Video capture and analysis of cyclists using infrastructure in the ACT through machine learning
| dc.contributor.assignee | ACT Road Safety Fund | |
| dc.contributor.author | Mackenzie, J. | |
| dc.contributor.author | Ponte, G. | |
| dc.date.issued | 2022 | |
| dc.description.abstract | 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. | |
| dc.description.statementofresponsibility | JRR Mackenzie, G Ponte | |
| dc.identifier.isbn | 9781925971361 | |
| dc.identifier.issn | 1449-2237 | |
| dc.identifier.orcid | Mackenzie, J. [0000-0002-7161-1250] | |
| dc.identifier.orcid | Ponte, G. [0000-0002-1485-8433] | |
| dc.identifier.uri | https://hdl.handle.net/2440/137444 | |
| dc.language.iso | en | |
| dc.publisher | Centre for Automotive Safety Research | |
| dc.publisher.place | Adelaide | |
| dc.relation.ispartofseries | CASR research reports; 202 | |
| dc.rights | © The University of Adelaide 2022 | |
| dc.source.uri | https://casr.adelaide.edu.au/publications/list/?id=2036 | |
| dc.subject | Traffic video analysis; machine learning; cyclist detection; cyclist safety | |
| dc.title | Video capture and analysis of cyclists using infrastructure in the ACT through machine learning | |
| dc.type | Report | |
| pubs.publication-status | Published |