Differences in the performance of safety performance functions estimated for total crash count and for crash count by crash type

In recent years the development and use of crash prediction models for roadway safety analyses have received substantial attention. These models, also known as safety performance functions (SPFs), relate the expected crash frequency of roadway elements (intersections, road segments, on-ramps) to traffic volumes and other geometric and operational characteristics. A commonly practiced approach for applying intersection SPFs is to assume that crash types occur in fixed proportions (e.g., rear-end crashes make up 20% of crashes, angle crashes 35%, and so forth) and then apply these fixed proportions to crash totals to estimate crash frequencies by type. As demonstrated in this paper, such a practice makes questionable assumptions and results in considerable error in estimating crash proportions. Through the use of rudimentary SPFs based solely on the annual average daily traffic (AADT) of major and minor roads, the homogeneity-in-proportions assumption is shown not to hold across AADT, because crash proportions vary as a function of both major and minor road AADT. For example, with minor road AADT of 400 vehicles per day, the proportion of intersecting-direction crashes decreases from about 50% with 2,000 major road AADT to about 15% with 82,000 AADT. Same-direction crashes increase from about 15% to 55% for the same comparison. The homogeneity-in-proportions assumption should be abandoned, and crash type models should be used to predict crash frequency by crash type. SPFs that use additional geometric variables would only exacerbate the problem quantified here. Comparison of models for different crash types using additional geometric variables remains the subject of future research.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Identifying large truck hot spots using crash counts and PDOEs

Large trucks are involved in a disproportionately small fraction of the total crashes but a disproportionately large fraction of fatal crashes. Large truck crashes often result in significant congestion due to their large physical dimensions and from difficulties in clearing crash scenes. Consequently, preventing large truck crashes is critical to improving highway safety and operations. This study identifies high risk sites (hot spots) for large truck crashes in Arizona and examines potential risk factors related to the design and operation of the high risk sites. High risk sites were identified using both state of the practice methods (accident reduction potential using negative binomial regression with long crash histories) and a newly proposed method using Property Damage Only Equivalents (PDOE). The hot spots identified via the count model generally exhibited low fatalities and major injuries but large minor injuries and PDOs, while the opposite trend was observed using the PDOE methodology. The hot spots based on the count model exhibited large AADTs, whereas those based on the PDOE showed relatively small AADTs but large fractions of trucks and high posted speed limits. Documented site investigations of hot spots revealed numerous potential risk factors, including weaving activities near freeway junctions and ramps, absence of acceleration lanes near on-ramps, small shoulders to accommodate large trucks, narrow lane widths, inadequate signage, and poor lighting conditions within a tunnel.

Modeling crash types: New insights into the effects of covariates on crashes at rural intersections

Many studies focused on the development of crash prediction models have resulted in aggregate crash prediction models to quantify the safety effects of geometric, traffic, and environmental factors on the expected number of total, fatal, injury, and/or property damage crashes at specific locations. Crash prediction models focused on predicting different crash types, however, have rarely been developed. Crash type models are useful for at least three reasons. The first is motivated by the need to identify sites that are high risk with respect to specific crash types but that may not be revealed through crash totals. Second, countermeasures are likely to affect only a subset of all crashes—usually called target crashes—and so examination of crash types will lead to improved ability to identify effective countermeasures. Finally, there is a priori reason to believe that different crash types (e.g., rear-end, angle, etc.) are associated with road geometry, the environment, and traffic variables in different ways and as a result justify the estimation of individual predictive models. The objectives of this paper are to (1) demonstrate that different crash types are associated to predictor variables in different ways (as theorized) and (2) show that estimation of crash type models may lead to greater insights regarding crash occurrence and countermeasure effectiveness. This paper first describes the estimation results of crash prediction models for angle, head-on, rear-end, sideswipe (same direction and opposite direction), and pedestrian-involved crash types. Serving as a basis for comparison, a crash prediction model is estimated for total crashes. Based on 837 motor vehicle crashes collected on two-lane rural intersections in the state of Georgia, six prediction models are estimated resulting in two Poisson (P) models and four NB (NB) models. The analysis reveals that factors such as the annual average daily traffic, the presence of turning lanes, and the number of driveways have a positive association with each type of crash, whereas median widths and the presence of lighting are negatively associated. For the best fitting models covariates are related to crash types in different ways, suggesting that crash types are associated with different precrash conditions and that modeling total crash frequency may not be helpful for identifying specific countermeasures.

