Skip to main content
Intended for healthcare professionals
Restricted access
Research article
First published online January 1, 2016

Facility-Demand Models of Peak Period Pedestrian and Bicycle Traffic: Comparison of Fully Specified and Reduced-Form Models

Steve Hankey [email protected] and Greg LindseyView all authors and affiliations
Volume 2586, Issue 1
https://doi.org/10.3141/2586-06

Abstract

Transportation planners and engineers need better spatial estimates of walking and cycling traffic to assess exposure to hazards, evaluate infrastructure investments, and locate facilities. Facility-demand models are potentially useful for generating spatial estimates of traffic volumes. Few facility-demand models have explored trade-offs between fully specified (i.e., exploratory) models and reduced-form models easily applied in the field. Presented are facility-demand models based on peak period (4 to 6 p.m.) counts of pedestrian and bicycle traffic in Minneapolis, Minnesota. The count database (n = 954 observations; 471 locations) has sufficient spatial density (~3 locations km−2) to develop spatially resolved models (i.e., ~100-m resolution). The modeling approach employs a stepwise linear regression method allowing for varying the spatial scale of independent (land use and transportation) variables. Compared were fully specified (statistically optimal) models and supervised, reduced-form models that included fewer variables based on theoretical validity. Reduced-form core models had modest goodness of fit (adjusted R2: ~.5) and included independent variables with large (industrial area and population density) and small (bicycle facilities, retail area, open space, transit stops) spatial scales. Also developed were reduced-form, time-averaged models for a subset of count sites having multiple observations (n = 84). With the use of reduced-form models (independent variables ranged from four to nine among models), block-level traffic estimates were generated (n = 13,886). Results suggest that reduced-form models perform nearly as well as fully specified models and are easier to apply and interpret. This work could be extended by assessing model performance when estimates of annual average traffic are used in model building.

Get full access to this article

View all access and purchase options for this article.

