection

• Sparsely collected

• Difficult to track and tabulate movements

• Unpredictable paths of travel

• Weather and seasonal impacts

• Lack of historical data

Challenge: Site/Mode characteristics

Counting System

Sensor technology one piece of counting system

32


NCHRP 07-19 Evaluation Automated devices that count all users

(pedestrians and bicyclists)

Types of Counts

Passive Infrared (IR)

Detect pedestrians and cyclists by infrared radiation (heat) patterns them emit

Passive infrared sensor placed on one side of facility

Widely used and tested

35


Active Infrared (IR)

Transmitter and receiver with IR beam

Counts caused by “breaking the beam”

36

Source: Steve Hankey, University of Minnesota

Radio Beam

Radio signal between transmitter and receiver

Detections occur when beam is broken

Not previously tested in literature

Some distinguish bikes from peds

37

Source: Karla Kingsley, Kittelson & Associates, Inc.


NCHRP 07-19 Evaluation Automated devices that count

bicyclists only

Pneumatic Tubes

Tube(s) stretched across roadway

When a bicycle rides over tube, pulse of air passes through tube to detector

39

Source: Karla Kingsley, Kittelson & Associates, Inc.

Inductive Loops

Generate a magnetic field that detect metal parts of bicycle passing over loop

In-pavement or temporary surface loops

40


Piezoelectric Sensor

Emit an electric signal when physically deformed

Typically embedded in pavement across travel way

41



NCHRP 07-19 Evaluation Other counting methods

Combination

Use one technology to detect all others plus another technology to detect bicyclists only

Provide bicycle and pedestrian counts separately

43

Photo Sources: Bob Schneider, UW-Milwaukee

Manual Counts

Most common type of counting, to date

Capture many different locations

Record pedestrians & bicyclists separately

Can count road crossings

Capture user characteristics (gender, helmet use, behavior, etc.)

Photo by Robert Schneider, UW-Milwaukee

Washington State DOT Pedestrian & Bicycle Counts

Source: Cascade Bicycle Club. Washington State Bicycle and Pedestrian Documentation Project, Prepared for the Washington State Department of Transportation, February 2013.

Existing Market Technology

• Passive infrared

• Active infrared

• Radio beam

• Pneumatic tubes

• Inductive loops

• Piezoelectric sensors

• Combination devices

• Manual Counts

• Automated video

• Emerging technologies

Understanding Count Technologies

Eve

ryth

ing

Bic

ycle

On

ly

Cla

ssif

y

Sources: Toole Design Group

Untested/Emerging technologies

Thermal

Radar

Laser scanners

Pressure and acoustic sensors

Automated video

47

Related Work

Topics related to quantifying pedestrian & bicycle activity, but not covered: – Bluetooth and WiFi detection

– GPS data collection

– Cell/smartphone data collection

– Radio frequency ID (RFID) tags

– Bike sharing data

– Pedestrian signal actuation buttons

– Surveys

– Presence detection

– Trip generation

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Quick Break for Questions

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Detection

Study Technologies and Site Locations

Technologies

– Passive infrared

– Active infrared

– Pneumatic tubes

– Inductive loops

– Piezoelectric

– Radio beam

– Combination of technologies

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Site Locations – Portland, OR

– San Francisco, CA

– Davis, CA

– Berkeley, CA

– Minneapolis, MN

– Washington, D.C.

– Arlington, VA

– Montreal, Canada

Washington, D.C. and Arlington, VA

Key Bridge separated path – Passive Infrared

– Pneumatic Tubes

– Passive Infrared with inductive loops

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Minneapolis, MN

Midtown Greenway multiuse path

– Active Infrared

– Passive Infrared

– Radio Beam

– Inductive Loops

– Pneumatic Tubes

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Eastbank Esplanade multiuse path – Passive Infrared

– Pneumatic Tubes

– Radio Beam

Portland, OR

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Source: Kittelson & Associates, Inc.