Visual assessment of pedestrian crashes

Of the numerous factors that play a role in fatal pedestrian collisions, the time of day, day of the week, and time of year can be significant determinants. More than 60% of all pedestrian collisions in 2007 occurred at night, despite the presumed decrease in both pedestrian and automobile exposure during the night. Although this trend is partially explained by factors such as fatigue and alcohol consumption, prior analysis of the Fatality Analysis Reporting System database suggests that pedestrian fatalities increase as light decreases after controlling for other factors. This study applies graphical cross-tabulation, a novel visual assessment approach, to explore the relationships among collision variables. The results reveal that twilight and the first hour of darkness typically observe the greatest frequency of pedestrian fatal collisions. These hours are not necessarily the most risky on a per mile travelled basis, however, because pedestrian volumes are often still high. Additional analysis is needed to quantify the extent to which pedestrian exposure (walking/crossing activity) in these time periods plays a role in pedestrian crash involvement. Weekly patterns of pedestrian fatal collisions vary by time of year due to the seasonal changes in sunset time. In December, collisions are concentrated around twilight and the first hour of darkness throughout the week while, in June, collisions are most heavily concentrated around twilight and the first hours of darkness on Friday and Saturday. Friday and Saturday nights in June may be the most dangerous times for pedestrians. Knowing when pedestrian risk is highest is critically important for formulating effective mitigation strategies and for efficiently investing safety funds. This applied visual approach is a helpful tool for researchers intending to communicate with policy-makers and to identify relationships that can then be tested with more sophisticated statistical tools.

Effect of clear zone widths on run-off-road crash outcomes

Current guidelines on clear zone selection and roadside hazard management adopt the US approach based on the likelihood of roadside encroachment by drivers. This approach is based on the available research conducted in the 1960s and 70s. Over time, questions have been raised regarding the robustness and applicability of this research in Australasia in 2010 and in the Safe System context. This paper presents a review of the fundamental research relating to selection of clear zones. Results of extensive rural highway statistical data modelling suggest that a significant proportion of run-off-road to the left casualty crashes occurs in clear zones exceeding 13 m. They also show that the risk of run-off-road to the left casualty crashes was 21% lower where clear zones exceeded 8 m when compared with clear zones in the 4 – 8 m range. The paper discusses a possible approach to selection of clear zones based on managing crash outcomes, rather than on the likelihood of roadside encroachment which is the basis for the current practice. It is expected that this approach would encourage selection of clear zones wider than 8 m when the combination of other road features suggests higher than average casualty crash risk.

Crash risk estimation and assessment tool

Currently in Australia, there are no decision support tools for traffic and transport engineers to assess the crash risk potential of proposed road projects at design level. A selection of equivalent tools already exists for traffic performance assessment, e.g. aaSIDRA or VISSIM. The Urban Crash Risk Assessment Tool (UCRAT) was developed for VicRoads by ARRB Group to promote methodical identification of future crash risks arising from proposed road infrastructure, where safety cannot be evaluated based on past crash history. The tool will assist practitioners with key design decisions to arrive at the safest and the most cost -optimal design options. This paper details the development and application of UCRAT software. This professional tool may be used to calculate an expected mean number of casualty crashes for an intersection, a road link or defined road network consisting of a number of such elements. The mean number of crashes provides a measure of risk associated with the proposed functional design and allows evaluation of alternative options. The tool is based on historical data for existing road infrastructure in metropolitan Melbourne and takes into account the influence of key design features, traffic volumes, road function and the speed environment. Crash prediction modelling and risk assessment approaches were combined to develop its unique algorithms. The tool has application in such projects as road access proposals associated with land use developments, public transport integration projects and new road corridor upgrade proposals.

Assetti proprietari e meccanismi di governance nelle imprese familiari [Finacial reporting in small family firms : searching for a new framework]

SOMMARIO: 1. La “governance” nelle aziende familiari: rilevanza, aspetti distintivi e criticità. 2. Il ruolo della compagine proprietaria nella definizione dei meccanismi di governo. 3. Composizione e funzioni del consiglio d’amministrazione. 4. I patti di famiglia come strumento di disciplina dei rapporti impresa-famiglia. 5. Considerazioni conclusive: prospettive di analisi e scenari futuri negli studi sulla governance delle imprese familiari.

