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Pedestrian and Bicyclist Data

This presentation by Shawn Turner, P.E., at the Texas Transportation Institute discusses the critical importance of bicycling and walking data in urban planning. It outlines the types of data needed, such as facility inventories, usage characteristics, and crash statistics, to support informed policy decisions and effective investments in transportation infrastructure. The session also highlights the challenges in collecting data for these “forgotten modes” of transportation and presents innovative data collection methods and grassroots initiatives to improve monitoring and performance measurement.

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Pedestrian and Bicyclist Data

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  1. Pedestrian and Bicyclist Data Shawn Turner, P.E. Texas Transportation Institute H-GAC Brown Bag Luncheon Houston, TX ~ October 24, 2011

  2. Overview • Why is bicycling & walking data important? • What data do we need? • National / international activities

  3. Why is bicycling and walkingdata important? • Same reasons as for other modes • Support policy decisions/changes • Plan for cost-effective investments • Design safe facilities and infrastructure • Measure performance and progress toward goals • “What gets measured, gets done” • “If you’re not counted, you don’t count”

  4. Portland Examples Source: Roger Geller, City of Portland

  5. Portland Examples Source: Roger Geller, City of Portland

  6. Portland Examples Source: Roger Geller, City of Portland

  7. What data do we need? • Maintain focus on users and uses of data! • Who needs information (based on your data)? • What decisions are they making? • Avoid collecting data only because: • “that’s what our program plan lists…” • “that’s what my boss said to do…” • “that’s what others are doing…”

  8. Bicycle and Pedestrian Data • Facilities (inventory) • LOS, quality of travel • Usage, trip & user characteristics • Counts, surveys • Crash & safety data • User preferences • Secondary data • Research and evaluation data

  9. Data & Monitoring Challenges • “The forgotten modes” (Tom Larson) • Typically lower priority, fewer resources • Typically small numbers, high variability • Typically on city streets, not major highways • Difficult to automatically count/measure • Scale of facilities

  10. Automated Counters

  11. Field Tests of Counters • Texas A&M Campus, College Station • ~2 hours, 470 people (15% in groups) • Brand A, 34% low • Brand B, 11% low • Pfuger Bridge (jogging trail), Austin • In ~2 hours, 967 people (47% in groups) • Brand A, 36% low • Brand B, 26% low • Brand C, 24% low

  12. Austin Regional Monitoring Program State-of-the-practice review Purchased 2 permanent counters and 2 portable counters from Eco Counter http://www.campotexas.org/programs_bicped.php

  13. Opportunities and Advances - 1 • “Grassroots” efforts from within the pedestrian/bicyclist community

  14. Opportunities and Advances - 2 • Map and navigation industry efforts

  15. Opportunities and Advances - 2 • Map and navigation industry efforts

  16. Opportunities and Advances - 2 • Map and navigation industry efforts

  17. Opportunities and Advances - 3 • Pocket-sized, location-aware mobile devices + crowdsourcing and social media

  18. Opportunities and Advances - 3 • Pocket-sized, location-aware mobile devices + crowdsourcing and social media

  19. Opportunities and Advances - 3 Source: Jennifer Dill, Portland State Univ.

  20. National Activities • Alta/ITE National Bicycle and Pedestrian Documentation Project • ABW Benchmarking Report • FHWA Update of Traffic Monitoring Guide • Supporting state-of-practice review • NCHRP 8-78: Demand Forecasting Methods • NCHRP 7-19: Count Collection Methods/Equipment • TRB Ped/Bike Data Subcommittee • And probably lots more!!

  21. Intl. Scan Tour - Monitoring • Bike “barometers”: counters in highly visible locations

  22. Queen Louise Bridge, Copenhagen:36,000 ADBT

  23. Concluding Thoughts • Progress is being made • Equipment • Monitoring programs • Travel surveys • Focus on uses and users • Who? • What decisions? • Capture passion/dedication of advocates • Mobile devices for crowd-sourced data

  24. Questions? shawn-turner@tamu.edu (979) 845-8829 http://tti.tamu.edu

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