Crash involvement patterns of unlicensed drivers: Literature review. Report to Queensland Department of Transport and Main Roads

There is a lack of definitive evidence available relating to the extent and nature of unlicensed driving. Analysis of the crash involvement of unlicensed drivers provides an opportunity to better understand the behaviours of this group. This paper reviews the available literature relating to crash involvement patterns of unlicensed drivers. Key areas discussed include the prevalence of unlicensed driving as indicated by studies of crashes involving this group and associations between unlicensed driving and higher levels of risk-taking on the road. This paper also notes differences found in the characteristics and on-road behaviour of unlicensed drivers and the degree to which these factors, in particular alcohol and drug misuse, may influence crash involvement patterns. Drawing on Australian and international studies, this paper consolidates the available research evidence and identifies gaps in current knowledge relating to crash involvement patterns of unlicensed drivers.

Links between unlicensed and unregistered driving (Data analysis). Report to Queensland Department of Transport and Main Roads

There is a current lack of understanding regarding the use of unregistered vehicles on public roads and road-related areas, and the links between the driving of unregistered vehicles and a range of dangerous driving behaviours. This report documents the findings of data analysis conducted to investigate the links between unlicensed driving and the driving of unregistered vehicles, and is an important initial undertaking into understanding these behaviours. This report examines de-identified data from two sources: crash data; and offence data. The data was extracted from the Queensland Department of Transport and Main Roads (TMR) databases and covered the period from 2003 to 2008.

Analysis and evaluation of use of the V1 and South East Freeway Bicycleway post completion of Stage C of the V1

Executive Summary: Completion of the Veloway 1 (V1) will provide a dedicated and safe route for cyclists between the Brisbane CBD and the Gateway Motorway off-ramp at Eight Mile Plains alongside the South East Motorway. The V1 is being delivered in stages and when completed will provide a dedicated 3m wide cycleway 17km in length. Two stages (D and E) remain to be constructed to complete the V1. Major trip attractors along the V1 include the Mater, Princes Alexandra and Greenslopes Hospitals, two campuses of Griffith University, Garden City shopping centre and the Australian Tax Office. This report assesses the available evidence on the impacts on cycling behaviour of the recently completed V1 Stage C. The data sources informing this review include three intercept surveys, motion activated traffic cameras and travel time surveys on the V1 and adjoining South East Freeway Bikeway (SEFB), Strava app data, and cyclist crash data along Logan Road. The key findings from the evidence are that the completed V1 Stage C has: a Attracted cyclists from Holland Park, Holland Park West, Mt Gravatt and southern parts of Tarragindi onto the V1 Stage C. b Reduced the crash exposure of pedestrians to cyclists by attracting higher speed cyclists off the adjoining SEFB onto the cycling dedicated V1 Stage C. c Reduced the potential crash exposure of cyclists to motor vehicles by attracting cyclists off Logan Road on to the V1. d Provided travel time benefits to cyclists and reduced road crossings (eight down to two). e Predominantly attracted adults commuting alone to and from work and university. The evidence shows that the two traffic crossings across Birdwood Road (required as a temporary measure until the V1 is completed) negate much of the travel time gains of the V1 Stage C compared to the adjoining SEFB for southbound cyclists. Many cyclists accessing the V1 Stage C from the south are cycling in high-volume vehicular traffic lanes to reduce their travel time along Birdwood Road, but in the process are increasing their exposure to crashes with motor vehicles. Based on these findings this report recommends that TMR: a. Continue with plans to complete the V1 Veloway b. Undertake an engineering feasibility assessment to determine the viability of constructing a section of the V1 Stage E from the intersection Weller and Birdwood Roads over Marshall Road and along Bapaume Road on the western side of the Motorway to the intersection of Bapaume and Sterculia Roads. c. In the interim, improve signage and Birdwood Road crossing points for cyclists accessing and egressing the southern end of the V1 Stage C. d. Work with Brisbane City Council to identify the safest and most practical bicycle facilities to facilitate cycle travel between Logan Road and the V1 south of Birdwood Road. e. Improve the awareness of the V1 Stage C through signage for cyclists approaching from the north with the aim of providing a better understanding of the route of the V1 to the south. f. Refine the use of motion activated traffic cameras to improve the capture rate of useable images and obtain an ongoing collection over time of V1 usage data. g. Undertake discussions with Strava, Inc. to refine the presentation of Strava data to improve visual understanding of maps showing before and after cycle route volumes along and on roads leading to the V1.

