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 bicycles Or Place the signal on a permanent recall/fixed time.

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

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

5 The State of the Practice

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 N Southbound approach on Lakeview Avenue & Riverdale Avenue 7

8 Iteris, Inc. Video Detectors 8

9 Econolite Group Video Detectors 9

10 Reno A&E Loop Detectors 10

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

12 Detection Case Study - Results Detection: Any individual bike successfully Sample Size: 55 Bikes identified by the technology. 𝐸𝑓𝑢𝑓𝑑𝑢𝑗𝑝𝑜 𝑆𝑏𝑢𝑗𝑝 = 𝑂𝑣𝑛𝑐𝑓𝑠 𝑝𝑔𝐸𝑓𝑢𝑓𝑑𝑢𝑗𝑝𝑜𝑡 Missed Detection: Any individual bike not 𝑇𝑏𝑛𝑞𝑚𝑓 𝑇𝑗𝑨𝑓 identified by the technology. Missed Detections Detection Ratio Detections Iteris, Inc. 3 52 95% (video detection) Econolite Group 1 54 98% (video detection) Reno A&E 10 45 82% (loop detection)

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

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

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

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