Get Access

References

1. National Bicycling and Walking Study: 15 Year Status Report. Pedestrian and Bicycle Information Center. FHWA, U.S. Department of Transportation, 2010.
Google Scholar
2. Pucher J., Buehler R., and Seinen M. Bicycling Renaissance in North America? An Update and Re-Appraisal of Cycling Trends and Policies. Transportation Research Part A, Vol. 45, 2011, pp. 451–475.
Crossref
Google Scholar
3. Frank L. D., Schmid T. L., Sallis J. F., Chapman J., and Saelens B. E. Linking Objectively Measured Physical Activity with Objectively Measured Urban Form. American Journal of Preventive Medicine, Vol. 28, 2005, pp. 117–125.
Crossref
PubMed
Google Scholar
4. Sallis J. F., Saelens B. E., Frank L. D., Conway T. L., Slymen D. J., Cain K. L., Chapman J. E., and Kerr J. Neighborhood Built Environment and Income: Examining Multiple Health Outcomes. Social Science and Medicine, Vol. 68, 2009, pp. 1285–1293.
Crossref
PubMed
Google Scholar
5. Hankey S., Marshall J. D., and Brauer M. Health Impacts of the Built Environment: Within-Urban Variability in Physical Inactivity, Air Pollution, and Ischemic Heart Disease Mortality. Environmental Health Perspectives, Vol. 120, 2012, pp. 247–253.
Crossref
PubMed
Google Scholar
6. Andersen L., Schnohr P., Schroll M., and Hein H. O. All-Cause Mortality Associated with Physical Activity During Leisure Time, Work, Sports, and Cycling to Work. Archive of Internal Medicine, Vol. 160, 2000, pp. 1621–1628.
Crossref
PubMed
Google Scholar
7. Hamer M., and Chida Y. Active Commuting and Cardiovascular Risk: A Meta-Analytic Review. Preventive Medicine, Vol. 46, 2008, pp. 9–13.
Crossref
PubMed
Google Scholar
8. Nordback K., Marshall W. E., Janson B. N., and Stolz E. Estimating Annual Average Daily Bicyclists: Errors and Accuracy. In Transportation Research Record: Journal of the Transportation Research Board, No. 2339, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 90–97.
Crossref
Google Scholar
9. Miranda-Moreno L. F., Nosal T., Schneider R. J., and Proulx F. Classification of Bicycle Traffic Patterns in Five North American Cities. In Transportation Research Record: Journal of the Transportation Research Board, No. 2339, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 68–79.
Crossref
Google Scholar
10. Hankey S., Lindsey G., and Marshall J. Day-of-Year Scaling Factors and Design Considerations for Nonmotorized Traffic Monitoring Programs. In Transportation Research Record: Journal of the Transportation Research Board, No. 2468, Transportation Research Board of the National Academies, Washington, D.C., 2014, pp. 64–73.
Crossref
Google Scholar
11. Nosal T., Miranda-Moreno L. F., and Krstulic Z. Incorporating Weather: Comparative Analysis of Annual Average Daily Bicyclist Traffic Estimation Methods. In Transportation Research Record: Journal of the Transportation Research Board, No. 2468, Transportation Research Board of the National Academies, Washington, D.C., 2014, pp. 100–110.
Crossref
Google Scholar
12. Esaway M. E., Lim C., Sayed T., and Mosa A. I. Development of Daily Adjustment Factors for Bicycle Traffic. Journal of Transportation Engineering, Vol. 139, 2013, pp. 859–871.
Crossref
Google Scholar
13. FHWA U.S. Department of Transportation. Traffic Monitoring for Non-Motorized Traffic. In Traffic Monitoring Guide, 2013.
Google Scholar
14. Ryus P., Ferguson E., Laustsen K. M., Schneider R. J., Proulx F. R., Hull T., and Miranda-Moreno L. NCHRP Report 797: Guidebook on Pedestrian and Bicycle Volume Data Collection. Transportation Research Board of the National Academies, Washington, D.C., 2014.
Google Scholar
15. Lindsey G., Nordback K., and Figliozzi M. A. Institutionalizing Bicycle and Pedestrian Monitoring Programs in Three States. In Transportation Research Record: Journal of the Transportation Research Board, No. 2443, Transportation Research Board of the National Academies, Washington, D.C., 2014, pp. 134–142.
Crossref
Google Scholar
16. Lindsey G., Hoff K., Hankey S., and Wang X. Understanding the Use of Non-Motorized Transportation Facilities. Report No. CTS 12-24. Minnesota Department of Transportation, Saint Paul, 2012.
Google Scholar