San Francisco, CA

Fell Street Bicycle Lane

– Passive Infrared

– Pneumatic Tubes

– Inductive Loops

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Source: Frank Proulx, UC Berkeley SafeTREC

Evaluation Method

Video-based manual counts

Interrater reliability tested

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Measures to Evaluate the Quality of Count Data from Technologies

Accuracy: The magnitude of the difference between the count produced by the technology, and actual (“ground-truth”) count. (Typically depends on user volumes, movement patterns, traffic mix, and environmental characteristics)

Consistency (Precision): The remaining variability in the count data after being corrected for expected under- or over-counting, given specific conditions. (Typically depends on the counting technology itself, how a specific vendor uses the technology in a particular product, and quality of installation)

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Graphical Analysis

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Undercounting

Overcounting


Active Infrared Counter Validation Data

(Somewhat accurate, very precise)

(Somewhat accurate, but not precise)

Passive Infrared Counter Validation Data

Evaluation Criteria

Accuracy

Precision

Ease of installation, maintenance requirements, cost, flexibility of data, etc.

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Summary of Data Collected

Condition Passive Infrared

Active Infrared

Pneumatic Tubes

Inductive Loops

Inductive Loops

(Facility) Piezo-

electric Radio Beam

Total hours of data 298 30 160 108 165 58 95

Temperature (°F) (mean/SD)

70 / 15 64 / 26 71 / 9 73 / 12 71 / 17 72 / 10 74 / 10

Hourly user volume (mean/SD)

240 / 190 328 / 249 218 / 203 128 / 88 200 / 176 128 / 52 129 / 130

Nighttime hours 30 3 10 13 19 15.75 3.5

Rain hours 17 0 4 7 7 0 6

Cold hours (<30 °F) 12 5 0 0 7 0 0

Hot hours (>90 °F) 11 0 0 5 5 3 4

Thunder hours 8 0 0 2 2 0 0

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Passive Infrared

Easy installation

Mounts to existing pole/surface or in purpose-built pole

Potential false detections from background

Possible undercounting due to occlusion

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Passive Infrared

Average undercount rate 8.75%

Differences between products tested

Correction function could account for facility width

Accuracy not affected by high temperatures

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Active Infrared

Moderately easy installation – requires aligning transmitter and receiver

Single device tested – accurate and highly precise

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Pneumatic Tubes

Tested BSCs – bicycle specific counters

Primarily tested tubes on multi-use paths and bicycle lanes

Issues with site on 15th Avenue in Minneapolis

– Tube nails came out of ground

– Severe undercounting and overcounting

– Removed sensors from data set

65

Pneumatic Tubes

Fairly high accuracy at very high volumes

Accuracy rates not observed to decline with aging tubes

Future research in mixed traffic settings

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Radio Beam

Required mounting devices 10’ apart

Tested two products: one that distinguished bicyclists and pedestrians (product A), one that did not (product B)

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Radio Beam

Product B higher accuracy

Product A – low precision and lower accuracy

Occlusion errors

Temperature, lighting, rain issues

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Inductive Loops

Permanent (in ground) or temporary (on surface)

Bypass errors

– Cyclists passing

outside bike lane

– Loops leaving gaps

in detection zone

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Inductive Loops

Accurate and precise

Errors with age of loops not detected

Higher volumes slightly affect accuracy

No substantial difference between permanent and temporary loops

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Inductive Loops

Need to mitigate bypass errors

71

Piezoelectric Strips

Tested one existing device, due to difficulties procuring equipment

Data suggests device is not functioning very precisely or accurately

Caution – data from single device not installed by research team

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Combination Counter

Passive infrared + inductive loops

Each device also assessed separately

Inferred pedestrian volumes (Total – bikes)

Occlusion errors and undercounting

High rate of precision

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Accuracy Calculations

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Average percentage deviation (APD): overall divergence from perfect accuracy (undercounting rate)

Average of the absolute percentage deviation (AAPD): accounts for undercount/overcount cancelation (total deviation)

Research Conclusions

Device Undercounting Rate Total Deviation

Passive Infrared (2 products) 8.75% 20.11%

Active Infrared 9.11% 11.61%

Pneumatic Tubes 17.89% 18.50%

Radio Beam 18.18% 48.15%

Inductive Loops 0.55% 8.87%

Piezoelectric Strips 11.36% 26.60%

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Automated counter accuracy:

Research Conclusions

Factors influencing accuracy

– Proper calibration and installation

– Occlusion

– Vendor differences

Factors not found to influence accuracy

– Age of inductive loops or pneumatic tubes

– Temperature

– Snow/rain (limited data)

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Guidebook Organization

Quick Start Guide

1. Introduction

2. Non-Motorized Count Data Applications

3. Data Collection Planning and Implementation

4. Adjusting Count Data

5. Sensor Technology Toolbox Case Studies

Manual Pedestrian and Bicyclist Counts: Example Data Collector

Instructions

Count Protocol Used for NCHRP Project 07-19

Appendix D. Day-of-Year Factoring Approach

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Ap

pen

dic

es

2. Non-Motorized Count Applications

Measuring facility usage

Evaluating before-and-after data

Monitoring travel patterns

Safety analysis

Project prioritization

Multimodal modeling

Source: Kittelson & Associates,

Portland State University, and Toole Design Group (2012)

Before-and-After Bicycle Facility Usage – buffered bicycle lanes on Pennsylvania Avenue

For each application:

Details Case Studies

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3. Data Collection Planning & Implementation

Covers:

1. Planning the count program

2. Implementing the count program

Provides examples, detailed guidance, checklists

Source: Toole Design Group.

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Common Strategy: Manual + Automated

Geographic Coverage

A Few Locations

Many Locations

Co

un

t D

ura

tio

n

Continuous Automated

Short-Term Manual

Comparison of Counting Technologies

Characteristic Passive Infrared

Active Infrared

Pneumatic Tubes

Inductive Loops


Passive IR + Inductive

Loops

Radio Beam (One

Frequency)

Radio Beam (High/Low Frequency)

Automated Video1

Manual Counts2

Type of users counted All facility users Yes Yes Yes Yes Yes Yes Yes

Pedestrians only Yes Yes Yes Yes Bicycles only Yes Yes Yes Yes Yes Yes Yes

Pedestrians vs. bicycles Yes Yes Yes Yes Bicycles vs. automobiles Yes Yes Yes Yes

Characteristics collected Different user types Yes Yes Yes Yes

Direction of travel3 Yes Yes Yes Yes Yes Yes Yes Yes Yes User characteristics4 Yes Yes

Types of sites counted Multiple-use trail segments Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Sidewalk segments Yes Yes Yes Yes Yes Yes Yes Bicycle lane segments Yes Yes Yes Yes Yes Cycle track segments Yes Yes Yes Yes Yes Yes

Shared roadway segments Yes Yes Yes Yes Roadway crossings (detect from median)5 Yes Yes Yes Yes Yes Yes Yes Yes Roadway crossings (detect from end of crosswalk) Yes Yes Intersections (identify turning movements) Yes

Notes: (1) Existing “automated video” systems may not use a completely automated counting process; they may also incorporate manual data checks of automated video processing.

(2) Includes manual counts from video images.

(3) Technologies noted as “Yes” have at least one vendor that uses the technology to capture directionality.

(4) User characteristics include estimated age, gender, helmet use, use of wheelchair or other assistive device, pedestrian and bicyclist behaviors, and other characteristics.

(5) Roadway crossings at medians potentially have issues with overcounting due to people waiting in the median. Median locations were not tested during this project.

Characteristic Passive Infrared

Active Infrared

Pneumatic Tubes

Inductive Loops


Passive IR + Inductive

Loops

Radio Beam (One

Frequency)

Radio Beam (High/Low Frequency)

Automated Video1

Manual Counts2

Type of users counted All facility users Yes Yes Yes Yes Yes Yes Yes

Pedestrians only Yes Yes Yes Yes Bicycles only Yes Yes Yes Yes Yes Yes Yes

Pedestrians vs. bicycles Yes Yes Yes Yes Bicycles vs. automobiles Yes Yes Yes Yes

Characteristics collected Different user types Yes Yes Yes Yes

Direction of travel3 Yes Yes Yes Yes Yes Yes Yes Yes Yes User characteristics4 Yes Yes

