Skip to main content
Intended for healthcare professionals
Restricted access
Research article
First published online January 1, 2009

Methodology for Counting Pedestrians at Intersections: Use of Automated Counters to Extrapolate Weekly Volumes from Short Manual Counts

Abstract

Accurate methods of counting pedestrians are needed to quantify exposure for safety analysis, rank infrastructure improvements and safety programs by priority, evaluate the benefits of pedestrian projects, develop models of pedestrian volumes, and track changes in pedestrian activity over time. However, pedestrian counts are still much less common than motor vehicle counts in most communities. In addition, existing count methodologies are not standardized and rarely provide enough information to extrapolate to weekly, monthly, or annual volumes. This exploratory study presents a methodology for estimating weekly pedestrian intersection crossing volumes based on 2-h manual counts. The methodology, implemented in Alameda County, California, involves a combination of manual and automated counts to determine weekly volumes. More than 690,000 pedestrians were counted during the 13-week study period. Manual counts were conducted at a set of 50 intersections. Automated counts from sidewalk locations in close proximity to a subset of 11 intersections were used to adjust these counts for time of day and week, surrounding land use characteristics, and weather conditions. The extrapolated pedestrian volume estimates were then used to calculate the number of reported crashes per 10 million pedestrian crossings at each of the study intersections. The results of this study demonstrate how pedestrian volumes can be routinely integrated into transportation safety and planning projects.

Get full access to this article

View all access and purchase options for this article.

References

1. Traffic Monitoring Guide. Report FHWA-PL-01-021. FHWA, U.S. Department of Transportation, 2001. www.fhwa.dot.gov/ohim/tmguide.
2. Diogenes M. C., Greene-Roesel R., Arnold L. S., and Ragland D. R. Pedestrian Counting Methods at Intersections: A Comparative Study. In Transportation Research Record: Journal of the Transportation Research Board, No. 2002, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 26–30.
3. Schneider R. J., Patten R. S., and Toole J. L. Case Study Analysis of Pedestrian and Bicycle Data Collection in U.S. Communities. In Transportation Research Record: Journal of the Transportation Research Board, No. 1939, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 77–90.
4. Greene-Roesel R., Diogenes M. C., Ragland D. R., and Lindau L. A. Effectiveness of a Commercially Available Automated Pedestrian Counting Device in Urban Environments: Comparison with Manual Counts. Traffic Safety Center, University of California, Berkeley, 2008.
5. Schneider R., Patten R., Toole J., and Raborn C. Pedestrian and Bicycle Data Collection in United States Communities: Quantifying Use, Surveying Users, and Documenting Facility Extent. FHWA, U.S. Department of Transportation, Jan. 2005.
6. Turner S., Middleton D., Longmire R., Brewer M., and Eurek R. Testing and Evaluation of Pedestrian Sensors. Texas Transportation Institute, Texas A&M University, Sept. 2007.
7. Bu F., Greene-Roesel R., Diogenes M. C., and Ragland D. R. Estimating Pedestrian Accident Exposure: Automated Pedestrian Counting Devices Report. Traffic Safety Center, Report UCB-TSC-RR-2007-7. University of California, Berkeley, March 2007.
8. Bell A. H. Technology Innovations: Infrared Bicyclist and Pedestrian Counters. Journal of the Association of Pedestrian and Bicycle Professionals, 2006, pp. 4–5.
9. Cameron R. M. Pedestrian Volume Characteristics. Traffic Engineering, Vol. 47, No. 1, 1977, pp. 36–37.
10. Davis S. E., King L. E., and Robertson H. D. Predicting Pedestrian Crosswalk Volumes. In Transportation Research Record 1168, TRB, National Research Council, Washington, D.C., 1988, pp. 25–30.
11. Hocherman I., Hakkert A. S., and Bar-Ziv J. Estimating the Daily Volume of Crossing Pedestrians from Short Counts. In Transportation Research Record 1168, TRB, National Research Council, Washington, D.C., 1988, pp. 31–38.
12. Zeeger C. V., Stewart R., Huang H., Lagerwey P. A., Feaganes J., and Campbell B. J. Safety Effects of Marked versus Unmarked Crosswalks at Uncontrolled Locations: Final Report and Recommended Guidelines. Report FHWA–HRT–04–100. FHWA, U.S. Department of Transportation, 2005.
13. U.S. Census Bureau. 2007 American Community Survey 1-Year Estimates. 2007. http://factfinder.census.gov/servlet/DatasetMainPageServlet?_program=ACS.
14. Ewing R., and Cervero R. Travel and the Built Environment: A Synthesis. In Transportation Research Record: Journal of the Transportation Research Board, No. 1780, TRB, National Research Council, Washington, D.C., 2001, pp. 87–114.
15. Handy S. Critical Assessment of the Literature on the Relationships Among Transportation, Land Use, and Physical Activity. Transportation Research Board of the National Academies, Washington, D.C., 2005. http://trb.org/downloads/sr282papers/sr282Handy.pdf.
16. Krizek K. J. Operationalizing Neighborhood Accessibility for Land Use–Travel Behavior Research and Regional Modeling. Journal of Planning Education and Research, Vol. 22, No. 3, 2003, pp. 270–287.
17. Shriver K. Influence of Environmental Design on Pedestrian Travel Behavior in Four Austin Neighborhoods. In Transportation Research Record 1578, TRB, National Research Council, Washington, D.C., 1997, pp. 64–75.
18. Schneider R. J., Arnold L. S., and Ragland D. R. Pilot Model for Estimating Pedestrian Intersection Crossing Volumes. In Transportation Research Record: Journal of the Transportation Research Board, No. 2140, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 13–26.
19. Eco-Counter homepage. www.eco-compteur.com. Accessed July 29, 2008.

