Male and female cyclist and driver perceptions of crash risk

Many drivers and non-cyclists perceive cycling as an extremely risky activity with women in particular being concerned about the risk of injury. The low rates of cycling participation by women pose a threat to the achievement of government targets for cycling participation and restrict the potential transport, health and environmental benefits that increased levels of cycling could provide. This study seeks to extend earlier research in gender and cycling by comparing the risks perceived by female and male cyclists and drivers in specific on-road situations while accounting for other potentially gender-related factors such as travel patterns and experience, perceived skill, and risk taking behaviors. In an online survey, 444 regular cyclists and 151 (non-cyclist) car drivers rated the level of risk in six situations: Failing to yield; Going through a red light; Not signaling when turning; Swerving; Tailgating; and Not checking traffic. The study found that the higher levels of risk perceived by women are not completely accounted for by differences in cycling patterns or perceptions of skill. Compared to their male counterparts, female cyclists and car drivers had similarly elevated perceptions of risk suggesting that these gender differences are not specific to cycling, but reflect wider differences in risk perception. Not all of the gender differences were consistent across cyclists and drivers. Higher levels of perceived skill were evident for male cyclists but not for male car drivers. Further research is needed to explore the robustness and interpretation of this finding.

Double star chopper cell converter for battery electric vehicles with inter-module SoC balancing and fault tolerant control

A modularized battery system with Double Star Chopper Cell (DSCC) based modular multilevel converter is proposed for a battery operated electric vehicle (EV). A design concept for the modularized battery micro-packs for DSCC is described. Multidimensional pulse width modulation (MD-PWM) with integrated inter-module SoC balancing and fault tolerant control is proposed and explained. The DSCC can be operated either as an inverter to drive the EV motor or as a synchronous rectifier connected to external three phase power supply equipment for charging the battery micro-packs. The methods of operation as inverter and synchronous rectifier with integrated inter-module SoC balancing and fault tolerant control are discussed. The proposed system operation as inverter and synchronous rectifier are verified through simulations and the results are presented.

Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis

The development of methods for real-time crash prediction as a function of current or recent traffic and roadway conditions is gaining increasing attention in the literature. Numerous studies have modeled the relationships between traffic characteristics and crash occurrence, and significant progress has been made. Given the accumulated evidence on this topic and the lack of an articulate summary of research status, challenges, and opportunities, there is an urgent need to scientifically review these studies and to synthesize the existing state-of-the-art knowledge. This paper addresses this need by undertaking a systematic literature review to identify current knowledge, challenges, and opportunities, and then conducts a meta-analysis of existing studies to provide a summary impact of traffic characteristics on crash occurrence. Sensitivity analyses were conducted to assess quality, publication bias, and outlier bias of the various studies; and the time intervals used to measure traffic characteristics were also considered. As a result of this comprehensive and systematic review, issues in study designs, traffic and crash data, and model development and validation are discussed. Outcomes of this study are intended to provide researchers focused on real-time crash prediction with greater insight into the modeling of this important but extremely challenging safety issue.

Analysis of young driver crash data. Report to the Research Council of Oman, Muscat

Road traffic crashes are an alarming public health issue in Oman, despite ongoing improvements in traffic law enforcement practices and technology. One of the main target groups for road safety in Oman are young drivers aged 17-25 years. This report provides an overview of the characteristics of crashes in Oman involving young drivers (17-25 years) between 1st January 2009 and 31st December 2011. Although, young drivers aged 17-25 years comprise around 17% of all licence holders in Oman, they represented more than one third of all drivers involved in road traffic crashes in the country. A total of 11,101 young drivers (17-25 years) were involved in registered crashes during the study period. From this, 7,727 young drivers (69.6%) were found to be the cause of the crashes...

