An evaluation of bicycle passing distances in the ACT

To evaluate bicycle passing distances in the Australian Capital Territory (ACT), specialised passing distance measurement devices (PDMDs) were installed on a sample of 23 cyclists who ride in the ACT. Passing distance data and GPS data was collected by cyclists using the PDMDs for a four-week period, during a trial phase of a newly legislated minimum passing distance (MPD) rule. The MPD rule requires drivers to provide more than 1 metre of space when passing a cyclist on a road with a speed limit of 60 km/h or below, and 1.5 meters of space when passing a cyclist on a road with a speed limit above 60 km/h Analysis of the data collected in the study identified 16,476 passing events during 6,531 kilometres of cycling, over a period of 271 riding hours. Non-compliance with the MPD rule on roads zoned 60 km/h or less was 2.7% and the mean passing distance was 1.85 metres. On roads zoned greater than 60 km/h non-compliance was 11.2% and the mean passing distance was 1.97 metres. The degree of non-compliance varied considerably with road characteristics and location.JRR Mackenzie, JK Dutschke, G Pont

An investigation of cyclist passing distances in the Australian Capital Territory

Available online 11 March 2021In Australia, cycling hospitalisations are increasing and the cycling participation rate is stagnating. In an effort to improve cyclist safety, many Australian jurisdictions have mandated a minimum passing distance that vehicles much provide when overtaking a cyclist on a public road, including the Australian Capital Territory (ACT). However, it is not currently clear how vehicle-cyclist passing distances are affected by various parameters such as the road environment, the vehicles involved, or the speed limit. This naturalistic bicycle riding study examined data from passing distance measurement devices that were installed on the bicycles of volunteer cyclists who ride in the ACT, to explore how passing distances and compliance with the minimum passing distance were affected by several parameters. Over a four-week period, 23 volunteer cyclist participants undertook 465 journeys and travelled 6531 km over a total period of 271 h. There were 10,959 passing events identified on roads zoned greater than 60 km/h (high speed roads) of which 1349 (12.3 %) were non-compliant. On roads zoned 60 km/h or less (low speed roads) there were 5517 passing events of which 153 (2.8 %) were non-compliant. Regression analyses showed that differences in passing distance and non-compliance with the minimum passing distance were associated with road classification, bike lane presence, and speed limit. The results were mixed but, in general, passing distances were greater on roads with a lower (hierarchy) classification and on motorways as well as on roads with higher speed limits. An exception to this was roads with a speed limit of 50 km/h where passing distances were closer in comparison to roads with a speed limit of 60 km/h. Bike lanes were generally associated with an increase in passing distance except on ‘trunk’ classified roads, where a bike lane resulted in closer passing events. This suggests that on trunk roads, which are assumed to carry large amounts of traffic, bike lanes may be insufficient to offer protection to cyclists and additional measures may be required.J.R.R Mackenzie , J.K. Dutschke, G. Pont

Car drivers with an AIS2+ spine injury: Description of a sample from South Australia

Car drivers from a database of road users discharged from the Royal Adelaide Hospital (RAH) between 1 July 2015 and 30 December 2017 following a road crash were analysed in this study. The hospital information was supplemented with details of the crash, obtained from the Police and the road authority. This study focused on those who had a spine injury coded at least 2 on the Abbreviated Injury Scale (AIS2+). This sample is from a single hospital, and there are several likely biases. Results. There were 518 car drivers with one or more AIS2+ injuries, of whom 152 had one or more AIS2+ spine injuries, mostly vertebral fractures. Of these 152, the maximum spine AIS score was 2 for 87%, 3 for 12%, and >3 for 1%. Tables and Figures are provided for characteristics of the spine-injured drivers and their accidents: gender, age group, crash location, speed limit, crash type, impact type, vehicle year, seatbelt use, Injury Severity Score, and days in hospital. Discussion. The biases in the dataset mean that there is no suitable comparison group. Nevertheless, data is given for three groups that provide some context: car drivers with an AIS2+ injury who did not have an AIS2+ spine injury, car drivers who did not have an AIS2+ injury, and a sample from the TARS database. (TARS refers to Traffic Accident Reporting System, that is, the accident reports that originate with the Police.) The hospitalisation characteristics show that this cohort/sample AIS2+ spine injury group is more seriously injured than other AIS2+ injured car drivers.JK Dutschke, TL Lindsay, TP Hutchinson, CF Jone

Biomechanical studies in an ovine model of non-accidental head injury

Abstract not availableR.W.G. Anderson, B. Sandoz, J.K. Dutschke, J.W. Finnie, R.J. Turner, P.C. Blumbergs, J. Manavis, R. Vin