An investigation on multi-vehicle motorcycle crashes using log-linear models

Motorcyclists are the most crash-prone road-user group in many Asian countries including Singapore; however, factors influencing motorcycle crashes are still not well understood. This study examines the effects of various roadway characteristics, traffic control measures and environmental factors on motorcycle crashes at different location types including expressways and intersections. Using techniques of categorical data analysis, this study has developed a set of log-linear models to investigate multi-vehicle motorcycle crashes in Singapore. Motorcycle crash risks in different circumstances have been calculated after controlling for the exposure estimated by the induced exposure technique. Results show that night-time influence increases crash risks of motorcycles particularly during merging and diverging manoeuvres on expressways, and turning manoeuvres at intersections. Riders appear to exercise more care while riding on wet road surfaces particularly during night. Many hazardous interactions at intersections tend to be related to the failure of drivers to notice a motorcycle as well as to judge correctly the speed/distance of an oncoming motorcycle. Road side conflicts due to stopping/waiting vehicles and interactions with opposing traffic on undivided roads have been found to be as detrimental factors on motorcycle safety along arterial, main and local roads away from intersections. Based on the findings of this study, several targeted countermeasures in the form of legislations, rider training, and safety awareness programmes have been recommended.

Building Large Scale Traversability Maps Using Vehicle Experience

Traversability maps are a global spatial representation of the relative difficulty in driving through a local region. These maps support simple optimisation of robot paths and have been very popular in path planning techniques. Despite the popularity of these maps, the methods for generating global traversability maps have been limited to using a-priori information. This paper explores the construction of large scale traversability maps for a vehicle performing a repeated activity in a bounded working environment, such as a repeated delivery task.We evaluate the use of vehicle power consumption, longitudinal slip, lateral slip and vehicle orientation to classify the traversability and incorporate this into a map generated from sparse information.

Embedding human expert cognition and real-time trajectory planning in autonomous UAS

This thesis presents a new approach to compute and optimize feasible three dimensional (3D) flight trajectories using aspects of Human Decision Making (HDM) strategies, for fixed wing Unmanned Aircraft (UA) operating in low altitude environments in the presence of real time planning deadlines. The underlying trajectory generation strategy involves the application of Manoeuvre Automaton (MA) theory to create sets of candidate flight manoeuvres which implicitly incorporate platform dynamic constraints. Feasible trajectories are formed through the concatenation of predefined flight manoeuvres in an optimized manner. During typical UAS operations, multiple objectives may exist, therefore the use of multi-objective optimization can potentially allow for convergence to a solution which better reflects overall mission requirements and HDM preferences. A GUI interface was developed to allow for knowledge capture from a human expert during simulated mission scenarios. The expert decision data captured is converted into value functions and corresponding criteria weightings using UTilite Additive (UTA) theory. The inclusion of preferences elicited from HDM decision data within an Automated Decision System (ADS) allows for the generation of trajectories which more closely represent the candidate HDM’s decision strategies. A novel Computationally Adaptive Trajectory Decision optimization System (CATDS) has been developed and implemented in simulation to dynamically manage, calculate and schedule system execution parameters to ensure that the trajectory solution search can generate a feasible solution, if one exists, within a given length of time. The inclusion of the CATDS potentially increases overall mission efficiency and may allow for the implementation of the system on different UAS platforms with varying onboard computational capabilities. These approaches have been demonstrated in simulation using a fixed wing UAS operating in low altitude environments with obstacles present.