Crash involvement patterns of unlicensed drivers and riders in Queensland. Report to Queensland Department of Transport and Main Roads

Unlicensed driving remains a serious problem for road safety, despite ongoing improvements in traffic law enforcement practices and technology. While it does not play a direct causative role in road crashes, unlicensed driving undermines the integrity of the driver licensing system and is associated with a range of high-risk behaviours. This report examines official road crash data from Queensland for the years 2003-08 to compare the crash involvement patterns of unlicensed drivers with those of licensed drivers and explore the scope and nature of unlicensed driving. This study replicates and extends upon two previous studies examining the involvement of unlicensed drivers in crashes in Queensland (Watson, 2004a; 2004b; Watson & Steinhardt, 2006).

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.

Heavy vehicle crash characteristics in Oman, 2009-2011

In recent years, Oman has seen a shift in the burden of diseases towards road accidents. The main objective of this paper, therefore, is to describe key characteristics of heavy vehicle crashes in Oman and identify the key driving behaviours that influence fatality risks. Crash data from January 2009 to December 2011 were examined and it was found that of the 22,543 traffic accidents that occurred within this timeframe, 3,114 involved heavy vehicles. While the majority of these crashes were attributed to driver behaviours, a small proportion was attributed to other factors. The results of the study indicate that there is a need for a more thorough crash investigation process in Oman. Future research should explore the reporting processes used by the Royal Oman Police, cultural influences on heavy vehicle operations in Oman, and improvements to the current licensing system.

Fault detection of wind turbine drivetrain utilizing power-speed characteristics

Wind energy, being the fastest growing renewable energy source in the present world, requires a large number of wind turbines to transform wind energy into electricity. One factor driving the cost of this energy is the reliable operation of these turbines. Therefore, it is a growing requirement within the wind farm community, to monitor the operation of the wind turbines on a continuous basis so that a possible fault can be detected ahead of time. As the wind turbine operates in an environment of constantly changing wind speed, it is a challenging task to design a fault detection technique which can accommodate the stochastic operational behavior of the turbines. Addressing this issue, this paper proposes a novel fault detection criterion which is robust against operational uncertainty, as well as having the ability to quantify severity level specifically of the drivetrain abnormality within an operating wind turbine. A benchmark model of wind turbine has been utilized to simulate drivetrain fault condition and effectiveness of the proposed technique has been tested accordingly. From the simulation result it can be concluded that the proposed criterion exhibits consistent performance for drivetrain faults for varying wind speed and has linear relationship with the fault severity level.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

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.

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.

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.

Social influences on risky driving behaviours among young drivers in Oman

Young drivers represent approximately 20% of the Omani population, yet account for over one third of crash injuries and fatalities on Oman's roads. Internationally, research has demonstrated that social influences play an important role within young driver safety, however, there is little research examining this within Arab gulf countries. This study sought to explore young driver behaviour using Akers' social learning theory. A self-report survey was conducted by 1319 (72.9% male and 27.1% female) young drivers aged 17-25 years. A hierarchical regression model was used to investigate the contribution of social learning variables (norms and behaviour of significant others, personal attitudes towards risky behaviour, imitation of significant others, beliefs about the rewards and punishments offered by risky behaviour), socio-demographic characteristics (age and gender), driving experience (initial training, time driving and previous driving without supervision) and sensitivity to rewards and punishments upon the self-reported risky driving behaviours of young drivers. It was found that 39.6% of the young drivers reported that they have been involved in at least one crash since the issuance of their driving licence and they were considered ‘at fault’ in 60.7% of these crashes. The hierarchical multiple regression models revealed that socio-demographic characteristics and driving experience alone explained 14.2% of the variance in risky driving behaviour. By introducing social learning factors into the model a further 37.0% of variance was explained. Finally, 7.9% of the variance in risky behaviour could be explained by including individual sensitivity to rewards and punishments. These findings and the implications are discussed.

Fault-tolerant characteristics and topological properties of a hierarchical network of hypercubes

We analyse the fault-tolerant parameters and topological properties of a hierarchical network of hypercubes. We take a close look at the Extended Hypercube (EH) and the Hyperweave (HW) architectures and also compare them with other popular architectures. These two architectures have low diameter and constant degree of connectivity making it possible to expand these networks without affecting the existing configuration. A scheme for incrementally expanding this network is also presented. We also look at the performance of the ASCEND/DESCEND class of algorithms on these architectures.