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ITE at U.C. Irvine
Problem Definition
Provide limit line detection for bicycles Or Place the signal on a permanent recall/fixed time.
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Goal: Understand how to better integrate bicycling as part of the
- verall transit system.
Signalized Intersections: An Orange County Case Study ITE at U.C. - - PowerPoint PPT Presentation
Signalized Intersections: An Orange County Case Study ITE at U.C. - - PowerPoint PPT Presentation
Bicycle Detection at Signalized Intersections: An Orange County Case Study ITE at U.C. Irvine 2 Problem Definition Goal: Understand how to better integrate bicycling as part of the overall transit system. Provide limit line detection for
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Overview
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How are cities approaching the problem? Detection case study Users’ perspective Conclusion & next steps
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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
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71% 81% 95% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
ILD (10) Video (9) Push Button (7) Radar (1)
City Traffic Engineer Reviews
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The State of the Practice
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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 9th, 2015 from 4am-9pm
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Southbound approach on Lakeview Avenue & Riverdale Avenue
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N
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Iteris, Inc. Video Detectors
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Econolite Group Video Detectors
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Reno A&E Loop Detectors
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Detection Case Study – Set-up
NEMA TS-1 DETECTOR PANEL AXIS VIDEO ENCODER ETHERNET SWITCH 22 GAUGE AWG COAXIAL CABLE ETHERNET
ANAHEIM TMC
BICYCLE DETECTION DIAGRAM
Axis video encoder NEMA Cabinet Ethernet switch Anaheim TMC
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Detection Case Study - Results
Missed Detections Detections Detection Ratio Iteris, Inc. (video detection) 3 52 95% Econolite Group (video detection) 1 54 98% Reno A&E (loop detection) 10 45 82% Detection: Any individual bike successfully identified by the technology. Missed Detection: Any individual bike not identified by the technology.
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𝐸𝑓𝑢𝑓𝑑𝑢𝑗𝑝𝑜 𝑆𝑏𝑢𝑗𝑝 = 𝑂𝑣𝑛𝑐𝑓𝑠 𝑝𝑔𝐸𝑓𝑢𝑓𝑑𝑢𝑗𝑝𝑜𝑡 𝑇𝑏𝑛𝑞𝑚𝑓 𝑇𝑗𝑨𝑓
Sample Size: 55 Bikes
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User Perspective
Goal: Understand steps of
holistic success
Surveyed 4 recreational groups,
2 commuter groups, 3 mixed groups
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Understand Cities’ Approaches Understand Technologies Understand Users
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User Perspective
Previous knowledge of bike detection from self education
and personal experience
Skeptical of the overall improvement of bicyclists’ experience
- n the road with the addition of bicycle detection
More information is needed to better educate both bicycle users
and cities.
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Conclusions & Future Analysis
15 More Data
- Different site conditions
- Varying bicycle densities
- More technologies
Education and Outreach
- View users as active participants for feedback and
improvement.
Combine Research
- Bicycle detection is only part of the overall
solution.
- Cost analysis
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Acknowledgments
The City of Anaheim
John Thai, Principal Traffic Engineer
Participating Companies
Iteris, Inc Econolite Group Reno A&E
Participating Survey Respondents
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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.
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95% 98% 82% 70% 75% 80% 85% 90% 95% 100%
Iteris, Inc. (video detection) Econolite Group (video detection) Reno A&E (loop detection)
Detection Ratio
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