Cite article

Cite article

Cite article

OR

Download to reference manager

If you have citation software installed, you can download article citation data to the citation manager of your choice

Share options

Share

Share this article

Share with email
EMAIL ARTICLE LINK
Share on social media

Share access to this article

Sharing links are not relevant where the article is open access and not available if you do not have a subscription.

For more information view the Sage Journals article sharing page.

Information, rights and permissions

Information

Published In

Article first published online: January 1, 2009
Issue published: January 2009

Rights and permissions

© 2009 National Academy of Sciences.
Request permissions for this article.

Authors

Affiliations

Robert J. Schneider
Traffic Safety Center, University of California, Berkeley, 2614 Dwight Way #7374, Berkeley, CA 94720.
Lindsay S. Arnold
Traffic Safety Center, University of California, Berkeley, 2614 Dwight Way #7374, Berkeley, CA 94720.
David R. Ragland
Traffic Safety Center, University of California, Berkeley, 2614 Dwight Way #7374, Berkeley, CA 94720.

Notes

Metrics and citations

Metrics

Journals metrics

This article was published in Transportation Research Record: Journal of the Transportation Research Board.

VIEW ALL JOURNAL METRICS

Article usage*

Total views and downloads: 144

*Article usage tracking started in December 2016


Altmetric

See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores



Articles citing this one

Receive email alerts when this article is cited

Web of Science: 0

Crossref: 32

  1. A Real-Time Model for Pedestrian Flow Estimation in Urban Areas based ...
    Go to citation Crossref Google Scholar
  2. The Usability of Unmanned Aerial Vehicles (UAVs) for Pedestrian Observ...
    Go to citation Crossref Google Scholar
  3. Clustering Approach to Generate Pedestrian Traffic Pattern Groups
    Go to citation Crossref Google Scholar
  4. Spatiotemporal exploration of Melbourne pedestrian demand
    Go to citation Crossref Google Scholar
  5. Enhancing non-motorist safety by simulating trip exposure using a tran...
    Go to citation Crossref Google Scholar
  6. Pedestrian and Bicycle Volume Data Collection Using Drone Technology
    Go to citation Crossref Google Scholar
  7. Modelling risk factors for fatal pedestrian crashes in Kolkata, India
    Go to citation Crossref Google Scholar
  8. Pedestrian safety at signalized intersections: Modelling spatial effec...
    Go to citation Crossref Google Scholar
  9. The Impact of PM10 Levels on Pedestrian Volume: Findings from Streets ...
    Go to citation Crossref Google Scholar
  10. A comparative study of safe and unsafe signalized intersections from t...
    Go to citation Crossref Google Scholar
  11. Identifying High-Risk Intersections for Walking and Bicycling Using Mu...
    Go to citation Crossref Google Scholar
  12. Pedestrian Count Expansion Methods: Bridging the Gap between Land Use ...
    Go to citation Crossref Google Scholar
  13. A Pedestrian Exposure Model for the California State Highway System
    Go to citation Crossref Google Scholar
  14. Comparative case studies: trip and parking generation at Orenco Statio...
    Go to citation Crossref Google Scholar
  15. Comparison of Pedestrian Count Expansion Methods: Land Use Groups vers...
    Go to citation Crossref Google Scholar
  16. Adding temporal information to direct-demand models: Hourly estimation...
    Go to citation Crossref Google Scholar
  17. Intelligent Consumer Flow and Experience Analysis System Based on Cogn...
    Go to citation Crossref Google Scholar
  18. Correlates of the Built Environment and Active Travel: Evidence from 2...
    Go to citation Crossref Google Scholar
  19. Spatial models of active travel in small communities: Merging the goal...
    Go to citation Crossref Google Scholar
  20. Bicycle and Pedestrian Counts at Signalized Intersections Using Existi...
    Go to citation Crossref Google Scholar
  21. Facility-Demand Models of Peak Period Pedestrian and Bicycle Traffic: ...
    Go to citation Crossref Google Scholar
  22. Modeling Nonmotorized Travel Demand at Intersections in Calgary, Canad...
    Go to citation Crossref Google Scholar
  23. Institutionalizing Bicycle and Pedestrian Monitoring Programs in Three...
    Go to citation Crossref Google Scholar
  24. Temporal trends and the effect of weather on pedestrian volumes: A cas...
    Go to citation Crossref Google Scholar
  25. Pedestrian Crash Risk on Boundary Roadways...
    Go to citation Crossref Google Scholar
  26. Performance measures and input uncertainty for pedestrian crossing exp...
    Go to citation Crossref Google Scholar
  27. Capturing and Representing Multimodal Trips in Travel Surveys...
    Go to citation Crossref Google Scholar
  28. Development and Application of Volume Model for Pedestrian Intersectio...
    Go to citation Crossref Google Scholar
  29. Modeling of Pedestrian Activity at Signalized Intersections: Land Use,...
    Go to citation Crossref Google Scholar
  30. Visual assessment of pedestrian crashes
    Go to citation Crossref Google Scholar
  31. Association between Roadway Intersection Characteristics and Pedestria...
    Go to citation Crossref Google Scholar
  32. Pilot Model for Estimating Pedestrian Intersection Crossing Volumes
    Go to citation Crossref Google Scholar

Figures and tables

Figures & Media

Tables

View Options

Get access

Access options

If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:


Alternatively, view purchase options below:

Purchase 24 hour online access to view and download content.

Access journal content via a DeepDyve subscription or find out more about this option.

View options

PDF/ePub

View PDF/ePub