Queue protection parameters’ fine-tuning for variable speed limits

Any incident on motorways potentially can be followed by secondary crashes. Rear-end crashes also could happen as a result of queue formation downstream of high speed platoons. To decrease the occurrence of secondary crashes and rear-end crashes, Variable Speed Limits (VSL) can be applied to protect queue formed downstream. This paper focuses on fine tuning the Queue Protection algorithm of VSL. Three performance indicators: activation time, deactivation time and number of false alarms are selected to optimise the Queue Protection algorithm. A calibrated microscopic traffic simulation model of Pacific Motorway in Brisbane is used for the optimisation. Performance of VSL during an incident and heavy congestion and the benefit of VSL will be presented in the paper.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Fast rates for estimation error and oracle inequalities for model section

We consider complexity penalization methods for model selection. These methods aim to choose a model to optimally trade off estimation and approximation errors by minimizing the sum of an empirical risk term and a complexity penalty. It is well known that if we use a bound on the maximal deviation between empirical and true risks as a complexity penalty, then the risk of our choice is no more than the approximation error plus twice the complexity penalty. There are many cases, however, where complexity penalties like this give loose upper bounds on the estimation error. In particular, if we choose a function from a suitably simple convex function class with a strictly convex loss function, then the estimation error (the difference between the risk of the empirical risk minimizer and the minimal risk in the class) approaches zero at a faster rate than the maximal deviation between empirical and true risks. In this paper, we address the question of whether it is possible to design a complexity penalized model selection method for these situations. We show that, provided the sequence of models is ordered by inclusion, in these cases we can use tight upper bounds on estimation error as a complexity penalty. Surprisingly, this is the case even in situations when the difference between the empirical risk and true risk (and indeed the error of any estimate of the approximation error) decreases much more slowly than the complexity penalty. We give an oracle inequality showing that the resulting model selection method chooses a function with risk no more than the approximation error plus a constant times the complexity penalty.

PTZ camera pose estimation by tracking a 3D target

We present a technique for estimating the 6DOF pose of a PTZ camera by tracking a single moving target in the image with known 3D position. This is useful in situations where it is not practical to measure the camera pose directly. Our application domain is estimating the pose of a PTZ camerso so that it can be used for automated GPS-based tracking and filming of UAV flight trials. We present results which show the technique is able to localize a PTZ after a short vision-tracked flight, and that the estimated pose is sufficiently accurate for the PTZ to then actively track a UAV based on GPS position data.

Predicting wave glider speed from environmental measurements

In the ocean science community, researchers have begun employing novel sensor platforms as integral pieces in oceanographic data collection, which have significantly advanced the study and prediction of complex and dynamic ocean phenomena. These innovative tools are able to provide scientists with data at unprecedented spatiotemporal resolutions. This paper focuses on the newly developed Wave Glider platform from Liquid Robotics. This vehicle produces forward motion by harvesting abundant natural energy from ocean waves, and provides a persistent ocean presence for detailed ocean observation. This study is targeted at determining a kinematic model for offline planning that provides an accurate estimation of the vehicle speed for a desired heading and set of environmental parameters. Given the significant wave height, ocean surface and subsurface currents, wind speed and direction, we present the formulation of a system identification to provide the vehicle’s speed over a range of possible directions.

Unbiased estimation of the half-life to price index convergence among U.S. cities

Estimates of the half-life to convergence of prices across a panel of cities are subject to bias from three potential sources: inappropriate cross-sectional aggregation of heterogeneous coefficients, presence of lagged dependent variables in a model with individual fixed effects, and time aggregation of commodity prices. This paper finds no evidence of heterogeneity bias in annual CPI data for 17 U.S. cities from 1918 to 2006, but correcting for the “Nickell bias” and time aggregation bias produces a half-life of 7.5 years, shorter than estimates from previous studies.

