Safety culture and speeding in the Australian heavy vehicle industry

Inappropriate speed and speeding are among the highest causes of crashes in the heavy vehicle industry. Truck drivers are subjected to a broad range of influences on their behaviour including industrial pressures, company monitoring and police enforcement. Further, drivers have a high level of autonomy over their own behaviour. As such it is important to understand how these external influences interact with commonly shared beliefs, attitudes and values of heavy vehicle drivers to influence their behaviour. The present study uses a re-conceptualisation of safety culture to explore the behaviours of driving at an inappropriate speed and speeding in the heavy vehicle industry. A series of case studies, consisting of interviews and ride-along observations, were conducted with three transport organisations to explore the effect of culture on safety in the heavy vehicle industry. Results relevant to inappropriate speed are reported and discussed. It was found that organisational management through monitoring, enforcement and payment, police enforcement, customer standards and vehicle design factors could all reduce the likelihood of driving at inappropriate speeds under some circumstances. However, due to weaknesses in the ability to accurately monitor appropriate speed, this behaviour was primarily influenced by cultural beliefs, attitudes and values. Truck drivers had a tendency to view speeding as relatively safe, had a desire to speed to save time and increase personal income, and thus often attempted to speed without detection. When drivers saw speeding as dangerous, however, they were more likely to drive safely. Implications for intervention are discussed.

Characteristics of road factors in multi and single vehicle motorcycle crashes in Queensland

Motorcyclists were involved in 6.4% of all police-reported crashes and 12.5% of all fatal crashes in Queensland during 2004-2011. Of these crashes, 43% were single-vehicle (SV) and 57% were multi-vehicle (MV). The overall reduction in motorcycle crashes in this period masked different trends: single-vehicle crashes increased while MV motorcycle crashes decreased. However, little research has been undertaken to understand the similarities and differences between SV and MV motorcycle crashes in Queensland and the factors underlying these diverging trends. The descriptive analyses and regression model developed here confirm international research findings regarding the greater role of road infrastructure factors in SV crashes. In particular, road geometric factors such as horizontal and vertical alignment and road surface factors such as sealed/unsealed and wet/dry were more important in SV than MV crashes.

Energy-based motion control of a slender hull unmanned underwater vehicle

This paper presents a motion control system for tracking of attitude and speed of an underactuated slender-hull unmanned underwater vehicle. The feedback control strategy is developed using the Port-Hamiltonian theory. By shaping of the target dynamics (desired dynamic response in closed loop) with particular attention to the target mass matrix, the influence of the unactuated dynamics on the controlled system is suppressed. This results in achievable dynamics independent of stable uncontrolled states. Throughout the design, the insight of the physical phenomena involved is used to propose the desired target dynamics. Integral action is added to the system for robustness and to reject steady disturbances. This is achieved via a change of coordinates that result in input-to-state stable (ISS) target dynamics. As a final step in the design, an anti-windup scheme is implemented to account for limited actuator capacity, namely saturation. The performance of the design is demonstrated through simulation with a high-fidelity model.

Validation of a 4DOF manoeuvring model of a high-speed vehicle-passenger trimaran

This paper presents the validation of a manoeuvring model for a novel 127m-vehicle-passenger trimaran via full scale trials. The adopted structure of the model is based on a model previously proposed in the literature with some simplifications. The structure of the model is discussed. Then initial parameter estimates are computed, and the final set of parameters are obtained via adjustments based on engineering judgement and application of a genetic algorithm so as to match the data of the trials. The validity of the model is also assessed with data from a trial different from the one use for the parameter adjustment. The model shows good agreement with the trial data.

Proposed vehicle impact speed: Severe injury probability relationships for selected crash types

Speed is recognised as a key contributor to crash likelihood and severity, and to road safety performance in general. Its fundamental role has been recognised by making Safe Speeds one of the four pillars of the Safe System. In this context, impact speeds above which humans are likely to sustain fatal injuries have been accepted as a reference in many Safe System infrastructure policy and planning discussions. To date, there have been no proposed relationships for impact speeds above which humans are likely to sustain fatal or serious (severe) injury, a more relevant Safe System measure. A research project on Safe System intersection design required a critical review of published literature on the relationship between impact speed and probability of injury. This has led to a number of questions being raised about the origins, accuracy and appropriateness of the currently accepted impact speed–fatality probability relationships (Wramborg 2005) in many policy documents. The literature review identified alternative, more recent and more precise relationships derived from the US crash reconstruction databases (NASS/CDS). The paper proposes for discussion a set of alternative relationships between vehicle impact speed and probability of MAIS3+ (fatal and serious) injury for selected common crash types. Proposed Safe System critical impact speed values are also proposed for use in road infrastructure assessment. The paper presents the methodology and assumptions used in developing these relationships. It identifies further research needed to confirm and refine these relationships. Such relationships would form valuable inputs into future road safety policies in Australia and New Zealand.