Estimating the under-reporting of road crash injuries to police using multiple linked data collections

The reliance on police data for the counting of road crash injuries can be problematic, as it is well known that not all road crash injuries are reported to police which under-estimates the overall burden of road crash injuries. The aim of this study was to use multiple linked data sources to estimate the extent of under-reporting of road crash injuries to police in the Australian state of Queensland. Data from the Queensland Road Crash Database (QRCD), the Queensland Hospital Admitted Patients Data Collection (QHAPDC), Emergency Department Information System (EDIS), and the Queensland Injury Surveillance Unit (QISU) for the year 2009 were linked. The completeness of road crash cases reported to police was examined via discordance rates between the police data (QRCD) and the hospital data collections. In addition, the potential bias of this discordance (under-reporting) was assessed based on gender, age, road user group, and regional location. Results showed that the level of under-reporting varied depending on the data set with which the police data was compared. When all hospital data collections are examined together the estimated population of road crash injuries was approximately 28,000, with around two-thirds not linking to any record in the police data. The results also showed that the under-reporting was more likely for motorcyclists, cyclists, males, young people, and injuries occurring in Remote and Inner Regional areas. These results have important implications for road safety research and policy in terms of: prioritising funding and resources; targeting road safety interventions into areas of higher risk; and estimating the burden of road crash injuries.

Motorway crash duration and its determinants: Do durations vary across motorways?

Traffic congestion has been a growing issue in many metropolitan areas during recent years, which necessitates the identification of its key contributors and development of sustainable strategies to help decrease its adverse impacts on traffic networks. Road incidents generally and crashes specifically have been acknowledged as the cause of a large proportion of travel delays in urban areas and account for 25% to 60% of traffic congestion on motorways. Identifying the critical determinants of travel delays has been of significant importance to the incident management systems which constantly collect and store the incident duration data. This study investigates the individual and simultaneous differential effects of the relevant determinants on motorway crash duration probabilities. In particular, it applies parametric Accelerated Failure Time (AFT) hazard-based models to develop in-depth insights into how the crash-specific characteristic and the associated temporal and infrastructural determinants impact the duration. AFT models with both fixed and random parameters have been calibrated on one year of traffic crash records from two major Australian motorways in South East Queensland and the differential effects of determinants on crash survival functions have been studied on these two motorways individually. A comprehensive spectrum of commonly used parametric fixed parameter AFT models, including generalized gamma and generalized F families, have been compared to random parameter AFT structures in terms of goodness of fit to the duration data and as a result, the random parameter Weibull AFT model has been selected as the most appropriate model. Significant determinants of motorway crash duration included traffic diversion requirement, crash injury type, number and type of vehicles involved in a crash, day of week and time of day, towing support requirement and damage to the infrastructure. A major finding of this research is that the motorways under study are significantly different in terms of crash durations; such that motorway exhibits durations that are on average 19% shorter compared to the durations on motorway. The differential effects of explanatory variables on crash durations are also different on the two motorways. The detailed presented analysis confirms that, looking at the motorway network as a whole, neglecting the individual differences between roads, can lead to erroneous interpretations of duration and inefficient strategies for mitigating travel delays along a particular motorway.

Do motor vehicle crashes arise from single or multiple unique risk processes? An inquiry into crash causes and modelling

Crashes at any particular transport network location consist of a chain of events arising from a multitude of potential causes and/or contributing factors whose nature is likely to reflect geometric characteristics of the road, spatial effects of the surrounding environment, and human behavioural factors. It is postulated that these potential contributing factors do not arise from the same underlying risk process, and thus should be explicitly modelled and understood. The state of the practice in road safety network management applies a safety performance function that represents a single risk process to explain crash variability across network sites. This study aims to elucidate the importance of differentiating among various underlying risk processes contributing to the observed crash count at any particular network location. To demonstrate the principle of this theoretical and corresponding methodological approach, the study explores engineering (e.g. segment length, speed limit) and unobserved spatial factors (e.g. climatic factors, presence of schools) as two explicit sources of crash contributing factors. A Bayesian Latent Class (BLC) analysis is used to explore these two sources and to incorporate prior information about their contribution to crash occurrence. The methodology is applied to the state controlled roads in Queensland, Australia and the results are compared with the traditional Negative Binomial (NB) model. A comparison of goodness of fit measures indicates that the model with a double risk process outperforms the single risk process NB model, and thus indicating the need for further research to capture all the three crash generation processes into the SPFs.

Crash risk perception of sleepy driving and its comparisons with drink driving and speeding: Which behavior is perceived as the riskiest?

- Objective Driver sleepiness is a major crash risk factor, but may be under-recognized as a risky driving behavior. Sleepy driving is usually rated as less of a road safety issue than more well-known risky driving behaviors, such as drink driving and speeding. The objective of this study was to compare perception of crash risk of sleepy driving, drink driving, and speeding. - Methods In total, 300 Australian drivers completed a questionnaire that assessed crash risk perceptions for sleepy driving, drink driving, and speeding. Additionally, the participants perception of crash risk was assessed for five different contextual scenarios that included different levels of sleepiness (low, high), driving duration (short, long), and time of day/circadian influences (afternoon, night-time) of driving. - Results The analysis confirmed that sleepy driving was considered a risky driving behavior, but not as risky as high levels of speeding (p < .05). Yet, the risk of crashing at 4 am was considered as equally risky as low levels of speeding (10 km over the limit). The comparisons of the contextual scenarios revealed driving scenarios that would arguably be perceived as quite risky due to time of day/circadian influences were not reported as high risk. - Conclusions The results suggest a lack of awareness or appreciation of circadian rhythm functioning, particularly the descending phase of circadian rhythm that promotes increased sleepiness in the afternoon and during the early hours of the morning. Yet, the results suggested an appreciation of the danger associated with long distance driving and driver sleepiness. Further efforts are required to improve the community’s awareness of the impairing effects from sleepiness and in particular, knowledge regarding the human circadian rhythm and the increased sleep propensity during the circadian nadir.

Sleep-related crash characteristics: Implications for applying a fatigue definition to crash reports

Sleep-related (SR) crashes are an endemic problem the world over. However, police officers report difficulties in identifying sleepiness as a crash contributing factor. One approach to improving the sensitivity of SR crash identification is by applying a proxy definition post hoc to crash reports. To identify the prominent characteristics of SR crashes and highlight the influence of proxy definitions, ten years of Queensland (Australia) police reports of crashes occurring in ≥100 km/h speed zones were analysed. In Queensland, two approaches are routinely taken to identifying SR crashes. First, attending police officers identify crash causal factors; one possible option is ‘fatigue/fell asleep’. Second, a proxy definition is applied to all crash reports. Those meeting the definition are considered SR and added to the police-reported SR crashes. Of the 65,204 vehicle operators involved in crashes 3449 were police-reported as SR. Analyses of these data found that male drivers aged 16–24 years within the first two years of unsupervised driving were most likely to have a SR crash. Collision with a stationary object was more likely in SR than in not-SR crashes. Using the proxy definition 9739 (14.9%) crashes were classified as SR. Using the proxy definition removes the findings that SR crashes are more likely to involve males and be of high severity. Additionally, proxy defined SR crashes are no less likely at intersections than not-SR crashes. When interpreting crash data it is important to understand the implications of SR identification because strategies aimed at reducing the road toll are informed by such data. Without the correct interpretation, funding could be misdirected. Improving sleepiness identification should be a priority in terms of both improvement to police and proxy reporting.

A novel approach to assessing road-curve crash severity

Curves are a common feature of road infrastructure; however crashes on road curves are associated with increased risk of injury and fatality to vehicle occupants. Countermeasures require the identification of contributing factors. However, current approaches to identifying contributors use traditional statistical methods and have not used self-reported narrative claim to identify factors related to the driver, vehicle and environment in a systemic way. Text mining of 3434 road-curve crash claim records filed between 1 January 2003 and 31 December 2005 at a major insurer in Queensland, Australia, was undertaken to identify risk levels and contributing factors. Rough set analysis was used on insurance claim narratives to identify significant contributing factors to crashes and their associated severity. New contributing factors unique to curve crashes were identified (e.g., tree, phone, over-steer) in addition to those previously identified via traditional statistical analysis of Police and licensing authority records. Text mining is a novel methodology to improve knowledge related to risk and contributing factors to road-curve crash severity. Future road-curve crash countermeasures should more fully consider the interrelationships between environment, the road, the driver and the vehicle, and education campaigns in particular could highlight the increased risk of crash on road-curves.

Predicting crashes using traffic offences. A meta-analysis that examines potential bias between self-report and archival data

Background Traffic offences have been considered an important predictor of crash involvement, and have often been used as a proxy safety variable for crashes. However the association between crashes and offences has never been meta-analysed and the population effect size never established. Research is yet to determine the extent to which this relationship may be spuriously inflated through systematic measurement error, with obvious implications for researchers endeavouring to accurately identify salient factors predictive of crashes. Methodology and Principal Findings Studies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. Potential impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were considered and discussed. After weighting for sample size, an average correlation of r = .18 was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. Consistent with common method variance effects, a within country analysis found stronger effect sizes in self-reported data even controlling for crash mean. Significance The effectiveness of traffic offences as a proxy for crashes may be limited. Inclusion of elements such as independently validated crash and offence histories or accurate measures of exposure to the road would facilitate a better understanding of the factors that influence crash involvement.

