Computer vision-based assessment of cyclist-tram track interactions for predictive modeling of crossing success
(2023) In Journal of Safety Research 87. p.202-216- Abstract
Introduction: Single Bicycle Brashes (SBCs) are common, and underreported in official statistics. In urban environments, light rail tram tracks are a frequent factor, however, they have not yet been the subject of engineering analysis. Method: This study employs video-based analysis at nine Dublin city centre locations and introduces a predictive model for crossing success on tram tracks, utilising cyclist crossing angles within a Surrogate Measure of Safety (SMoS) framework. Additionally, Convolutional Neural Networks (CNNs) were explored for automatic estimation of crossing angles. Results: Modelling results indicate that cyclist crossing angle is a strong predictor of crossing success, and that cyclist velocity is not. Findings also... (More)
Introduction: Single Bicycle Brashes (SBCs) are common, and underreported in official statistics. In urban environments, light rail tram tracks are a frequent factor, however, they have not yet been the subject of engineering analysis. Method: This study employs video-based analysis at nine Dublin city centre locations and introduces a predictive model for crossing success on tram tracks, utilising cyclist crossing angles within a Surrogate Measure of Safety (SMoS) framework. Additionally, Convolutional Neural Networks (CNNs) were explored for automatic estimation of crossing angles. Results: Modelling results indicate that cyclist crossing angle is a strong predictor of crossing success, and that cyclist velocity is not. Findings also highlight the prevalence of external factors which limit crossing angles for cyclists. In particular, kerbs are a common factor, along with passing/approaching vehicles or other cyclists. Furthermore, results indicate that further training on a relatively small sample of 100 domain-specific examples can achieve substantial accuracy improvements for cyclist detection (from 0.31AP0.5 to 0.98AP0.5) and crossing angle inference from traffic camera footage. Conclusions: Ensuring safe crossing angles is important for cyclist safety around tram tracks. Infrastructural planners should aim for intuitive, self-explainable road layouts that allow for and encourage crossing angles of 60° or more – ideally 90°. Practical Applications: The SMoS framework and the open-source SafeCross1 application offer actionable insights and tools for enhancing cyclist safety around tram tracks.
(Less)
- author
- Gildea, Kevin LU ; Hall, Daniel ; Mercadal-Baudart, Clara ; Caulfield, Brian and Simms, Ciaran
- organization
- publishing date
- 2023-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Computer vision, Single bicycle crashes, Surrogate measures of safety, Tram tracks, Video analysis
- in
- Journal of Safety Research
- volume
- 87
- pages
- 15 pages
- publisher
- Elsevier
- external identifiers
-
- pmid:38081695
- scopus:85177038082
- ISSN
- 0022-4375
- DOI
- 10.1016/j.jsr.2023.09.017
- language
- English
- LU publication?
- yes
- id
- 6b117d6e-cf3a-4dba-b0de-18c3d1e8c48d
- date added to LUP
- 2024-01-10 13:32:08
- date last changed
- 2024-04-25 09:30:47
@article{6b117d6e-cf3a-4dba-b0de-18c3d1e8c48d,
abstract = {{<p>Introduction: Single Bicycle Brashes (SBCs) are common, and underreported in official statistics. In urban environments, light rail tram tracks are a frequent factor, however, they have not yet been the subject of engineering analysis. Method: This study employs video-based analysis at nine Dublin city centre locations and introduces a predictive model for crossing success on tram tracks, utilising cyclist crossing angles within a Surrogate Measure of Safety (SMoS) framework. Additionally, Convolutional Neural Networks (CNNs) were explored for automatic estimation of crossing angles. Results: Modelling results indicate that cyclist crossing angle is a strong predictor of crossing success, and that cyclist velocity is not. Findings also highlight the prevalence of external factors which limit crossing angles for cyclists. In particular, kerbs are a common factor, along with passing/approaching vehicles or other cyclists. Furthermore, results indicate that further training on a relatively small sample of 100 domain-specific examples can achieve substantial accuracy improvements for cyclist detection (from 0.31AP<sub>0.5</sub> to 0.98AP<sub>0.5</sub>) and crossing angle inference from traffic camera footage. Conclusions: Ensuring safe crossing angles is important for cyclist safety around tram tracks. Infrastructural planners should aim for intuitive, self-explainable road layouts that allow for and encourage crossing angles of 60° or more – ideally 90°. Practical Applications: The SMoS framework and the open-source SafeCross<sup>1</sup> application offer actionable insights and tools for enhancing cyclist safety around tram tracks.</p>}},
author = {{Gildea, Kevin and Hall, Daniel and Mercadal-Baudart, Clara and Caulfield, Brian and Simms, Ciaran}},
issn = {{0022-4375}},
keywords = {{Computer vision; Single bicycle crashes; Surrogate measures of safety; Tram tracks; Video analysis}},
language = {{eng}},
pages = {{202--216}},
publisher = {{Elsevier}},
series = {{Journal of Safety Research}},
title = {{Computer vision-based assessment of cyclist-tram track interactions for predictive modeling of crossing success}},
url = {{http://dx.doi.org/10.1016/j.jsr.2023.09.017}},
doi = {{10.1016/j.jsr.2023.09.017}},
volume = {{87}},
year = {{2023}},
}