Bicycle Detection at Signalized Intersections: An Orange County Case Study. ITE at U.C. Irvine

Transcription

1 Bicycle Detection at Signalized Intersections: An Orange County Case Study ITE at U.C. Irvine

2 2 Problem Definition Goal: Understand how to better integrate bicycling as part of the overall transit system. Provide limit line detection for bicycles Or Place the signal on a permanent recall/fixed time.

3 3 Overview How are cities approaching the problem? Detection case study Users perspective Conclusion & next steps

4 4 State of the Practice + Goal: Understand what is currently being done + Developed detailed survey to identify what cities are currently using. + Contacted 34 cities + 20 completed surveys 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% City Traffic Engineer Reviews 71% 81% ILD (10) Video (9) 95% Push Button (7) 100% Radar (1)

5 The State of the Practice 5

6 6 Detection Case Study Set-up Goal: Test how well current detection technologies work. Collaborated with the City of Anaheim to test three different technologies: Iteris, Inc. (video detection) Econolite Group (video detection) Reno A&E (inductive loops) Analyzed 17 hours of data Collected data on Saturday, May 9 th, 2015 from 4am-9pm

7 7 N Southbound approach on Lakeview Avenue & Riverdale Avenue 7

8 8 Iteris, Inc. Video Detectors 8

9 9 Econolite Group Video Detectors 9

10 10 Reno A&E Loop Detectors 10

11 11 Detection Case Study Set-up BICYCLE DETECTION DIAGRAM Anaheim ANAHEIM TMC TMC Axis AXIS VIDEO ENCODER video encoder Ethernet ETHERNET SWITCH switch TS-1 NEMA DETECTOR PANEL Cabinet 22 GAUGE AWG COAXIAL CABLE ETHERNET

12 12 Detection Case Study - Results Detection: Any individual bike successfully identified by the technology. Sample Size: 55 Bikes Missed Detection: Any individual bike not identified by the technology. Detection Ratio = Number ofdetections Sample Size Iteris, Inc. (video detection) Econolite Group (video detection) Reno A&E (loop detection) Missed Detections Detections Detection Ratio % % %

13 13 User Perspective Goal: Understand steps of holistic success Understand Users Understand Cities Approaches Surveyed 4 recreational groups, 2 commuter groups, 3 mixed groups Understand Technologies

14 14 User Perspective Previous knowledge of bike detection from self education and personal experience Skeptical of the overall improvement of bicyclists experience on the road with the addition of bicycle detection More information is needed to better educate both bicycle users and cities.

15 15 Conclusions & Future Analysis More Data Different site conditions Varying bicycle densities More technologies Cost analysis Education and Outreach View users as active participants for feedback and improvement. Combine Research Bicycle detection is only part of the overall solution.

16 Acknowledgments The City of Anaheim John Thai, Principal Traffic Engineer Participating Companies Iteris, Inc Econolite Group Reno A&E Participating Survey Respondents 16

17 17 Summary and Review Objective: Better integrate bicycling as part of the existing transportation system. Results: Bicycle detection technologies work and play a key role, but more factors are required to fully integrate bicycles into the transportation system. 100% 95% 90% 85% 80% 75% 70% 95% Iteris, Inc. (video detection) Detection Ratio 98% Econolite Group (video detection) 82% Reno A&E (loop detection) More information at: