Collisions at rail level crossings (RLXs) are typically high-severity and high-cost, often involving serious injuries, fatalities and major disruptions to the transport network. Most research examining behaviour at RLXs has focused exclusively on drivers and consequently there is little knowledge on how other road users make decisions at RLXs. We collected drivers', motorcyclists', bicyclists' and pedestrians' self-reported daily experiences at RLXs for two weeks, focusing on behaviour, decision-making and information use in the presence of a train and/or activated RLX signals. Both information use and behaviour differed between road users. Visual information (e.g. flashing lights) was more influential for motorists, whereas pedestrians and cyclists relied more on auditory information (e.g. bells). Pedestrians were also more likely to violate active RLX warnings and/or cross before an approaching train. These results emphasise the importance of adopting holistic RLX design approaches that support cognition and behaviour across for all road users. Practitioner Summary: This study explores how information use and decision-making at rail level crossings (RLXs) differ between road user groups, using a two-week self-report study. Most users make safe decisions, but pedestrians are most likely to violate RLX warnings. Information use (visual vs. auditory) also differs substantially between road user groups.
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http://dx.doi.org/10.1080/00140139.2015.1095356 | DOI Listing |
Biol Rev Camb Philos Soc
January 2025
Wildlife Observatory of Australia (WildObs), Queensland Cyber Infrastructure Foundation (QCIF), Brisbane, Queensland, 4072, Australia.
Camera traps are widely used in wildlife research and monitoring, so it is imperative to understand their strengths, limitations, and potential for increasing impact. We investigated a decade of use of wildlife cameras (2012-2022) with a case study on Australian terrestrial vertebrates using a multifaceted approach. We (i) synthesised information from a literature review; (ii) conducted an online questionnaire of 132 professionals; (iii) hosted an in-person workshop of 28 leading experts representing academia, non-governmental organisations (NGOs), and government; and (iv) mapped camera trap usage based on all sources.
View Article and Find Full Text PDFSci Data
January 2025
Hochschule für Technik und Wirtschaft Berlin (HTW Berlin), Berlin, Germany.
Road unevenness significantly impacts the safety and comfort of traffic participants, especially vulnerable groups such as cyclists and wheelchair users. To train models for comprehensive road surface assessments, we introduce StreetSurfaceVis, a novel dataset comprising 9,122 street-level images mostly from Germany collected from a crowdsourcing platform and manually annotated by road surface type and quality. By crafting a heterogeneous dataset, we aim to enable robust models that maintain high accuracy across diverse image sources.
View Article and Find Full Text PDFJ Agromedicine
January 2025
Injury Prevention and Community Outreach, University of Iowa Health Care Stead Family Children's Hospital, University of Iowa, Iowa City, IA, USA.
Most deaths due to all-terrain vehicles (ATVs) and utility task vehicles (UTVs) occur on public roads, despite manufacturers' warnings that they are not designed for roadway use. Our study objective was to determine rural residents' use, knowledge, and attitudes regarding ATVs/UTVs on public roads. A convenience sample of 2022 Farm Progress Show attendees were surveyed ( = 361).
View Article and Find Full Text PDFHeliyon
July 2024
The Design School, Taylor's University, Subang Jaya, Selangor, Malaysia.
Road traffic injuries are one of the main causes of death among children. In recent years, the incidence and casualty rates of traffic accidents have increased year by year, which is a major challenge faced by safety organizations and governments in various countries, especially in developing countries. Among them, correct understanding of road traffic signs is a factor in reducing accidents.
View Article and Find Full Text PDFNanotechnology
January 2025
Anhui Agricultural University, Hefei, 230036, P. R. China, Hefei, 230036, CHINA.
Strain sensing fabrics are able to sense the deformation of the outside world, bringing more accurate and real-time monitoring and feedback to users. However, due to the lack of clear sensing mechanism for high sensitivity and high linearity carbon matrix composites, the preparation of high performance strain sensing fabric weaving is still a major challenge. Here, an elastic polyurethane(PU)-based conductive fabric(GCPU) with high sensitivity, high linearity and good hydrophobicity is prepared by a novel synergistic conductive network strategy.
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