Types of sites counted Multiple-use trail segments Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Sidewalk segments Yes Yes Yes Yes Yes Yes Yes Bicycle lane segments Yes Yes Yes Yes Yes Cycle track segments Yes Yes Yes Yes Yes Yes

Shared roadway segments Yes Yes Yes Yes Roadway crossings (detect from median)5 Yes Yes Yes Yes Yes Yes Yes Yes Roadway crossings (detect from end of crosswalk) Yes Yes Intersections (identify turning movements) Yes

Notes: (1) Existing “automated video” systems may not use a completely automated counting process; they may also incorporate manual data checks of automated video processing.

(2) Includes manual counts from video images.

(3) Technologies noted as “Yes” have at least one vendor that uses the technology to capture directionality.

(4) User characteristics include estimated age, gender, helmet use, use of wheelchair or other assistive device, pedestrian and bicyclist behaviors, and other characteristics.

(5) Roadway crossings at medians potentially have issues with overcounting due to people waiting in the median. Median locations were not tested during this project.

Comparison of Counting Technologies

INSTALLATION CHECKLIST: ADVANCE PREPARATION

Site visit to identify the specific installation location. Specifically, note poles that will be used, where pavement will be cut, or where utility boxes will be installed to house electronics. Verify that no potential obstructions (e.g., vegetation) or sources of interference (e.g., doorway, bus stop, bicycle rack) are present.

Obtain and document necessary permissions. Permits or permissions may include right-of-way encroachment permits, pavement cutting permits or bonds, landscaping permits, or interagency agreements. Obtaining these permissions may take up to several months, particularly if other agencies are involved.

Create a site plan. Develop a detailed diagram of the planned installation on an aerial photo or ground-level image documenting the intended equipment installation locations and anticipated detection zone (after installation this will be useful for validating equipment either visually or with video monitoring). This diagram may be useful for obtaining installation permissions and working with contractors. Figure 3-6 provides an example site plan.

Hire a contractor if necessary (or schedule appropriate resources from within the organization).

Arrange an on-site coordination meeting involving all necessary parties (e.g., staff representing the organization installing the counter, permitting staff, contractors). If possible, a vendor representative should be on hand or available by phone. It may take several weeks to find a suitable time when everyone is available.

Check for potential problems. Problems with the site may include interference from utility wires, upcoming constructions projects, hills, sharp curves, nearby illicit activity, and nearby insect and animal activity. Some of these conditions can be identified from imagery, but they should also be evaluated in the field.

EQUIPMENT MONITORING CHECKLIST

Sensor height. Is the sensor still mounted at the correct height?

Sensor direction. Is the sensor still pointed in the correct direction?

Clock. Is the device’s clock set correctly?

Cleaning. Remove dirt, mud, water, or other material that may affect the sensor or other vital components.

Battery. Is the remaining battery life sufficient to last until the next scheduled visit?

Obstructions. For example, is vegetation growing too close to the device?

Unanticipated site problems. For example, is the pole being used for bicycle parking, or are people congregating in the area (as opposed to walking past the counter)?

Pedestrian or bicycle detection. Are pedestrians or bicyclists passing through the counter’s detection zone being counted? If not, can the counter’s sensitivity be adjusted in the field, or does it need to be removed for repairs?

Download data. Use the same export option consistently to ease the data management burden back in the office.

Securement. Are the installation elements and locking devices still secure and durable? Poorly secured or loose fitted devices are more vulnerable to theft and vandalism.

Downloading data from an automated counter in the field.

Source: Lindsay Arnold, UC Berkeley Safe Transportation Research & Education Center

4. Adjusting Count Data

Sources of counter inaccuracy

Measured counter accuracy

Counter correction factors

Expansion factors

Example applications

Occlusion error

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Linear Correction Factors

Table 4-2. Simple Counter Correction Factors Developed by NCHRP Project 07-19

Sensor Technology

Adjustment

Factor Hours of Data

Passive infrared 1.137 298

Product A 1.037 176

Product B 1.412 122

Active infrared† 1.139 30

Radio beam 1.130 95

Product A (bicycles) 1.470 28

Product A (pedestrians) 1.323 27

Product B 1.123 39

Pneumatic tubes* 1.135 160

Product A 1.127 132

Product B 1.520 28

Surface inductive loops* 1.041 29

Embedded inductive loops* 1.054 79

Piezoelectric strips† 1.059 58

Notes: *Bicycle-specific products.

