Ross Blackman

Comparison of moped, scooter and motorcycle crash risk and crash severity.

The increased popularity of mopeds and motor scooters in Australia and elsewhere in the last decade has contributed substantially to the greater use of powered two-wheelers (PTWs) as a whole. As the exposure of mopeds and scooters has increased, so too has the number of reported crashes involving those PTW types, but there is currently little research comparing the safety of mopeds and, particularly, larger scooters with motorcycles. This study compared the crash risk and crash severity of motorcycles, mopeds and larger scooters in Queensland, Australia. Comprehensive data cleansing was undertaken to separate motorcycles, mopeds and larger scooters in police-reported crash data covering the five years to 30 June 2008. The crash rates of motorcycles (including larger scooters) and mopeds in terms of registered vehicles were similar over this period, although the moped crash rate showed a stronger downward trend. However, the crash rates in terms of distance travelled were nearly four times higher for mopeds than for motorcycles (including larger scooters). More comprehensive distance travelled data is needed to confirm these findings. The overall severity of moped and scooter crashes was significantly lower than motorcycle crashes but an ordered probit regression model showed that crash severity outcomes related to differences in crash characteristics and circumstances, rather than differences between PTW types per se. Greater motorcycle crash severity was associated with higher (>80km/h) speed zones, horizontal curves, weekend, single vehicle and nighttime crashes. Moped crashes were more severe at night and in speed zones of 90km/h or more. Larger scooter crashes were more severe in 70km/h zones (than 60km/h zones) but not in higher speed zones, and less severe on weekends than on weekdays. The findings can be used to inform potential crash and injury countermeasures tailored to users of different PTW types.

At work or play: a comparison of private property vehicle crashes with those occurring on public roads in north Queensland.

OBJECTIVE To define characteristics of vehicle crashes occurring on rural private property in north Queensland with an exploration of associated risk factors. DESIGN Descriptive analysis of private property crash data collected by the Rural and Remote Road Safety Study. SETTING Rural and remote north Queensland. PARTICIPANTS A total of 305 vehicle controllers aged 16 years or over hospitalised at Atherton, Cairns, Mount Isa or Townsville for at least 24 hours as a result of a vehicle crash. MAIN OUTCOME MEASURE A structured questionnaire completed by participants covering crash details, lifestyle and demographic characteristics, driving history, medical history, alcohol and drug use and attitudes to road use. RESULTS Overall, 27.9% of interviewees crashed on private property, with the highest proportion of private road crashes occurring in the North West Statistical Division (45%). Risk factors shown to be associated with private property crashes included male sex, riding off-road motorcycle or all-terrain vehicle, first-time driving at that site, lack of licence for vehicle type, recreational use and not wearing a helmet or seatbelt. CONCLUSIONS Considerable trauma results from vehicle crashes on rural private property. These crashes are not included in most crash data sets, which are limited to public road crashes. Legislation and regulations applicable to private property vehicle use are largely focused on workplace health and safety, yet work-related crashes represent a minority of private property crashes in north Queensland.

Young driver restrictions: Does the evidence support them?

OBJECTIVE To assess the suitability of Queenslands graduated licensing system in the context of rural and remote Queensland. DESIGN Age-based comparison of crash data collected by the Rural and Remote Road Safety Study (RRRSS). SETTING Rural and remote North Queensland. PARTICIPANTS A total of 367 vehicle controllers aged 16 years or over hospitalised at Townsville, Cairns or Mount Isa for at least 24 hours, or killed, as a result of a vehicle crash. MEASUREMENTS Specific RRRSS variables are assessed in relation to Queenslands graduated licensing program, including rates of unlicensed driving/riding, late night crashes, crashes with multiple passengers, contributing factors in crashes and vehicle types involved. RESULTS While people between 16 and 24 years of age comprise 16% of the target population, 25% of crashes meeting RRRSS criteria involved a vehicle controller in that age group. 12.8% of all cases involved an unlicensed driver/rider, within which 66% were below 25 years of age. Young drivers/riders were represented in 50% of crashes occurring between 11:00 p.m. and 5:00 a.m., and 33% of crashes in vehicles with multiple passengers. Motorcyclists represented about 40% of cases in both age groups. There were no significant differences between age groups in vehicle types used, or circumstances that contributed to crashes. CONCLUSIONS The general overrepresentation of young drivers/riders in rural and remote North Queensland supports tailored interventions, such as graduated licensing. However, while some measures in the legislation are well supported, problems surrounding unlicensed driving/riding might be exacerbated.