17. Kuzmyak J. R., Walters J., Bradley M., and Kockelman K. M. NCHRP Report 770: Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. Transportation Research Board of the National Academies, Washington, D.C., 2014.
Crossref
Google Scholar
18. Hood J., Sall E., and Charlton B. A GPS-Based Bicycle Route Choice Model for San Francisco, California. Transportation Letters: International Journal of Transportation Research, Vol. 3, 2011, pp. 63–75.
Google Scholar
19. Krizek K., Barnes G., and Thompson K. Analyzing the Effect of Bicycle Facilities on Commute Mode Share over Time. Journal of Urban Planning and Development, Vol. 135, 2009, pp. 66–73.
Crossref
Google Scholar
20. Iacono M., Krizek K., and El-Geneidy A. Measuring Non-Motorized Accessibility: Issues, Alternatives, and Execution. Journal of Transport Geography, Vol. 18, 2010, pp. 133–140.
Crossref
Google Scholar
21. Hankey S., Lindsey G., Wang X., Borah J., Hoff K., Utecht B., and Xu Z. Estimating Use of Non-Motorized Infrastructure: Models of Bicycle and Pedestrian Traffic in Minneapolis, MN. Landscape and Urban Planning, Vol. 107, No. 3, 2012, pp. 307–316.
Crossref
Google Scholar
22. Wang X., Lindsey G., and Hankey S. Estimating Non-Motorized Trail Traffic Using Negative Binomial Regression Models. Journal of Urban Planning and Development, Vol. 140, 2014, pp. 1–9.
Crossref
Google Scholar
23. Jones M. G., Ryan S., Donlon L., Ledbetter L., Ragland D. R., and Arnold L. Seamless Travel: Measuring Bicycle and Pedestrian Activity in San Diego County and Its Relationship to Land Use, Transportation, Safety, and Facility Type. Safe Transportation Research and Education Center, University of California, Berkeley, 2010.
Google Scholar
24. Pulugurtha S. S., and Repaka S. R. Assessment of Models to Measure Pedestrian Activity at Signalized Intersections. In Transportation Research Record: Journal of the Transportation Research Board, No. 2073, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 39–48.
Crossref
Google Scholar
25. 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.
Crossref
Google Scholar
26. Miranda-Moreno L. F., and Fernandes D. Modeling of Pedestrian Activity at Signalized Intersections: Land Use, Urban Form, Weather, and Spatiotemporal Patterns. In Transportation Research Record: Journal of the Transportation Board, No. 2264, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 74–82.
Crossref
Google Scholar
27. Schneider R. J., Henry T., Mitman M. F., Stonehill L., and Koehler J. Development and Application of a Pedestrian Volume Model in San Francisco, California. In Transportation Research Record: Journal of the Transportation Research Board, No. 2299, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 65–78.
Crossref
Google Scholar
28. Fagnant D. J., and Kockelman K. A Direct-Demand Model for Bicycle Counts: The Impacts of Level of Service and Other Factors. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014.
Google Scholar
29. Schepers P. Does More Cycling Also Reduce the Risk of Single-Bicycle Crashes? Injury Prevention, Vol. 18, 2012, pp. 240–245.
Crossref
Google Scholar
30. Schneider R. J., Arnold L. S., and Ragland D. R. Methodology for Counting Pedestrians at Intersections: Use of Automated Counters to Extrapolate Weekly Volumes from Short Manual Counts. In Transportation Research Record: Journal of the Transportation Research Board, No. 2140, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 1–12.
Crossref
Google Scholar
31. Minneapolis Bicyclist and Pedestrian Count Report 2014. Department of Public Works, Minneapolis, Minn., 2014.
Google Scholar
32. Non-Motorized Traffic Counts: Operations and Methodology. Department of Public Works, Minneapolis, Minn., 2013.
Google Scholar
33. Owen A., Murphy B., and Levinson D. M. Access Across America: Walk 2014 Data, 2015. Accessibility Observatory, University of Minnesota. http://conservancy.umn.edu/handle/11299/173991. Accessed Nov. 6, 2015.
Google Scholar
34. Metropolitan Council. MetroGISDataFinder. www.datafinder.org. Accessed Feb. 2015.
Google Scholar
35. Su J. G., Jerrett M., and Beckerman B. A. A Distance-Decay Variable Selection Strategy for Land Use Regression Modeling of Ambient Air Pollution Exposures. Science of the Total Environment, Vol. 407, 2009, pp. 3890–3898.
Crossref
Google Scholar