The crash involvement of older drivers is associated with their hazard perception latencies

Hazard perception in driving is the one of the few driving-specific skills associated with crash involvement. However, this relationship has only been examined in studies where the majority of individuals were younger than 65. We present the first data revealing an association between hazard perception and self-reported crash involvement in drivers aged 65 and over. In a sample of 271 drivers, we found that individuals whose mean response time to traffic hazards was slower than 6.68 seconds (the ROC-curve derived pass mark for the test) were 2.32 times (95% CI 1.46, 3.22) more likely to have been involved in a self-reported crash within the previous five years than those with faster response times. This likelihood ratio became 2.37 (95% CI 1.49, 3.28) when driving exposure was controlled for. As a comparison, individuals who failed a test of useful field of view were 2.70 (95% CI 1.44, 4.44) times more likely to crash than those who passed. The hazard perception test and the useful field of view measure accounted for separate variance in crash involvement. These findings indicate that hazard perception testing and training could be potentially useful for road safety interventions for this age group.

V2V/V2I augmented maps : state-of-the-art and contribution to real-time crash risk assessment

A number of advanced driver assistance systems (ADAS) are currently being released on the market, providing safety functions to the drivers such as collision avoidance, adaptive cruise control or enhanced night-vision. These systems however are inherently limited by their sensory range: they cannot gather information from outside this range, also called their “perceptive horizon”. Cooperative systems are a developing research avenue that aims at providing extended safety and comfort functionalities by introducing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communications to the road actors. This paper presents the problematic of cooperative systems, their advantages and contributions to road safety and exposes some limitations related to market penetration, sensors accuracy and communications scalability. It explains the issues of how to implement extended perception, a central contribution of cooperative systems. The initial steps of an evaluation of data fusion architectures for extended perception are exposed.

The Heavy Vehicle Study : a case-control study investigating risk factors for crash in long distance heavy vehicle drivers in Australia

Background Heavy vehicle transportation continues to grow internationally; yet crash rates are high, and the risk of injury and death extends to all road users. The work environment for the heavy vehicle driver poses many challenges; conditions such as scheduling and payment are proposed risk factors for crash, yet the precise measure of these needs quantifying. Other risk factors such as sleep disorders including obstructive sleep apnoea have been shown to increase crash risk in motor vehicle drivers however the risk of heavy vehicle crash from this and related health conditions needs detailed investigation. Methods and Design The proposed case control study will recruit 1034 long distance heavy vehicle drivers: 517 who have crashed and 517 who have not. All participants will be interviewed at length, regarding their driving and crash history, typical workloads, scheduling and payment, trip history over several days, sleep patterns, health, and substance use. All participants will have administered a nasal flow monitor for the detection of obstructive sleep apnoea. Discussion Significant attention has been paid to the enforcement of legislation aiming to deter problems such as excess loading, speeding and substance use; however, there is inconclusive evidence as to the direction and strength of associations of many other postulated risk factors for heavy vehicle crashes. The influence of factors such as remuneration and scheduling on crash risk is unclear; so too the association between sleep apnoea and the risk of heavy vehicle driver crash. Contributory factors such as sleep quality and quantity, body mass and health status will be investigated. Quantifying the measure of effect of these factors on the heavy vehicle driver will inform policy development that aims toward safer driving practices and reduction in heavy vehicle crash; protecting the lives of many on the road network.

The prevalence and characteristics of self-reported dangerous driving behaviours among a young cohort

Background: Young motorists engaging in anti-social and often dangerous driving manoeuvres (which is often referred to as “hooning” within Australia) is an increasing road safety problem. While anecdotal evidence suggests that such behaviour is positively linked with crash involvement, researchers have yet to examine whether younger drivers who deliberately break road rules and drive in an erratic manner (usually with peers) are in fact over represented in crash statistics. This paper outlines research that aimed to identify the characteristics of individuals most likely to engaging in hooning behaviours, as well as examine the frequency of such driving behaviours and if such activity is linked with self-reported crash involvement.---------- Methods: A total of 717 young drivers in Queensland voluntarily completed a questionnaire to investigate their driving behaviour and crash history.---------- Results: Quantitative analysis of the data revealed that almost half the sample reported engaging in some form of “hooning” behaviour at least once in their lifetime, although only 4% indicated heavy participation in the behaviour e.g., >50 times. Street racing was the most common activity reported by participants followed by “drifting” and then “burnouts”. Logistic regression analysis indicated that being younger and a male was predictive of reporting such anti-social driving behaviours, and importantly, a trend was identified between such behaviour and self-reported crash involvement.---------- Conclusions: This research provides preliminary evidence that younger male drivers are more likely to engage in dangerous driving behaviours, which ultimately may prove to increase their overall risk of becoming involved in a crash. This paper will further outline the study findings in regards to current enforcement efforts to deter such driving activity as well as provide direction for future research efforts in this area.---------- Research highlights: ► The self-reported driving behaviours of 717 younger Queensland drivers were examined to investigate the relationship between deliberately breaking road rules and self-reported crash involvement. ► Younger male drivers were most likely to engage in such aberrant driving behaviours and a trend was identified between such behaviour and self-reported crash involvement.