Relating vehicle generated pollutants to urban stormwater quality

Rapid urbanisation and resulting continuous increase in traffic has been recognised as key factors in the contribution of increased pollutant loads to urban stormwater and in turn to receiving waters. Urbanisation primarily increases anthropogenic activities and the percentage of impervious surfaces in urban areas. These processes are collectively responsible for urban stormwater pollution. In this regard, urban traffic and land use related activities have been recognised as the primary pollutant sources. This is primarily due to the generation of a range of key pollutants such as solids, heavy metals and PAHs. Appropriate treatment system design is the most viable approach to mitigate stormwater pollution. However, limited understanding of the pollutant process and transport pathways constrains effective treatment design. This highlights necessity for the detailed understanding of traffic and other land use related pollutants processes and pathways in relation to urban stormwater pollution. This study has created new knowledge in relation to pollutant processes and transport pathways encompassing atmospheric pollutants, atmospheric deposition and build-up on ground surfaces of traffic generated key pollutants. The research study was primarily based on in-depth experimental investigations. This thesis describes the extensive knowledge created relating to the processes of atmospheric pollutant build-up, atmospheric deposition and road surface build-up and establishing their relationships as a chain of processes. The analysis of atmospheric deposition revealed that both traffic and land use related sources contribute total suspended particulate matter (TSP) to the atmosphere. Traffic sources become dominant during weekdays whereas land use related sources become dominant during weekends due to the reduction in traffic sources. The analysis further concluded that atmospheric TSP, polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs) concentrations are highly influenced by total average daily heavy duty traffic, traffic congestion and the fraction of commercial and industrial land uses. A set of mathematical equation were developed to predict TSP, PAHs and HMs concentrations in the atmosphere based on the influential traffic and land use related parameters. Dry deposition samples were collected for different antecedent dry days and wet deposition samples were collected immediately after rainfall events. The dry deposition was found to increase with the antecedent dry days and consisted of relatively coarser particles (greater than 1.4 ìm) when compared to wet deposition. The wet deposition showed a strong affinity to rainfall depth, but was not related to the antecedent dry period. It was also found that smaller size particles (less than 1.4 ìm) travel much longer distances from the source and deposit mainly with the wet deposition. Pollutants in wet deposition are less sensitive to the source characteristics compared to dry deposition. Atmospheric deposition of HMs is not directly influenced by land use but rather by proximity to high emission sources such as highways. Therefore, it is important to consider atmospheric deposition as a key pollutant source to urban stormwater in the vicinity of these types of sources. Build-up was analysed for five different particle size fractions, namely, <1 ìm, 1-75 ìm, 75-150 ìm, 150-300 ìm and >300 ìm for solids, PAHs and HMs. The outcomes of the study indicated that PAHs and HMs in the <75 ìm size fraction are generated mainly by traffic related activities whereas the > 150 ìm size fraction is generated by both traffic and land use related sources. Atmospheric deposition is an important source for HMs build-up on roads, whereas the contribution of PAHs from atmospheric sources is limited. A comprehensive approach was developed to predict traffic and other land use related pollutants in urban stormwater based on traffic and other land use characteristics. This approach primarily included the development of a set of mathematical equations to predict traffic generated pollutants by linking traffic and land use characteristics to stormwater quality through mathematical modelling. The outcomes of this research will contribute to the design of appropriate treatment systems to safeguard urban receiving water quality for future traffic growth scenarios. The „real world. application of knowledge generated was demonstrated through mathematical modelling of solids in urban stormwater, accounting for the variability in traffic and land use characteristics.

Fusing loop detector and probe vehicle data to estimate travel time statistics on signalized urban networks

This article presents a methodology that integrates cumulative plots with probe vehicle data for estimation of travel time statistics (average, quartile) on urban networks. The integration reduces relative deviation among the cumulative plots so that the classical analytical procedure of defining the area between the plots as the total travel time can be applied. For quartile estimation, a slicing technique is proposed. The methodology is validated with real data from Lucerne, Switzerland and it is concluded that the travel time estimates from the proposed methodology are statistically equivalent to the observed values.

Physically sound vehicle-driver model for realistic microscopic simulation

In microscopic traffic simulators, the interaction between vehicles is considered. The dynamics of the system then becomes an emergent property of the interaction between its components. Such interactions include lane-changing, car-following behaviours and intersection management. Although, in some cases, such simulators produce realistic prediction, they do not allow for an important aspect of the dynamics, that is, the driver-vehicle interaction. This paper introduces a physically sound vehicle-driver model for realistic microscopic simulation. By building a nanoscopic traffic simulation model that uses steering angle and throttle position as parameters, the model aims to overcome unrealistic acceleration and deceleration values, as found in various microscopic simulation tools. A physics engine calculates the driving force of the vehicle, and the preliminary results presented here, show that, through a realistic driver-vehicle-environment simulator, it becomes possible to model realistic driver and vehicle behaviours in a traffic simulation.

Developing guidelines for intervention to reduce risk of low-speed vehicle run-overs of young children

In Australia, research suggests that up to one quarter of child pedestrian hospitalisations result from driveway run-over incidents (Pinkney et al., 2006). In Queensland, these numbers equate to an average of four child fatalities and 81 children presenting at hospital emergency departments every year (The Commission for Children, Young People and Child Guardian). National comparison shows that these numbers represent a slightly higher per capita rate (23.5% of all deaths). To address this issue, the current research was undertaken with the aim to develop an educative intervention based on data collected from parents and caregivers of young children. Thus, the current project did not seek to use available intervention or educational material, but to develop a new evidence-based intervention specifically targeting driveway run-overs involving young children. To this end, general behavioural and environmental changes that caregivers had undertaken in order to reduce the risk of injury to any child in their care were investigated. Broadly, the first part of this report sought to: • develop a conceptual model of established domestic safety behaviours, and to investigate whether this model could be successfully applied to the driveway setting; • explore and compare sources of knowledge regarding domestic and driveway child safety; and • examine the theoretical implications of current domestic and driveway related behaviour and knowledge among caregivers. The aim of the second part of this research was to develop and test the efficacy of an intervention based on the findings in the first part of the research project. Specifically, it sought to: • develop an educational driveway intervention that is based on current safety behaviours in the domestic setting and informed by existing knowledge of driveway safety and behaviour change theory; and • evaluate its efficacy in a sample of parents and caregivers.