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

Volume 2586, Issue 1
Pages: 48 - 58
Article first published online: January 1, 2016
Issue published: January 2016

Rights and permissions

© 2016 National Academy of Sciences.
Request permissions for this article.

Authors

Affiliations

Steve Hankey
School of Public and International Affairs, College of Architecture and Urban Studies, Virginia Polytechnic Institute and State University, 140 Otey Street, Blacksburg, VA 24061
[email protected]
View all articles by this author
Greg Lindsey
Humphrey School of Public Affairs, University of Minnesota, 130 Humphrey School, 301 19th Avenue South, Minneapolis, MN 55455
View all articles by this author

Notes

S. Hankey, [email protected].

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: 201

*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: 35

  1. Effects of data aggregation (buffer) techniques on bicycle volume esti...
    Go to citation Crossref Google Scholar
  2. Analysis of factors influencing cycling count: A micro-level study usi...
    Go to citation Crossref Google Scholar
  3. Estimation of daily bicycle traffic using machine and deep learning te...
    Go to citation Crossref Google Scholar
  4. Pedestrian crossing volume estimation at signalized intersections usin...
    Go to citation Crossref Google Scholar
  5. Estimating Pedestrian Crossing Volumes and Crashes at Midblock Locatio...
    Go to citation Crossref Google Scholar
  6. Pedestrian volume prediction with high spatiotemporal granularity in u...
    Go to citation Crossref Google Scholar
  7. Challenges and Opportunities of Emerging Data Sources to Estimate Netw...
    Go to citation Crossref Google Scholar
  8. Vulnerable road users’ crash hotspot identification on multi-lane arte...
    Go to citation Crossref Google Scholar
  9. Forecasting e-scooter substitution of direct and access trips by mode ...
    Go to citation Crossref Google Scholar
  10. Development and Validation of a Seven-County Regional Pedestrian Volum...
    Go to citation Crossref Google Scholar
  11. Enhancing non-motorist safety by simulating trip exposure using a tran...
    Go to citation Crossref Google Scholar
  12. Varying influences of the built environment on daily and hourly pedest...
    Go to citation Crossref Google Scholar
  13. Understanding the Operational Efficiency of Bicycle-Sharing Based on t...
    Go to citation Crossref Google Scholar
  14. Direct demand modelling approach to forecast cycling activity for a pr...
    Go to citation Crossref Google Scholar
  15. Real-time estimation of pedestrian volume at button-activated midblock...
    Go to citation Crossref Google Scholar
  16. Predicting bicycling and walking traffic using street view imagery and...
    Go to citation Crossref Google Scholar
  17. Estimating Pedestrian Volumes for Signalized and Stop-Controlled Inter...
    Go to citation Crossref Google Scholar
  18. A Bayesian spatial Poisson-lognormal model to examine pedestrian crash...
    Go to citation Crossref Google Scholar
  19. Is Built Environment Associated with Travel Mode Choice in Developing ...
    Go to citation Crossref Google Scholar
  20. Incorporating bicycling demand into pavement management systems for co...
    Go to citation Crossref Google Scholar
  21. Quantifying Recreational Use of an Estuary: A Case Study of Three Bays...
    Go to citation Crossref Google Scholar
  22. How to Make Dockless Bikeshare Good for Cities: Curbing Oversupplied B...
    Go to citation Crossref Google Scholar
  23. Safety in Numbers for Bicyclists at Urban Intersections
    Go to citation Crossref Google Scholar
  24. Development of a neighborhood commute mode share model using nationall...
    Go to citation Crossref Google Scholar
  25. A Pedestrian Exposure Model for the California State Highway System
    Go to citation Crossref Google Scholar
  26. Have walking and bicycling increased in the US? A 13-year longitudinal...
    Go to citation Crossref Google Scholar
  27. The Why and Where of Active Travel: Modeling Bike and Foot Traffic acr...
    Go to citation Crossref Google Scholar
  28. 美国的自行车和步行交通模式
    Go to citation Crossref Google Scholar
  29. Understanding Potential Exposure of Bicyclists on Roadways to Traffic-...
    Go to citation Crossref Google Scholar
  30. Adding temporal information to direct-demand models: Hourly estimation...
    Go to citation Crossref Google Scholar
  31. Correlates of the Built Environment and Active Travel: Evidence from 2...
    Go to citation Crossref Google Scholar
  32. Measuring Neighborhood Walkability Using Detour Factor Distributions
    Go to citation Crossref Google Scholar
  33. Traffic Peak Period Detection from an Image Processing View
    Go to citation Crossref Google Scholar
  34. Spatial models of active travel in small communities: Merging the goal...
    Go to citation Crossref Google Scholar
  35. Population-Level Exposure to Particulate Air Pollution during Active T...
    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.

Subscribe to this journal

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

Start 2 week free trial
Need help?

View options

PDF/ePub

View PDF/ePub
Download PDF

Also from Sage