This study was conducted to determine the prevalence and reliability of risk factors collected on uninjured cyclists-pedestrians in Edmonton, Alberta, and what characteristics predict cyclist-pedestrian visibility. At randomly selected locations from July 2004 to August 2004, two independent observers recorded cyclist-pedestrian characteristics such as age, sex, clothing color, use of reflectors, flags, helmets, and a subjective impression of visibility. Data were collected on 836 individuals; most were either walking/jogging (approximately 63%) or cycling (approximately 33%). For non-cyclists, the prevalence of bright colored clothing on the trunk ranged from 12.7 to 14.7%. Few people used any kind of reflective strips. Inter-observer agreement (Kappa) ranged from 0.37 (visibility assessment) to 0.99 (sex). For cyclists, 17-19% of headgear was brightly colored, and 13-14% was white. Approximately one-fourth had a front light; half had a rear reflector. Few cyclists used a flag and just over half used spoke reflectors. Kappa ranged from 0.35 (observer assessed speed) to 0.95 (head gear and sex). A major trunk color of orange, red, yellow or white resulted in a higher visibility rating for both cyclists and pedestrians. The results indicate a low prevalence of visibility aid use among cyclists and pedestrians, but there appears to be acceptable inter-observer reliability for most data collected. Further work is required before an overall visibility rating can be used in place of component scores.
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http://dx.doi.org/10.1016/j.aap.2006.07.010 | DOI Listing |
Accid Anal Prev
December 2024
Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI 48910, USA. Electronic address:
Navigating intersections is a major challenge for autonomous vehicles (AVs) because of the complex interactions between different roadway user types, conflicting movements, and diverse operational and geometric features. This study investigated intersection-related AV-involved traffic conflicts by analyzing the Arogoverse-2 motion forecasting dataset to understand the driving behavior of AVs at intersections. The conflict scenarios were categorized into AV-involved and no AV conflict scenarios.
View Article and Find Full Text PDFAccid Anal Prev
December 2024
Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge CB3 0FA, United Kingdom. Electronic address:
Shared spaces prioritise the role of micromobility in urban environments by separating vulnerable road users from motorised vehicles, aiming to enhance both actual and perceived safety. However, the presence of various transport modes, such as pedestrians, cyclists and e-scooters, with differing navigation behaviours, increases the heterogeneity of these spaces and impacts the perception of safety. Despite the increasing use of e-scooters, the safety perceptions of e-scooter riders remain largely underexplored in the literature.
View Article and Find Full Text PDFSensors (Basel)
October 2024
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Existing 3D object detection frameworks in sensor-based applications heavily rely on large-scale annotated data to achieve optimal performance. However, obtaining such annotations from sensor data-like LiDAR or image sensors-is both time-consuming and costly. Semi-supervised learning offers an efficient solution to this challenge and holds significant potential for sensor-driven artificial intelligence (AI) applications.
View Article and Find Full Text PDFInjury
October 2023
School of Public Affairs, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China. Electronic address:
Background: China has the highest number of road injury deaths in the world. The aim of this study was to determine the long-term incidence and mortality trends of road injuries in China between 1990 and 2019 and to make projections up to 2030.
Methods: Incident and death data were extracted from the Global Burden of Disease (GBD) 2019 study and population data were extracted from the GBD 2019 and World Population Prospects 2019 studies.
Traffic Inj Prev
November 2024
Waymo LLC, Mountain View, California.
Objective: Understanding and modeling baseline driving safety risk in dense urban areas represents a crucial starting point for automated driving system (ADS) safety impact analysis. The purpose of this study was to leverage naturalistic vulnerable road user (VRU) collision data to quantify collision rates, crash severity, and injury risk distributions in the absence of objective injury outcome data.
Methods: From over 500 million vehicle miles traveled, a total of 335 collision events involving VRUs were video verified and reconstructed (126 pedestrians, 144 cyclists, and 65 motorcyclists).
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