Reducing actuator switchings for motion control of autonomous underwater vehicles

A priority when designing control strategies for autonomous underwater vehicles is to emphasize their cost of implementation on a real vehicle and at the same time to minimize a prescribed criterion such as time, energy, payload or combination of those. Indeed, the major issue is that due to the vehicles' design and the actuation modes usually under consideration for underwater platforms the number of actuator switchings must be kept to a small value to ensure feasibility and precision. This constraint is typically not verified by optimal trajectories which might not even be piecewise constants. Our goal is to provide a feasible trajectory that minimizes the number of switchings while maintaining some qualities of the desired trajectory, such as optimality with respect to a given criterion. The one-sided Lipschitz constant is used to derive theoretical estimates. The theory is illustrated on two examples, one is a fully actuated underwater vehicle capable of motion in six degrees-of-freedom and one is minimally actuated with control motions constrained to the vertical plane.

Preface to special issue on digital human modelling for vehicle design and manufacturing

The automotive industry has been the focus of digital human modeling (DHM) research and application for many years. In the highly competitive marketplace for personal transportation, the desire to improve the customer’s experience has driven extensive research in both the physical and cognitive interaction between the vehicle and its occupants. Human models provide vehicle designers with tools to view and analyze product interactions before the first prototypes are built, potentially improving the design while reducing cost and development time. The focus of DHM research and applications began with prediction and representation of static postures for purposes of driver workstation layout, including assessments of seat adjustment ranges and exterior vision. Now DHMs are used for seat design and assessment of driver reach and ingress/egress. DHMs and related simulation tools are expanding into the cognitive domain, with computational models of perception and motion, and into the dynamic domain with models of physical responses to ride and vibration. Moreover, DHMs are now widely used to analyze the ergonomics of vehicle assembly tasks. In this case, the analysis aims to determine whether workers can be expected to complete the tasks safely and with good quality. This preface provides a review of the literature to provide context for the nine new papers presented in this special issue.

Negligible heat strain in armored vehicle officers wearing personal body armor

Objectives This study evaluated the heat strain experienced by armored vehicle officers (AVOs) wearing personal body armor (PBA) in a sub-tropical climate. Methods Twelve male AVOs, aged 35-58 years, undertook an eight hour shift while wearing PBA. Heart rate and core temperature were monitored continuously. Urine specific gravity (USG) was measured before and after, and with any urination during the shift. Results Heart rate indicated an intermittent and low-intensity nature of the work. USG revealed six AVOs were dehydrated from pre through post shift, and two others became dehydrated. Core temperature averaged 37.4 ± 0.3°C, with maximum's of 37.7 ± 0.2°C. Conclusions Despite increased age, body mass, and poor hydration practices, and Wet-Bulb Globe Temperatures in excess of 30°C; the intermittent nature and low intensity of the work prevented excessive heat strain from developing.

Visual sea-floor mapping from low overlap imagery using bi-objective bundle adjustment and constrained motion

In most visual mapping applications suited to Autonomous Underwater Vehicles (AUVs), stereo visual odometry (VO) is rarely utilised as a pose estimator as imagery is typically of very low framerate due to energy conservation and data storage requirements. This adversely affects the robustness of a vision-based pose estimator and its ability to generate a smooth trajectory. This paper presents a novel VO pipeline for low-overlap imagery from an AUV that utilises constrained motion and integrates magnetometer data in a bi-objective bundle adjustment stage to achieve low-drift pose estimates over large trajectories. We analyse the performance of a standard stereo VO algorithm and compare the results to the modified vo algorithm. Results are demonstrated in a virtual environment in addition to low-overlap imagery gathered from an AUV. The modified VO algorithm shows significantly improved pose accuracy and performance over trajectories of more than 300m. In addition, dense 3D meshes generated from the visual odometry pipeline are presented as a qualitative output of the solution.

Optimal planning of electric-vehicle charging stations in distribution systems

With the progressive exhaustion of fossil energy and the enhanced awareness of environmental protection, more attention is being paid to electric vehicles (EVs). Inappropriate siting and sizing of EV charging stations could have negative effects on the development of EVs, the layout of the city traffic network, and the convenience of EVs' drivers, and lead to an increase in network losses and a degradation in voltage profiles at some nodes. Given this background, the optimal sites of EV charging stations are first identified by a two-step screening method with environmental factors and service radius of EV charging stations considered. Then, a mathematical model for the optimal sizing of EV charging stations is developed with the minimization of total cost associated with EV charging stations to be planned as the objective function and solved by a modified primal-dual interior point algorithm (MPDIPA). Finally, simulation results of the IEEE 123-node test feeder have demonstrated that the developed model and method cannot only attain the reasonable planning scheme of EV charging stations, but also reduce the network loss and improve the voltage profile.