Video capture and analysis of cyclists using infrastructure in the ACT through machine learning / JRR Mackenzie, G Ponte
- Bib ID:
- 8809097
- Format:
- Book
- Author:
- Mackenzie, J. R. R, author
- Online Access:
- National edeposit
- Related Online Resources:
- Access Conditions:
- National edeposit: Available online
- Description:
-
- Adelaide : Centre for Automotive Safety Research,The University of Adelaide, 2022
- ©2022
- 1 online resource (iv, 35 pages) : colour illustrations, colour charts, colour maps.
- ISBN:
- 9781925971361
- Series:
- CASR report series ; CASR202 1449-2237
- Summary:
-
"The implementation of infrastructure to reduce traffic conflicts and improve road safety for cyclists is critical. However, potential benefits resulting from strategic interventions can only be achieved if there is corresponding compliance or good utilisation of that infrastructure. Traditional methods for evaluating cycling behaviours and interactions with the traffic system either involve expensive roadside observations, which can influence cycling behaviours through the observer effect, or by using induction or pneumatic tube system which can only yield count information and cannot be used in mixed traffic situations. This study used long duration portable video cameras to, somewhat covertly, record mixed road traffic, with the captured video subsequently being processed with bespoke machine learning software. This innovative process required negligible set-up or manual processing time and a researcher was only required to do a desktop analysis on 6% of the total traffic video recorded. Agencies requiring evaluations of cyclist interactions with specific infrastructure or behaviours at specific locations could use this rapid and cost effective process to assist with data collection and analyses to inform or optimise their cycling safety strategies." -- Colophon.
- Full contents:
-
- 1. Introduction
- 2. Identification of a suitable camera system
- 3. Selection of locations for surveillance
- 4. Installation of cameras for data collection
- 5. Video processing
- 6. Results
- 6.1. Site A
- 6.2. Site B
- 6.3. Site C
- 6.4. Site D
- 7. Discussion
- Acknowledgements
- References
- Appendix A. Stakeholder engagement letter
- Appendix B. Camera signage.
- Notes:
-
- Keywords: Traffic video analysis, machine learning, cyclist detection, cyclist safety -- Colophon.
- September 2022.
- Sponsored by ACT Road Safety Fund.
- Also available online at publisher website as of 09/03/2023.
- Subject:
-
- Cycling -- Australia -- Australian Capital Territory -- Safety measures
- Cycling -- Australian Capital Territory -- Safety measures
- Traffic engineering -- Research -- Australia -- Australian Capital Territory
- Traffic engineering -- Research -- Australian Capital Territory
- Traffic monitoring -- Australia -- Australian Capital Territory
- Traffic monitoring -- Australian Capital Territory
- Traffic safety -- Research -- Australia -- Australian Capital Territory
- Traffic safety -- Research -- Australian Capital Territory
- Cycling -- Safety measures
- Traffic engineering -- Research
- Traffic monitoring
- Traffic safety -- Research
- Australian Capital Territory
- Other authors/contributors:
- Also Titled:
- Video capture and analysis of cyclists using infrastructure in the Australian Capital Territory through machine learning
- Terms of Use:
- Licensed under Creative Commons. Attribution 4.0 International (CC BY 4.0)
- Copyright:
-
In Copyright
You may copy under some circumstances, for example you may copy a portion for research or study. Order a copy through Copies Direct to the extent allowed under fair dealing. Contact us for further information about copying.
Copyright status was determined using the following information:
- Material type:
- Literary Dramatic Musical
- Published status:
- Published
- Publication date:
- 2022
Copyright status may not be correct if data in the record is incomplete or inaccurate. Other access conditions may also apply. For more information please see: Copyright in library collections.
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