Cycling near misses: a review of the current methods, challenges and the potential of an AI-embedded system-Reference-Cited by-同舟云学术

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Cycling near misses: a review of the current methods, challenges and the potential of an AI-embedded system

Published:2020-11-04 Issue:3 Volume:41 Page:304-328
ISSN:0144-1647
Container-title:Transport Reviews
language:en
Short-container-title:Transport Reviews

Author:

Ibrahim Mohamed R.¹^ORCID,Haworth James¹^ORCID,Christie Nicola²^ORCID,Cheng Tao¹^ORCID,Hailes Stephen³

Affiliation:

1. SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London (UCL), London, UK

2. Centre for Transport Studies (CTS), Department of Civil, Environmental and Geomatic Engineering, University College London (UCL), London, UK

3. Autonomous Systems Group, Department of Computer Science, University College London (UCL), London, UK

Funder

UCL Overseas Research Scholarship

Road Safety Trust

NVIDIA

GPU grant

Publisher

Informa UK Limited

Subject

Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/01441647.2020.1840456

Reference92 articles.

1. Investigating the nature and impact of reporting bias in road crash data

2. Cycling near misses: Their frequency, impact, and prevention

3. Inequalities in self-report road injury risk in Britain: A new analysis of National Travel Survey data, focusing on pedestrian injuries

4. Investigating the rates and impacts of near misses and related incidents among UK cyclists

5. Predictors of the frequency and subjective experience of cycling near misses: Findings from the first two years of the UK Near Miss Project

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Computer vision and statistical insights into cycling near miss dynamics;Scientific Reports;2024-09-10

2. FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases;PLOS ONE;2024-06-13

3. A Benchmark for Cycling Close Pass Detection from Video Streams;2024

4. Deep transfer learning-based anomaly detection for cycling safety;Journal of Safety Research;2023-12

5. Key Contributory Factors Influencing Cycling Safety: Comprehensive Review Based on Accident Data and Literature Survey;ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering;2023-09

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