1. Automated Bicycle Data Collection: A Case Study from Portland, OR Western District Annual ITE Meeting June 26th, 2012 Presenter: Sirisha Kothuri skothuri@pdx.edu Authors: Sirisha Kothuri Titus Reynolds Christopher Monsere Peter Koonce Automated Bicycle Data Collection 1

2. Outline  Introduction  Study Area  Bicycle Counts  Bicycle Delay  Summary  Next Steps Automated Bicycle Data Collection 2

3. 3 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Our intentions are to be as sustainable a city as possible. That means socially, that means environmentally and that means economically. The bike is great on all three of those factors. You just can’t get a better transportation return on your investment than you get with promoting bicycling. – Mayor Sam Adams Source: P Koonce

4. Portland’s Bicycle Network Automated Bicycle Data Collection 4 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps  2010 Bicycle Network  Bicycle boulevards  Bicycle lanes  Off street paths  Total system (314 mi) Source: R Geller

5. Portland’s Bicycle Network Automated Bicycle Data Collection 5 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Source: R Geller

6. Where are we going? Automated Bicycle Data Collection 6 Introduction | Study Area | Bicycle Data | Pedestrian Data | Summary | Next Steps

7. Why is this important?  Bicycle Data  Gaps/Deficiencies in data  Evaluation of system performance  Current demand estimation  Future infrastructure and operational needs  Prioritize investments  Improve safety Automated Bicycle Data Collection 7 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps

8. Objective  Utilize existing infrastructure to develop a long term monitoring and collection system to monitor bicycle activity. Automated Bicycle Data Collection 8 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps

9. Study Area Automated Bicycle Data Collection 9 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps I-205 I-5 I-84 US-26 Downtown OR -217  Bicycle Data  Bicycle Counts  Bicycle Delay  Pedestrian Data  Push button actuations  Pedestrian Delay

10. Bicycle Data Automated Bicycle Data Collection 10 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps  Single inductive loops  Advance loop counts  Criteria for counts  Bicycle lane  Advance loop in bike lane  Individual loop wire  Communication

11. Automated Bicycle Data Collection 11 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Count Verification Outbound Loop Inbound Loop  Video and loop counts  Underestimation of loop counts

12. Automated Bicycle Data Collection 12 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Daily Trend

13. Automated Bicycle Data Collection 13 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Weekday Trends

14. Automated Bicycle Data Collection 14 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Bicycle Delay  Active at one intersection  Internal logic commands  Latch is set -  Bike is detected  Light status ≠ green  Latch is released  Light status = green  Delay = Duration of latch  Maximum delay per cycle

15. Automated Bicycle Data Collection 15 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Bicycle Delay  Delay Reduction Strategies  Coordinated  Free  Increase in permissive length

16. PORTAL  Regional data archive  Data currently archived:  Freeway loop detector  Weather  Incidents  Bluetooth  Bike and Ped  Arterial Automated Bicycle Data Collection 16 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps http://portal.its.pdx.edu/

17. Conclusions  Growing need for bicycle data  Operations  Planning  Bicycle counts from inductive loops  Cost effective  Potential for undercounting  Affected by placement, sensitivity and calibration Automated Bicycle Data Collection 17 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps

18. Next Steps  Expansion and verification of counts  Expansion of bicycle delay to other intersections  Optimizing signal timing based on delay Automated Bicycle Data Collection 18 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps

19. Thank you! Sirisha Kothuri skothuri@pdx.edu Titus Reynolds titus.reynolds@portland oregon.gov Christopher Monsere monsere@pdx.edu Peter Koonce peter.koonce@portlandoregon.gov Automated Bicycle Data Collection 19 http://portal.its.pdx.edu/