Measuring falls events in acute hospitals-A comparison of three reporting methods to identify missing data in the hospital reporting system

OBJECTIVES: To compare three different methods of falls reporting and examine the characteristics of the data missing from the hospital incident reporting system. DESIGN: Fourteen-month prospective observational study nested within a randomized controlled trial. SETTING: Rehabilitation, stroke, medical, surgical, and orthopedic wards in Perth and Brisbane, Australia. PARTICIPANTS: Fallers (n5153) who were part of a larger trial (1,206 participants, mean age 75.1 � 11.0). MEASUREMENTS: Three falls events reporting measures: participants’ self-report of fall events, fall events reported in participants’ case notes, and falls events reported through the hospital reporting systems. RESULTS: The three reporting systems identified 245 falls events in total. Participants’ case notes captured 226 (92.2%) falls events, hospital incident reporting systems captured 185 (75.5%) falls events, and participant selfreport captured 147 (60.2%) falls events. Falls events were significantly less likely to be recorded in hospital reporting systems when a participant sustained a subsequent fall, (P5.01) or when the fall occurred in the morning shift (P5.01) or afternoon shift (P5.01). CONCLUSION: Falls data missing from hospital incident report systems are not missing completely at random and therefore will introduce bias in some analyses if the factor investigated is related to whether the data ismissing.Multimodal approaches to collecting falls data are preferable to relying on a single source alone.

It really is black and white when you look at it like this : reporting on a study into Australian professors' research and teaching priorities

Despite optimistic claims about the research-teaching nexus, Australian academics still face tension between research and teaching. The teaching and research priorities, beliefs and behaviours of 70 Professorial and Associate Professorial academics in Science, Information Technology and Engineering were examined in this study. The academics from 4 faculties in 3 Australian universities, were asked to rank 16 research activities and 16 matched learning and teaching (L&T) activities from each of three perspectives: job satisfaction, leadership behaviour, and perceptions of professional importance. The findings, which were remarkably consistent across the three universities, were unequivocally in favour of Research. The only L&T activity that was ranked consistently well was “Improving student satisfaction ratings for Teaching”. The data demonstrates that Australian government and university initiatives to raise the status of L&T activity are not impacting significantly on Australia’s future leaders of university learning.

The impact of changes to the graduated driver licensing program in Queensland, Australia on the experiences of learner drivers

Purpose: Graduated driver licensing (GDL) has been introduced in numerous jurisdictions in Australia and internationally in an attempt to ameliorate the significantly greater risk of death and injury for young novice drivers arising from road crashes. The GDL program in Queensland, Australia, was extensively modified in July 2007. This paper reports the driving and licensing experiences of Learner drivers progressing through the current-GDL program, and compares them to the experiences of Learners who progressed through the former-GDL program. ----- ----- Method: Young drivers (n = 1032, 609 females, 423 males) aged 17 to 19 years (M = 17.43, SD = 0.67) were recruited as they progressed from a Learner to a Provisional driver’s licence. They completed a survey exploring their sociodemographic characteristics, driving and licensing experiences as a Learner. Key measures for a subsample (n = 183) of the current-GDL drivers were compared with the former-GDL drivers (n = 149) via t-tests and chi-square analyses. ----- ----- Results: As expected, Learner drivers progressing through the current-GDL program gained significantly more driving practice than those in the former program, which was more likely to be provided by mothers than in the past. Female learners in the current-GDL program reported less difficulty obtaining supervision than those in the former program. The number of attempts needed to pass the practical driving assessment did not change, nor did the amount of professional supervision. The current-GDL Learners held their licence for a significantly longer duration than those in the former program, with the majority reporting that their Logbook entries were accurate on the whole. Compared to those in the former program, a significantly smaller proportion of male current-GDL Learners reported being detected for a driving offence while the females reported significantly lower crash involvement. Most current-GDL drivers reported undertaking their supervised practice at the end of the Learner period. ----- ----- Conclusions: The enhancements to the GDL program in Queensland appear to have achieved many of their intended results. The current-GDL learners participating in the study reported obtaining a significantly greater amount of supervised driving experience compared to former-GDL learners. Encouragingly, the current-GDL Learners did not report any greater difficulty in obtaining supervised driving practice, and there was a decline in the proportion of current-GDL Learners engaging in unsupervised driving. In addition, the majority of Learners do not appear to be attempting to subvert logbook recording requirements, as evidenced by high rates of self-reported logbook accuracy. The results have implications for the development and the evaluation of GDL programs in Australia and around the world.