Scientism and instrumentalism After the Bomb: Dr Strangelove, End Zone, Crash and The Wire

This thesis argues that through the prism of America’s Cold War, scientism has emerged as the metanarrative of the postnuclear age. The advent of the bomb brought about a new primacy for mechanical and hyperrational thinking in the corridors of power not just in terms of managing the bomb itself but diffusing this ideology throughout the culture in social sciences, economics and other such institutional systems. The human need to mitigate or ameliorate against the chaos of the universe lies at the heart of not just religious faith but in the desire for perfect control. Thus there has been a transference of power from religious faith to the apparent material power of science and technology and the terra firma these supposedly objective means supply. The Cold War, however was a highly ideologically charged opposition between the two superpowers, and the scientific methodology that sprang forth to manage the Cold War and the bomb, in the United States, was not an objective scientific system divorced from the paranoia and dogma but a system that assumed a radically fundamentalist idea of capitalism. This is apparent in the widespread diffusion of game theory throughout Western postindustrial institutions. The inquiry of the thesis thus examines the texts that engage and criticise American Cold War methodology, beginning with the nuclear moment, so to speak, and Dr Strangelove’s incisive satire of moral abdication to machine processes. Moving on chronologically, the thesis examines the diffusion of particular kinds of masculinity and sexuality in postnuclear culture in Crash and End Zone and finishing up its analysis with the ethnographic portrayal of a modern American city in The Wire. More than anything else, the thesis wishes to reveal to what extent this technocratic consciousness puts pressure on language and on binding narratives.

A computational study of the characteristics of aircraft post-crash fires

Full-scale furnished cabin fires have been studied experimentally for the purpose of characterising the post-crash cabin fire environment by the US Federal Aviation Administration for many years. In this paper the Computational Fluid Dynamics fire field model SMARTFIRE is used to simulate one of these fires conducted in the C-133 test facility in order to provide further validation of the computational approach and the SMARTFIRE software. The experiment involves exposing the interior cabin materials to an external fuel fire, opening only one exit at the far end of the cabin (the same side as the rupture) for ventilation, and noting the subsequent spread of the external fire to the cabin interior and the onset of flashover at approximately 210 seconds. Through this analysis, the software is shown to be in good agreement with the experimental data, producing reasonable agreement with the fire dynamics prior to flashover and producing a reasonable prediction of the flashover time i.e. 225 seconds. The paper then proceeds to utilize the model to examine the impact on flashover time of the extent of cabin furnishings and cabin ventilation provided by available exits

Predicting the likely impact of aircraft post crash fire on aircraft evacuation using fire and evacuation simulation

The SMARTFIRE Computational Fluid Dynamics (CFD) fire field model has successfully reproduced the observed characteristics including measured temperatures, species concentrations and time to flashover for a post-crash fire experiment conducted by the FAA within their C-133 cabin test facility. In this test only one exit was open in order to provide ventilation for the developing cabin fire. In real post-crash fires, many exits are likely to be open as passangers attempt to evacuate. In this paper, the likely impacts on evacuation of a post-crash fire in which various exiting combinations are available are investigated. The fire scenario, investigated using the SMARTFIRE software, is based on the C-133 experiment but with a fully furnished cabin and with four different exit availability options. The fire data is imported into the airEXODUS evacuation simulation software and the resulting evacuations examined. The combined fire and evacuation analysis reveals that even though the aircraft configuration is predicted to comfortably satisfy the evacuation certification requirement, when fire is included, a number of casualties result, even from the certification compliant exit configuration.

Comparison of crash dynamics of articulated and non-articulated trains

This paper presents a comparison of impact dynamic performance between articulated trains and non-articulated trains. This is carried out by investigation of the characteristics of the two trains types and analysis of their effects on impact dynamics. The analysis shows that the differences in bogie support positions on the carbody and coupling devices lead to differences in several structural and compositional characteristics. These characteristics result in different impact responses for the two types of train and are directly related to their impact stablity. Articulated trains have stiff connection and integral performance in collisions but with less capability for absorbing impact energy between carriages, whereas non-articulated trains show loose connection and scattered performance in collisions but with more options for energy absorber installation between carriages.

Pattern Recognition Approach for Road Collision Hotspots Analysis: Case Study of Northern Ireland

In order to address road safety effectively, it is essential to understand all the factors, which
attribute to the occurrence of a road collision. This is achieved through road safety
assessment measures, which are primarily based on historical crash data. Recent advances
in uncertain reasoning technology have led to the development of robust machine learning
techniques, which are suitable for investigating road traffic collision data. These techniques
include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis).
This study extends upon previous research work, carried out in Coll et al. [3], which
proposed a non-linear aggregation framework for identifying temporal and spatial hotspots.
The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety,
in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any
hidden patterns associated with collisions that occurred in Lisburn area, which in turn, will
provide more clarity in the causation factors so that appropriate countermeasures can be put
in place.