†Factor is based on a single sensor at one site; use caution when applying.

Raw Data

Corrected Data

5. Treatment Toolbox

Description

Typical application

Level of effort

Strengths

Limitations

Accuracy

Usage

Sidebar with quick facts

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Summary: Key Issues for Practice

Consider automated + manual counts

Determine whether to use permanent or mobile counters (or both)

Accuracy of devices & service vary by vendor

Results are useful for general corrections, but best practice is to conduct local ground-truth counts & make specific corrections

Consider approvals and site characteristics when selecting a count site

How critical is accuracy?

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Suggested Research

Additional testing of automated technologies

– Technologies not tested or underrepresented

– Additional sites and conditions

Extrapolating short-duration counts to longer-duration counts

Adjustment factors for environmental factors

Uniform approach(es) for FHWA TMG?

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Questions and Discussion

Contact Information

– Conor Semler, Kittelson & Associates, Boston, MA [email protected], 857.265.2153

– Robert Schneider, UW-Milwaukee, Milwaukee, WI [email protected], 414.229.3849

– RJ Eldridge, Toole Design Group, Washington, DC [email protected], 301.927.1900 x107

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(Extra Slides)

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Plan the Count Program

Specify the data collection purpose

Identify data collection resources

Select count locations & determine timeframe

Consider available counting methods

Implement the Count Program

Obtain necessary permissions

Procure counting devices

Inventory and prepare devices

Train staff

Install and validate devices

Calibrate devices

Maintain devices

Manage count data

Clean and correct count data

Three Types of Data Adjustments

Cleaning erroneous data involves identifying when a count is likely to represent a time period when an automated counter was not observing the intended pedestrian or bicycle movements. Data from these time periods are discarded or replaced by better estimates.

Correcting for systematic errors involves applying a correction function that removes the expected amount of under- or overcounting for a particular counting technology (often due to occlusion).

Expanding short counts to longer time periods involves applying an expansion factor to a count collected for a short time period. The expansion factor is based on the type of activity pattern assumed to exist at the site, and it can account for the effects of weather on pedestrian and bicycle volumes.

Weekday

Hour Raw Count

Cleaned

Count

Corrected

Count

Extrapolated

Count

0:00 10 10 11.2 11.2

1:00 5 5 5.6 5.6

2:00 3 3 3.36 3.36

3:00 2 2 2.24 2.24

4:00 3 3 3.36 3.36

5:00 15 15 16.8 16.8

6:00 65 65 72.8 72.8

7:00 125 125 140 140

8:00 150 150 168 168

9:00 140 140 156.8 156.8

10:00 95 95 106.4 106.4

11:00 105 105 117.6 117.6

12:00 130 130 145.6 145.6

13:00 0 115 128.8 128.8

14:00 100 100 112 112

15:00 125 125 140 140

16:00 140 140 156.8 156.8

17:00 160 160 179.2 179.2

18:00 155 155 173.6 173.6

19:00 145 145 162.4 162.4

20:00 120 120 134.4 134.4

21:00 85 85 95.2 95.2

22:00 190 45 50.4 50.4

23:00 20 20 22.4 22.4

0:00 11.2

1:00 5.6

2:00 3.36

3:00 2.24

4:00 3.36

Three Types of Data Adjustments

1 2 3

Example: 24-hours on one weekday

Adjustment 1: Clean Data

Adjustment 2: Correct Data

+X% to correct for undercounting

Adjustment 3: Expand Data

Raw Count Data with Missing Data Imputed

Taking Pedestrian and Bicycle Counting Programs to the Next Level

Presentations & Public Speaking

Transcript of Taking Pedestrian and Bicycle Counting Programs to the Next Level