Design and Development of an In-Vehicle Human Machine Interface to Improve Fuel Efficiency & Safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behaviour change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, this research seeks to use human factors related theories and practices to inform the design and evaluation of an in-vehicle Human Machine Interface (HMI) providing real-time driver feedback with the aim of improving both fuel efficiency and safety.

A Bayesian latent class safety performance function for identifying motor vehicle crash blackspots

The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.

Co2 emissions and income trajectory in Australia: The role of technological change

This study investigates the relationship between per capita carbon dioxide (CO2) emissions and per capita GDP in Australia, while controlling for technological state as measured by multifactor productivity and export of black coal. Although technological progress seems to play a critical role in achieving long term goals of CO2 reduction and economic growth, empirical studies have often considered time trend to proxy technological change. However, as discoveries and diffusion of new technologies may not progress smoothly with time, the assumption of a deterministic technological progress may be incorrect in the long run. The use of multifactor productivity as a measure of technological state, therefore, overcomes the limitations and provides practical policy directions. This study uses recently developed bound-testing approach, which is complemented by Johansen- Juselius maximum likelihood approach and a reasonably large sample size to investigate the cointegration relationship. Both of the techniques suggest that cointegration relationship exists among the variables. The long-run and short-run coefficients of CO2 emissions function is estimated using ARDL approach. The empirical findings in the study show evidence of the existence of Environmental Kuznets Curve type relationship for per capita CO2 emissions in the Australian context. The technology as measured by the multifactor productivity, however, is not found as an influencing variable in emissionsincome trajectory.

Aircraft trajectory clustering techniques using circular statistics

This paper presents a statistical aircraft trajectory clustering approach aimed at discriminating between typical manned and expected unmanned traffic patterns. First, a resampled version of each trajectory is modelled using a mixture of Von Mises distributions (circular statistics). Second, the remodelled trajectories are globally aligned using tools from bioinformatics. Third, the alignment scores are used to cluster the trajectories using an iterative k-medoids approach and an appropriate distance function. The approach is then evaluated using synthetically generated unmanned aircraft flights combined with real air traffic position reports taken over a sector of Northern Queensland, Australia. Results suggest that the technique is useful in distinguishing between expected unmanned and manned aircraft traffic behaviour, as well as identifying some common conventional air traffic patterns.

Developing an Unmanned Aerial Vehicle (UAV) multi-sensor payload carrier for air quality monitoring

Emissions of gases and particles from sea-faring ships have been shown to impact on the atmospheric chemistry and climate. To efficiently monitor and report these emissions found from a ship’s plume, the concept of using a multi-rotor or UAV to hover inside or near the exhaust of the ship to actively record the data in real time is being developed. However, for the required sensors obtain the data; their sensors must face into the airflow of the ships plume. This report presents an approach to have sensors able to read in the chemicals and particles emitted from the ship without affecting the flight dynamics of the multi-rotor UAV by building a sealed chamber in which a pump can take in the surrounding air (outside the downwash effect of the multi-rotor) where the sensors are placed and can analyse the gases safely. Results show that the system is small, lightweight and air-sealed and ready for flight test.

Significance of a short-term test drive for prospective plug-in electric vehicle consumers: An exploratory study

The test drive is a well-known step in car buying. In the emerging plug-in electric vehicle (PEV) market, however, the influence of a pre-purchase test drive on a consumer's inclination to purchase is unknown. Policy makers and industry participants both are eager to understand what factors motivate vehicle consumers at the point-of-sale. A number of researchers have used choice models to shed light on consumer perceptions of PEVs, and others have investigated consumer change in disposition toward a PEV over the course of a trial, wherein test driving a PEV may take place over a number of consecutive days, weeks or months. However, there is little written on the impact of a short-term test drive - a typical experience at dealerships or public "ride-and-drive" events. The impact of a typical test drive, often measured in minutes of driving, is not well understood. This paper first presents a synthesis of the literature on the effect of PEV test drives as they relate to consumer disposition toward PEVs. An analysis of data obtained from an Australian case study whereby attitudinal and stated preference data were collected pre- and post- test drive at public "ride-and-drive" event held Brisbane, Queensland in March 2014 using a custom-designed iPad application. Motorists' perceptions and choice preferences around PEVs were captured, revealing the relative importance of their experience behind the wheel. Using the Australian context as a case-study, this paper presents an exploratory study of consumers' stated preferences toward PEVs both before and after a short test drive.