Impact of Waiting Times on Risky Driver Behaviour at Railway Level Crossings

Increased road and rail traffic in Australia results in actively protected crossings being closed for extended periods of time during peak hours. This results in road congestion. It is known that extended periods of warning/waiting times at level crossings have impacts on drivers’ decision making in regards to violating crossing rules. Excessive waiting times could lead to non-compliant behaviour by motorists, resulting in incidents, including injuries and fatalities. However, the correlation between waiting time and rule violation is not well documented, although it is known that a range of personal and environmental factors influence rule non-compliance. This leads to the question of whether longer waiting times affect motorists’ assessment of risk and how long motorists are prepared to wait at level crossings before undertaking risky behaviour. A driving simulator study was used to obtain objective measures of railway level crossing (RLX) rule violations. Sixty participants completed six driving tasks each, with the tasks varying in terms of waiting times. Compliance with road rules at the level crossing during the simulated drives was examined. Main results include that increased waiting times result in increased likelihood of risky driving behaviour, particularly for waiting times longer than three minutes. Risky driving behaviours included entering the activated crossing before boom gates are down; entering the crossing after the train passage but before signals are deactivated; and stopping/reversing on the crossing. The results suggest that, where possible, waiting times should be standardized at values lower than three minutes in order to reduce the likelihood of risky road user behaviour.

Influence of Remotely Operated Stop–Slow Controls on Driver Behavior in Work Zones

Remotely operated devices for traffic control—portable traffic lights and automated flagger assistance devices—are used to improve flagger safety in a one-lane-each-way work zone with lane closure. Previous research has measured the effectiveness of these devices as driver compliance rates and driver understanding of the devices, but the effects of these devices on driver behavior have not yet been examined comprehensively. Therefore, the influence of remotely operated stop–slow traffic control devices on driver behavior was examined. Video-recorded traffic movements from a rural work zone in the Queensland state of Australia provided driver speeds, deceleration profiles, stopping behavior, and compliance rates for a set of remotely operated devices new to Australia: static red–amber–green lights, static red–amber lights, static red–amber arrow lights, and mechanical stop–slow signs. Pneumatic tube traffic counters were used to collect driver speeds before and after the devices, and an on-road driver survey was conducted to elicit driver understanding of the devices. Results indicated that drivers had difficulty understanding the new devices, particularly the amber light and amber arrow options (which confused drivers about their meaning—to stop or to go). The new remotely operated devices resulted in higher approach speeds, greater variability in approach speeds, and faster deceleration rates than the flagger method. The good compliance rates observed with the remotely operated devices imply that the devices could improve flagger safety by reducing flagger exposure to traffic; however, the negative effects on driver behavior might indicate an increased risk of rear-end crashes in the advance warning area.

863 Understanding the profile of injuries for vulnerable road users: a data linkage study

Background Unless substantial gains are made in the prevention of road crashes, they will become the third ranked global burden of disease and injury by 2020. A growing proportion of this burden relates to vulnerable road users (e.g., motorcyclists, cyclists, and pedestrians). In order to reduce this burden, there is a need to fully understand the nature and contributing circumstances of crashes and the resulting injuries. While police-reported crash data contain detailed information about crash circumstances, they lack accurate information about the severity and nature of injuries which are included in hospital data. By bringing these data collections together using data linkage, the relationship between the characteristics of vulnerable road user crashes and their resulting injuries were explored. Methods Data from the Queensland Road Crash Database (QRCD) and the Queensland Hospital Admitted Patients Data Collection (QHAPDC) for the year 2010 were linked. There were 1,382 police-reported motorcyclist injuries that linked to a hospital record (42% of all police-reported motorcyclist injuries), 429 cyclists (25%), and 644 (39%) pedestrians. The relationships between crash characteristics such as counterpart involvement, alcohol, fatigue, speed, and helmet use and injury severity (e.g., ICD-derived Injury Severity Score ICISS, length of stay), nature, and body region were examined. Results The involvement of motor vehicles was related to more severe injury outcomes for vulnerable road users (between 1.5 and 2.8 times the odds of being serious), as were high speeds (between 1.2 and 1.6 times), alcohol (2.1 times for motorcycles), and helmet non-use (between 1.9 and 2 times). The circumstances of crashes, such as counterpart type and crash location, influenced injury nature and body region. Conclusions By bringing together circumstance data from police and injury outcome data from hospitals, a greater understanding of the link between crash circumstances and injury outcomes was achieved. Studies using linked data can better inform intervention and treatment for a more holistic approach to the reduction of road trauma for vulnerable road users.