This page is a compendium of the resources provided throughout the Guide to Bicycle & Pedestrian Count Program pages.
Count Program Checklist
Bicycle and pedestrian count webinar slides (udpated 4.14)
Walk-Bike-Count discussion Google group
Traffic Monitoring Guide
Summary of bicycle & pedestrian count inventory process in three states
The Minnesota Department of Transportation published a report in 2013, which describes its inventory of the state’s bicycle and pedestrian count programs and offers recommendations.
Bicycle and Pedestrian Count Data Available Online
The Los Angeles metropolitan area has a bicycle count data clearinghouse which recommends manual count formats, allows partner agencies to upload data and makes the data publicly available.
In the Philadelphia metropolitan area, the Delaware Valley Regional Planning Commission (DVRPC) provides online access to its bicycle and pedestrian counts.
Similarly, Arlington, Virginia offers access to their count data through an online site which allows data to be sorted by weather and time.
Many other jurisdictions are offering their bicycle and pedestrian count data in various formats online. Below is a partial list:
• Boulder, Colorado
• Minneapolis, Minnesota
• New York City, New York
• Portland, Oregon
• Seattle, Washington
Unfortunately, many of the sites above to do not show all of the count data available in a given city or region. For example, the Boulder site only shows short-duration bicycle and pedestrian counts collected as part of the regularly motor vehicle counting program, and does not provide access to data from their many permanent bicycle count stations.
For this reason, more efforts on creating centralized data clearinghouses for bicycle and pedestrian count data are needed. To that end, the National Institute of Transportation and Communities (NITC) has assembled a pooled-fund of local, regional, state and federal agencies, which will create an online national data archive for non-motorized traffic count data. Work on the archive is scheduled to start in March 2014. For more information, contact Hau Hagedorn.
Turner and Lasley discuss QA for non-motorized counts and give an example of how they cleaned data from an infrared bicycle and pedestrian counter in their 2013 paper.
The following document details how to compute AADBP according to the AASHTO method in order to create traffic pattern plots.
Miranda-Moreno and others have created a statistically-based method for grouping bicycling patterns using data from 40 North American locations. They classified these locations into four groups: utilitarian, mixed utilitarian, recreational and mixed recreational.
In 2013, Colorado Department of Transportation released a report detailing the process of inventorying, grouping, and computing seasonal adjustment factors for the state. Locations are classified into three groups: mountain non-commute, urban plains non-commute and commute.
A 2012 Colorado Department of Transportation report discusses the topic of grouping sites and more generally recommends three basic groups: commute, non-commute and mixed.
Cluster analysis offers a more statistically based option to grouping locations based on daily and monthly factors. For motor vehicles, the Traffic Monitoring Guide Section 3.2 discusses the pros and cons of cluster analysis compared to other methods for grouping stations, and Appendix G gives an example of cluster analysis applied to North Carolina motor vehicle data. For non-motorized traffic, the 2013 report for the Colorado Department of Transportation (page 95) includes an example of how cluster analysis was applied to bicycle and pedestrian count data.
Alex Hyde-Wright has created an example of a simple method for estimating factors for one permanent count station. This example can be downloaded.
A traditional method for computing factors is documented in this PDF.
Similar methods are described in the Traffic Monitoring Guide.
If no permanent bicycle or pedestrian count data are available in your region or state, the National Bicycle and Pedestrian Documentation Project, a joint effort by Alta Planning and Design and the Institute of Transportation Engineers, provides a set of factors that can be downloaded from their website (see the Extrapolation Workbook). Since bicycle and pedestrian traffic patterns vary greatly by geography and climate, applying these national factors can result in large error and may only be appropriate for very rough estimates.
El Esawey and others, working with data from Vancouver, British Columbia, have investigated the details of how to estimate hourly, daily and monthly factors, including investigating how to include weather factors. Their first paper discusses the best approaches to computing daily factors specifically. Their second paper analyzes both daily and monthly factors and can be found below in addition to their TRB presentation:
Others have also investigated factoring including how weather might be included:
• Sears, Flynn, Autlman-Hall and Dana 2012
• Nordback 2012
• Dowds and Sullivan 2012
• Figliozzi, Johnson, Monsere, Nordback 2014
Nordback and others have created a set of Colorado-specific factors published in their 2013 report.
Recent work presented at the 2014 annual meeting of the Transportation Research Board by two independent teams, Hankey, Lindsey and Marshall in Minneapolis1, and Nosal, Miranda-Moreno and Krstulic in Montreal2, found that using factors for each day of the year could outperform traditional seasonal adjustment factors even when weather was specifically factored in.
The National Bicycle and Pedestrian Documentation Project offers a standard procedure for collecting manual short duration counts.
Boulder County, Colorado conducts a short duration bicycle counting program as part of its motor vehicle count program using pneumatic tube counters. Through extensive testing using various equipment configurations, the county determined that bicycles were being counted as trucks. To improve the accuracy of the off-the-shelf pneumatic tube counter, the county modified the counter’s vehicle classification scheme so that fewer cyclists were misclassified.3 Below is a presentation from Alex Hyde-Wright and Brian Graham of Boulder County as well as instructions for the classification scheme and a copy of the classification scheme.
• Instructions for Classification Scheme
• Classification Scheme Boulder County (BOCO) Classification Scheme.sch_.docx (Download and change the file extension to *.sch. If this is problematic, just open the file and copy the text to a text editor - like Notepad - and save the file as BOCO.SCH)
Multiple smart phone apps are available for counting bicyclists and pedestrians including Bike And Walk and Bike Count.
Kothuri and others describe how pedestrian activity and bicycle volume data can be collected using existing signal detection equipment.
Davis and Wicklatz used a random stratified sampling approach with a short duration count program to estimate bicycle miles traveled in the Twin Cities area of Minnesota.
Dowds and Sullivan applied a similar approach to estimate bicycle miles traveled in Chittenden County, Vermont.
Nordback and others recommend one week of short duration counts as optimal.
The Los Angeles metropolitan area hosts a Bike Count Data Clearinghouse and provides guidance on selecting sites and counting technologies.
1Hankey, S., G. Lindsey, et al. (2014). Day-of-Year Scaling Factors and Design Considerations for Non-motorized Traffic Monitoring Programs. 93rd Annual Meeting of the Transportation Research Board. Washington, D.C., National Academies.
2Nosal, T., L. Miranda-Moreno, et al. (2014). Incorporating weather: a comparative analysis of Average Annual Daily Bicyclist estimation methods. 93rd Annual Meeting of the Transportation Research Board. Washington, D.C., National Academies.
3Hyde-Wright, A., B. Graham, et al. (2014). "Counting Bicyclists with Pneumatic Tube Counters on Shared Roadways." ITE Journal.