Dynamics of attacker–defender dyads in Association Football : parameters influencing decision-making

Previous work on pattern-forming dynamics of team sports has investigated sub-phases of basketball and rugby union by focussing on one-versus-one (1v1) attacker-defender dyads. This body of work has identified the role of candidate control parameters, interpersonal distance and relative velocity, in predicting the outcomes of team player interactions. These two control parameters have been described as functioning in a nested relationship where relative velocity between players comes to the fore within a critical range of interpersonal distance. The critical influence of constraints on the intentionality of player behaviour has also been identified through the study of 1v1 attacker-defender dyads. This thesis draws from previous work adopting an ecological dynamics approach, which encompasses both Dynamical Systems Theory and Ecological Psychology concepts, to describe attacker-defender interactions in 1v1 dyads in association football. Twelve male youth association football players (average age 15.3 ± 0.5 yrs) performed as both attackers and defenders in 1v1 dyads in three field positions in an experimental manipulation of the proximity to goal and the role of players. Player and ball motion was tracked using TACTO 8.0 software (Fernandes & Caixinha, 2003) to produce two-dimensional (2D) trajectories of players and the ball on the ground. Significant differences were found for player-to-ball interactions depending on proximity to goal manipulations, indicating how key reference points in the environment such as the location of the goal may act as a constraint that shapes decision-making behaviour. Results also revealed that interpersonal distance and relative velocity alone were insufficient for accurately predicting the outcome of a dyad in association football. Instead, combined values of interpersonal distance, ball-to-defender distance, attacker-to-ball distance, attacker-to-ball relative velocity and relative angles were found to indicate the state of dyad outcomes.

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

Arterial travel time estimation : revisiting the classical procedure

Travel time is an important network performance measure and it quantifies congestion in a manner easily understood by all transport users. In urban networks, travel time estimation is challenging due to number of reasons such as, fluctuations in traffic flow due to traffic signals, significant flow to/from mid link sinks/sources, etc. The classical analytical procedure utilizes cumulative plots at upstream and downstream locations for estimating travel time between the two locations. In this paper, we discuss about the issues and challenges with classical analytical procedure such as its vulnerability to non conservation of flow between the two locations. The complexity with respect to exit movement specific travel time is discussed. Recently, we have developed a methodology utilising classical procedure to estimate average travel time and its statistic on urban links (Bhaskar, Chung et al. 2010). Where, detector, signal and probe vehicle data is fused. In this paper we extend the methodology for route travel time estimation and test its performance using simulation. The originality is defining cumulative plots for each exit turning movement utilising historical database which is self updated after each estimation. The performance is also compared with a method solely based on probe (Probe-only). The performance of the proposed methodology has been found insensitive to different route flow, with average accuracy of more than 94% given a probe per estimation interval which is more than 5% increment in accuracy with respect to Probe-only method.

A multi-resolution image alignment technique based on direct methods for pose estimation of aerial vehicles

In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi- Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles’ state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle’s state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle’s state for more than one minute, at real-time frame rates based, only on visual information.

LMP estimation considering the uncertainty of wind power

One of the impediments to large-scale use of wind generation within power system is its variable and uncertain real-time availability. Due to the low marginal cost of wind power, its output will change the merit order of power markets and influence the Locational Marginal Price (LMP). For the large scale of wind power, LMP calculation can't ignore the essential variable and uncertain nature of wind power. This paper proposes an algorithm to estimate LMP. The estimation result of conventional Monte Carlo simulation is taken as benchmark to examine accuracy. Case study is conducted on a simplified SE Australian power system, and the simulation results show the feasibility of proposed method.

Influence of physical and chemical parameters on the treatment of heavy metals in polluted stormwater using zeolite : A review

Zeolite-based technology can provide a cost effective solution for stormwater treatment for the removal of toxic heavy metals under increasing demand of safe water from alternative sources. This paper reviews the currently available knowledge relating to the effect of properties of zeolites such as pore size, surface area and Si:Al ratio and the physico-chemical conditions of the system such as pH, temperature, initial metal concentration and zeolite concentration on heavy metal removal performance. The primary aims are, to consolidate available knowledge and identify knowledge gaps. It was established that an in-depth understanding of operational issues such as, diffusion of metal ions into the zeolite pore structure, pore clogging, zeolite surface coverage by particulates in stormwater as well as the effect of pH on stormwater quality in the presence of zeolites is essential for developing a zeolite-based technology for the treatment of polluted stormwater. The optimum zeolite concentration to treat typical volumes of stormwater and initial heavy metal concentrations in stormwater should also be considered as operational issues in this regard. Additionally, leaching of aluminium and sodium ions from the zeolite structure to solution were identified as key issues requiring further research in the effort to develop cost effective solutions for the removal of heavy metals from stormwater.